U.S. patent application number 17/698897 was filed with the patent office on 2022-09-29 for precision medicine portal for human diseases.
The applicant listed for this patent is Genome International Corporation. Invention is credited to Periannan Senapathy, Sudar Senapathy.
Application Number | 20220310201 17/698897 |
Document ID | / |
Family ID | 1000006251679 |
Filed Date | 2022-09-29 |
United States Patent
Application |
20220310201 |
Kind Code |
A1 |
Senapathy; Periannan ; et
al. |
September 29, 2022 |
PRECISION MEDICINE PORTAL FOR HUMAN DISEASES
Abstract
A method for genome analysis is provided. The method includes
receiving a nucleotide string comprising a plurality of nucleotides
from at least a portion of one or more individual patients' genome.
The method also includes identifying a plurality of variants in
said nucleotide string, assigning each identified variant a score
based on a location of a variant and a predicted functional
consequence, and determining a strength of a variation responsible
for a trait or phenotypic manifestation of the variants. The method
also includes identifying at least one phenotype, and displaying,
in a graphic unit interface of a client device, said nucleotide
string, the identified variants, and the at least one phenotype, in
one or more genetic elements for one or more individual patients. A
system and a non-transitory, computer-readable medium storing
instructions to perform the above method are also provided.
Inventors: |
Senapathy; Periannan;
(Madison, WI) ; Senapathy; Sudar; (Madison,
WI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Genome International Corporation |
Madison |
WI |
US |
|
|
Family ID: |
1000006251679 |
Appl. No.: |
17/698897 |
Filed: |
March 18, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2021/047027 |
Aug 20, 2021 |
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17698897 |
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63166803 |
Mar 26, 2021 |
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63166829 |
Mar 26, 2021 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 15/00 20180101;
G16H 50/20 20180101; G16B 20/50 20190201; G16H 70/40 20180101; G16B
20/20 20190201; G16H 50/70 20180101 |
International
Class: |
G16B 20/50 20060101
G16B020/50; G16B 20/40 20060101 G16B020/40; G16B 20/20 20060101
G16B020/20; G16H 70/40 20060101 G16H070/40; G16H 15/00 20060101
G16H015/00; G16H 10/60 20060101 G16H010/60; G16H 50/20 20060101
G16H050/20; G16H 50/70 20060101 G16H050/70 |
Claims
1. A computer-implemented method comprising: receiving a nucleotide
string comprising a plurality of nucleotides from at least a
portion of one or more individual patients genome, wherein the
portion of the genome includes at least one genetic element of: a
5'-UTR, a promoter, an enhancer, a silencer, an exon, an intron, a
coding sequence, a splice acceptor, a splice donor, a branch point
site, a 3'-UTR, a Kozak sequence, a poly-A addition site or signal,
or a cryptic version thereof, from a known protein coding gene or a
regulatory, splicing, or functional element of a non-protein coding
RNA gene, and within genes not yet identified in a Dark Matter
genome; identifying a plurality of variants in said nucleotide
string by comparing a sequence of said nucleotide string with at
least one reference genome; identifying mutations in the portion of
the genome across a plurality of genetic elements comprising coding
sequences, regulatory elements, splicing elements, and cryptic
versions thereof; determining a mutation in one or more genetic
elements in a gene, wherein the gene mutation is indicative of a
disease, therapeutic or pharmacogenomic phenotype in the one or
more individual patients; and providing, based on the determined
gene mutation, a recommendation of diagnosis, therapeutic drugs or
drugs to avoid.
2. The computer-implemented method of claim 1, further comprising:
providing pharmacogenomic recommendations, such as use as directed,
use with caution, avoid use, or other relevant recommendations, for
the drugs reported in an electronic health record (EHR) of a
patient, as a drug alert utility.
3. The computer-implemented method of claim 1, further comprising:
enabling voice navigation within an application to display, with
voiceover, details of genes, mutations, genetic elements,
biochemical functions, domains, gene families, disease, drugs,
therapeutics, or harmful side effects, or a clinically relevant
detail such as alleles, mode of inheritance or other genetic and
clinical features, in tables, graphical illustrations and reports,
by implementing clinic centric, genome centric, patient centric,
clinician centric questions and answers through voice; and enabling
a user to pause voice navigation or ask for more details, and
obtain relevant answers.
4. The computer-implemented method of claim 1, further comprising,
enabling various automated summary voice over and visualization
reports, wherein key features, tables, graphical illustrations and
reports are automatically displayed in animation with voice over to
provide a quick summary overview, for a patient, of disease causing
genes, genes that are associated with actionable drugs, or drugs to
avoid, or other genetically or clinically relevant detail.
5. A computer-implemented method, comprising: receiving a
nucleotide string comprising a plurality of nucleotides from at
least a portion of two or more individual patients genome, wherein
the portion of the genome includes at least one of: a 5'-UTR, a
promoter, an enhancer, a silencer, an exon, an intron, a coding
sequence, a non-protein coding RNA, a splice acceptor, a splice
donor, a branch point site, a 3'-UTR, a Kozak sequence, a poly-A
addition site or signal, or a cryptic version thereof, from a known
protein coding gene or a regulatory, splicing, or functional
element of a non-protein coding RNA gene, and within genes not yet
identified in a Dark Matter genome; determining, via disease
cohorts or clinical trial cohorts, one or more groups of the two or
more individual patients with similar phenotypes such as a disease
or drug response including efficacy or side effects of a
therapeutic drug, wherein each group exhibit different levels of
phenotypes; determining mutated genes from individuals of a group,
with the similar phenotypes; and identifying frequently mutated
genes of the mutated genes that are common across the individuals
of a disease cohort or clinical trial cohort that are causal of a
similar phenotype.
6. The computer-implemented method of claim 5, further comprising,
assessing the effects of mutations across a plurality of genetic
elements on gene functions, zygosity, biochemical functions,
pathways, or protein domains in the portion of the genome
comprising pathogenic or strength altering mutations in protein
coding and non-coding RNA genes, in the plurality of groups; and,
determining, based on comparing the plurality of groups, a
plurality of genes, alleles, mutations, domains, or biochemical
functions that are indicative of a drug response phenotype
corresponding to high efficacy or low side effects of the
therapeutic drug.
7. The computer-implemented method of claim 5, wherein determining
the causal genes comprises determining the genes that contain
pathogenic or strength altering mutations in the highest number of
types of genetic elements in the highest number of patients as
diagnostic, therapeutic, or PGx genes.
8. The computer-implemented method of claim 5, wherein identifying
the genes responsible for a particular phenotype comprises
identifying the frequently mutated genes, alleles, biochemical
functions, pathways, or protein domains in the portion of the
genome across a plurality of genetic elements comprising pathogenic
or strength altering mutations in protein coding and non-coding RNA
genes.
9. The computer-implemented method of claim 5, wherein identifying
the frequently mutated genes comprises determining a highest
frequency of mutated genes across different genetic elements as an
indicator of disease, therapeutics or harmful side effects, in a
sub-cohort of the disease cohorts, exhibiting a specific disease,
treated with a drug, or exhibiting a drug response phenotype, from
an electronic health record (EHR).
10. The computer-implemented method of claim 5, further comprising:
depicting statistics of the frequently mutated genes comprising
mutations in different elements across the disease cohorts or
clinical trial cohorts in tabular or graphical illustrations on a
gene structure or sequence view, with details of number or
frequency of patients, genes, mutations, mutated position,
zygosity, alleles, pathogenicity status, or score or strength of
the different elements based on one or more scoring algorithms,
biochemical functions, or domains.
11. The computer-implemented method of claim 5, further comprising:
graphically representing a clinical summary of findings from an
analysis of the disease cohorts or clinical trial cohorts, such as
diagnostic genes responsible for disease causation, therapeutic or
PGx genes and drug recommendations, based on mutations in at least
one of the plurality of genetic elements in coding or non-coding
genes from the patients in the disease cohorts or clinical trial
cohorts, in various blocks.
12. The computer-implemented method of claim 5, further comprising:
graphically depicting a frequency of mutations, patients with
different zygosities within a selected protein coding or non-coding
RNA gene in various genetic elements drawn to scale on a gene
structure or sequence, and displaying a detail of a mutation on
mouse hover and popover.
13. The computer-implemented method of claim 5, further comprising:
determining biochemical pathways, protein domains, or gene families
contributing to a disease causation, therapeutics, or harmful side
effects, by identifying genes that contain pathogenic or strength
altering mutations in a highest number of genetic elements in a
highest number of patients, which contribute to a highest frequency
of affected biochemical pathways, domains, or gene families in the
highest number of patients in the disease cohort or a clinical
trial cohort.
14. The computer-implemented method of claim 5, further comprising:
determining frequencies of various genomic structural variations
(GSVs), such as copy number variants, gene fusions, rearrangement,
or other structural variants in the disease cohort or clinical
trial cohort, to discover a highest frequency of GSVs that are
causal of the disease, or therapeutic or pharmacogenomic
phenotypes; and depicting the frequencies of GSVs in tabular or
graphical illustrations.
15. The computer-implemented method of claim 5, further comprising:
discovering biochemical causes for the drug response phenotypes, by
identifying the genes with highest number of pathogenic or strength
altering mutations across various genetic elements leading to the
affected features such as biochemical functions, domains, or
pathways across individuals in each of the plurality of groups, and
comparing frequently affected features in the plurality of groups
exhibiting the various drug response phenotypes.
16. The computer-implemented method of claim 5, further comprising:
depicting frequently occurring features such as the number of
individuals, frequently mutated genes, zygosity, biochemical
functions, pathways, or domains, in the individuals of the
plurality of groups, in graphical blocks with sub sections for
genes with mutations in genetic elements such as coding, regulatory
and splicing elements, genomic structural variants, or non-coding
RNA genes, in different graphical notations and color codes, and
using scroll bars, blocks and sub blocks.
17. The computer-implemented method of claim 5, further comprising:
graphically depicting the frequently mutated genes in each of the
one or more groups on a single line representing the genome drawn
to scale of a genome length, within the view of a computer screen,
along with a frequency of the frequently occurring features such as
various genetic elements, biochemical pathways, domains, mutations,
patients, disease or drug response genes, using various graphical
notations and toggle buttons.
18. The computer-implemented method of claim 5, further comprising:
providing multiple single lines representing the genome, or toggled
using the toggle buttons, to depict the frequently mutated genes,
associated with different features such as pathogenicity, mutated
genetic elements, mutational consequence, biochemical functions,
domains, pathways, gene families, therapeutic or pharmacogenomic
drug responses, treatment outcomes, or other clinical parameters,
in each group of individuals or a combination of groups of
individuals exhibiting varied levels of disease, side effects or
efficacy for a drug.
19. The computer-implemented method of claim 5, further comprising:
analyzing clinical genomic data from an electronic health record
(EHR), or a combination of multiple EHRs, using one or more patient
data comprising disease, drugs, treatment outcomes, or other
clinical parameters, based on the mutations across a plurality of
various genetic elements, in protein-coding or the non-coding RNA
genes, in a static mode, or non-static realtime mode, to determine
genetic causes of different clinical phenotypes.
20. A computer-implemented method comprising: receiving a
nucleotide string comprising a plurality of nucleotides from at
least a portion of one or more individual patients genome, wherein
the portion of the genome includes at least one of: a 5'-UTR, a
promoter, an enhancer, a silencer, an exon, an intron, a coding
sequence, a splice acceptor, a splice donor, a branch point site, a
3'-UTR, a Kozak sequence, a poly-A addition site or signal, or a
cryptic version thereof, from a known protein coding gene or a
non-protein coding RNA gene, and within genes not yet identified in
a Dark Matter genome; identifying mutations and zygosity in genes
within the portion of the genome across a plurality of genetic
elements comprising coding sequences, regulatory elements, splicing
elements, or cryptic versions thereof, wherein the one or more
individuals correspond to a family; and, determining, based on the
mutations and zygosity, a gene mutation or a gene affected by any
pathogenic mutation, in the portion of the genome, wherein the gene
mutation or the affected gene is indicative of a disease,
therapeutic or pharmacogenomic response in the one or more
individual patients that is inherited from or related to a family
member.
21. The computer-implemented method of claim 20, further
comprising, depicting a feature such as genes, genetic elements,
alleles, domains, biochemical functions, or mutations on a single
line representing the genome of an individual, siblings, parents,
or grandparents, each on a different line, or toggled using toggle
buttons, to enable tracing of inheritance of the feature from the
individual's genome to any member of the family using graphical
notations, color codes, and line navigators.
22. The computer-implemented method of claim 20, further
comprising, determining a probability of any disease or drug
response phenotype that an individual exhibits within a lifetime,
based on an occurrence of features such as genes, genetic elements,
mutations, biochemical functions, domains, alleles, mode of
inheritance, genes from gene panels for diseases or drug responses,
or a combination of these features in one or more individuals of
the family.
23. The computer-implemented method of claim 20, further
comprising: determining causal genes for a rare or undiagnosed
disease in an individual by tracing mutations, zygosity, affected
genes, genetic elements, alleles, mode of inheritance, domains,
biochemical functions, or diseases across members of the family in
various generations; and, conducting a similar analysis in a cohort
of families to detect inherited mutated genes, commonly mutated or
causal genes, the mutations, the zygosity, alleles, affected
domains, or biochemical functions across the cohort of
families.
24. The computer-implemented method of claim 20, further
comprising: determining or depicting a pedigree chart illustrating
an affected or carrier status of the disease, inherited or
un-inherited genes, genetic elements, mutations, alleles,
biochemical functions or domains in various members in a family;
and producing an inheritance tracing report of causal genes,
genetic elements, mutations, and other genetic and clinical
features for the disease, using toggle buttons, lists, dropdowns,
or other graphical notations or navigators.
25. The computer-implemented method of claim 20, further
comprising: reducing a number of potential causal genes or alleles
with pathogenic or strength altering mutations from the one or more
individual patients genome, based on whether the disease or
phenotype is inherited from a single parent; and, enabling
subsequent reduction of mutated genes for every generation or
sibling the disease has been inherited or traced from, based on if
a causal gene for the disease or a drug response phenotype is
present, to determine the causal gene for the phenotype.
26. The computer-implemented method of claim 20, further
comprising: determining an allelic status or zygosity of a gene
based on the occurrence of a pathogenic or strength altering
mutation anywhere within the gene in a genetic element including
coding, regulatory or splicing elements, rather than, based on a
particular mutation occurring at a same chromosomal position in one
or two alleles of the gene.
27. The computer-implemented method of claim 20, further
comprising: determining the genes and mutations in the plurality of
genetic elements that are causal of a phenotype in each of the
family members and representing them in different blocks for
different individuals to compare the genes, mutations, zygosity or
any other genetic or clinical features, with toggle buttons to view
individual or combinations of genetic elements, protein coding
genes, non-coding RNA genes or genomic structural variations in
various blocks, sub-blocks in various graphical notations and color
codes.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of International
Application No. PCT/US2021/047027, filed on Aug. 20, 2021, entitled
"A PRECISION MEDICINE PORTAL FOR HUMAN DISEASES", which claims
priority under Article 8 of the PCT to U.S. Provisional Application
No. 63/166,803, entitled "A UNIFIED PORTAL FOR REGULATORY AND
SPLICING ELEMENTS FOR GENOME ANALYSIS," to Sudar Senapathy, filed
on Mar. 26, 2021, and to U.S. Provisional Application No.
63/166,829, entitled "A PRECISION MEDICINE PORTAL FOR HUMAN
DISEASES," to Periannan Senapathy, filed on Mar. 26, 2021, the
contents of both applications incorporated herein by reference in
their entirety, for all purposes.
[0002] This application is related to International Application No.
PCT/US2021/047025 filed Aug. 20, 2021, which claims the benefit of
U.S. Provisional Patent Application No. 63/166,803, filed Mar. 26,
2021, and U.S. Provisional Patent Application No. 63/166,829, filed
on Mar. 26, 2021, each of which is owned by Applicant and is
incorporated herein by reference and which is not admitted to be
prior art with respect to the present invention by its mention in
the cross-reference section.
BACKGROUND
Field
[0003] The present disclosure relates generally to a platform of
networked computing devices for performing a comprehensive use of
gene regulation in the human genome. More specifically, the present
disclosure provides a platform for use in pharmaceutical and
therapeutic applications, as well as in biological and
anthropological research, for the use of gene mutations and their
phenotypical manifestation in the development of personalized
medicine, new drugs and treatments to cure disease, and for
understanding social and behavioral traits in different human
communities over time.
Related Art
[0004] The traditional expectation in clinical genomics has been
that most disease-causing mutations occur in the protein-coding
regions (exons, which are protein-coding sequences called CDS), as
they directly affect the proteins. The vast majority of
cancer-causing mutations in the TP53 gene, for example, are thought
to be caused by the CDS mutations (missense, nonsense, and
Frameshift), The widely used clinical genome sequence analysis
software focuses on the coding sequence mutations. In addition, the
American College of Medical Genetics (ACMG) guidelines largely
focus on CDS variants and classify them as Pathogenic (P), Likely
Pathogenic (LP), Benign (B), Likely Benign (LB), and Variants of
Unknown Significance (VUS), which are universally followed in the
clinical genomics field. However, a vast majority of the genome
includes non-coding portions, which are largely ignored or
considered secondary information sources, in state of the art
approaches.
SUMMARY
[0005] In a first embodiment, a computer-implemented method for
genomic analysis includes receiving a nucleotide string comprising
a plurality of nucleotides from at least a portion of one or more
individual patients genome, wherein the portion of one or more
individual patient's genome includes at least one of: a 5'-UTR, a
promoter, an enhancer, a silencer, an exon, an intron, a coding
sequence, a non-protein coding RNA, a splice acceptor, a splice
donor, a branch point site, a 3'-UTR, a poly-A addition site or
signal, or a cryptic version thereof, from a known protein coding
gene and a non-protein coding RNA gene, and within the genes not
yet identified in a Dark Matter genome. The computer-implemented
method includes identifying a plurality of variants in said
nucleotide string by comparing a sequence of said nucleotide string
with at least one reference genome, wherein at least one of the
variants is in at least one of the 5'-UTR, the promoter, the
enhancer, the silencer, the exon, the intron, the non-protein
coding RNA, the splice acceptor, the splice donor, the branch point
site, the 3'-UTR, the poly-A addition site or signal, or the
cryptic version thereof. The computer-implemented method also
includes assigning each identified variant a score based on a
location of a variant and a predicted functional consequence. The
computer-implemented method also includes determining a strength of
a variation responsible for a trait or phenotypic manifestation of
the variants based on a similarity score by executing instructions
from an algorithm such as Shapiro-Senapathy algorithm, a MaxEntScan
algorithm, and NNSplice algorithm, stored in a memory. The
computer-implemented method also includes identifying at least one
phenotype such as a disease, a drug response, a therapeutic
indications, and a harmful side effects of a medication or
substance, based on the strength of the variation identified in one
or more patients, and displaying, in a graphic unit interface of a
client device, said nucleotide string, the identified variants, and
the at least one phenotype, in one or more genetic elements for one
or more individual patients.
[0006] In a second embodiment, a system includes a memory storing
instructions, and one or more processors configured to execute the
instructions to cause the system to receive a nucleotide string
comprising a plurality of nucleotides from at least a portion of
one or more individual patients genome, wherein the portion of one
or more individual patients genome includes at least one of: a
5'-UTR, a promoter, an enhancer, a silencer, an exon, an intron, a
coding sequence, a non-protein coding RNA, a splice acceptor, a
splice donor, a branch point site, a 3'-UTR, a poly-A addition site
or signal, or a cryptic version thereof, from a known protein
coding gene and a non-protein coding RNA gene, and within the genes
not yet identified in a Dark Matter genome. The one or more
processors also execute instructions to cause the system to
identify a plurality of variants in said nucleotide string by
comparing a sequence of said nucleotide string with at least one
reference genome, wherein at least one of the variants is in at
least one of the 5'-UTR, the promoter, the enhancer, the silencer,
the exon, the intron, the non-protein coding RNA, the splice
acceptor, the splice donor, the branch point site, the 3'-UTR, the
poly-A addition site or signal, or the cryptic version thereof. The
one or more processors also execute instructions to cause the
system to assign each identified variant a score based on a
location of a variant and a predicted functional consequence, and
to determine a strength of a variation responsible for a trait or
phenotypic manifestation of the variants based on a similarity
score by executing instructions from an algorithm such as
Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice
algorithm, stored in a memory. The one or more processors also
execute instructions to cause the system to identify at least one
phenotype such as a disease, a drug response, a therapeutic
indications, and a harmful side effects of a medication or
substance, based on the strength of the variation identified in one
or more patients, and to display, in a graphic unit interface of a
client device, said nucleotide string, the identified variants, and
the at least one phenotype, in one or more genetic elements for one
or more individual patients.
[0007] In a third embodiment, a non-transitory, computer-readable
medium stores instructions which, when executed by a processor,
cause a computer to execute a method for genome analysis. The
method includes receiving a nucleotide string comprising a
plurality of nucleotides from at least a portion of one or more
individual patients genome, wherein the portion of one or more
individual patients genome includes at least one of: a 5'-UTR, a
promoter, an enhancer, a silencer, an exon, an intron, a coding
sequence, a non-protein coding RNA, a splice acceptor, a splice
donor, a branch point site, a 3'-UTR, a poly-A addition site or
signal, or a cryptic version thereof, from a known protein coding
gene and a non-protein coding RNA gene, and within the genes not
yet identified in a Dark Matter genome. The method includes
identifying a plurality of variants in said nucleotide string by
comparing a sequence of said nucleotide string with at least one
reference genome, wherein at least one of the variants is in at
least one of the 5'-UTR, the promoter, the enhancer, the silencer,
the exon, the intron, the non-protein coding RNA, the splice
acceptor, the splice donor, the branch point site, the 3'-UTR, the
poly-A addition site or signal, or the cryptic version thereof. The
method also includes assigning each identified variant a score
based on a location of a variant and a predicted functional
consequence. The computer-implemented method also includes
determining a strength of a variation responsible for a trait or
phenotypic manifestation of the variants based on a similarity
score by executing instructions from an algorithm such as
Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice
algorithm, stored in a memory. The method also includes identifying
at least one phenotype such as a disease, a drug response,
therapeutic indications, and a harmful side effects of a medication
or substance, based on the strength of the variation identified in
one or more patients, and displaying, in a graphic unit interface
of a client device, said nucleotide string, the identified
variants, and the at least one phenotype, in one or more genetic
elements for one or more individual patients.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 illustrates an architecture of devices and systems
for providing a personalized product service, according to some
embodiments.
[0009] FIG. 2 illustrates the details for devices and systems in
the architecture of FIG. 1, according to some embodiments.
[0010] FIG. 3 is a block diagram illustrating a genome sequence
analysis strategy, according to some embodiments.
[0011] FIG. 4 illustrates copy number variants and associated genes
in a chromosome, according to some embodiments.
[0012] FIG. 5 illustrates structural variants and associated genes
in a chromosome, according to some embodiments.
[0013] FIGS. 6A-6C illustrates block diagrams in a cohort and
family genomics application of a genome sequence analysis engine,
according to some embodiments.
[0014] FIG. 7 illustrates a patient risk assessment chart based on
a genome analysis, according to some embodiments.
[0015] FIG. 8 illustrates a map of non-coding RNA provided by a
genome sequence analysis engine, according to some embodiments.
[0016] FIGS. 9A-9C illustrates a chart of a pathogenome module in a
genome sequence analysis engine, according to some embodiments.
[0017] FIG. 10 illustrates a chart with mutation distributions for
genes having pharmacologic impact, according to some
embodiments.
[0018] FIG. 11 illustrates tracks for copy number variants,
structural variants, and gene fusions in a nucleotide sequence
provided by a genome sequence analysis engine, according to some
embodiments.
[0019] FIG. 12 illustrates an immunotherapy track including tumor
mutation burden and microsatellite instability status, according to
some embodiments.
[0020] FIGS. 13A-13D illustrates a customizing framework configured
to provide a cohort pathogenome frequency plot, a pathogene
frequency plot, and an individual pathogenome plot in a genome
sequence analysis engine, according to some embodiments.
[0021] FIG. 14 illustrates portions of a family pathogenome,
according to some embodiments.
[0022] FIG. 15 illustrates a diagram of a pathogene module in a
genome sequence analysis engine, according to some embodiments.
[0023] FIGS. 16A-16B illustrates the structure of an entire gene in
a compact and expanded view provided by a genome sequence analysis
engine, according to some embodiments.
[0024] FIG. 17 illustrates a visual plot of genome mutations,
according to some embodiments.
[0025] FIG. 18 illustrates a protein signature in a genome sequence
analysis engine, according to some embodiments.
[0026] FIGS. 19A-19C-1 through-3, illustrate a gene fusion mutation
event leading to a chimeric gene, according to some
embodiments.
[0027] FIGS. 20A-20G illustrate mutation visualizations provided by
a genome sequence analysis engine, according to some
embodiments.
[0028] FIG. 21 illustrates a variant density plot in a chromosome
with positive and negative strand genes, according to some
embodiments.
[0029] FIG. 22 illustrates a distribution of mutations in the
introns and exons, according to some embodiments.
[0030] FIG. 23 illustrates a strip including an overview of
diagnosis, therapeutics, pharmacogenomics and Rx check, according
to some embodiments.
[0031] FIG. 24 illustrates steps in a method for studying a disease
or other inherited trait using a gene explorer platform, according
to some embodiments.
[0032] FIG. 25 illustrates steps in a method for developing a
therapeutic procedure using a gene explorer platform, according to
some embodiments.
[0033] FIG. 26 illustrates steps in a method for applying a genome
explorer platform to identify structural variants and phenotypic
traits in a population cohort, according to some embodiments.
[0034] FIG. 27 is a block diagram illustrating an example computer
system with which the client and server of FIGS. 1 and 2 and the
methods of FIGS. 24, 25, and 26 can be implemented.
[0035] In the figures, elements or steps having the same or similar
labels are associated with features or processes having the same or
similar description, unless otherwise stated.
DETAILED DESCRIPTION
[0036] In the following detailed description, numerous specific
details are set forth to provide a full understanding of the
present disclosure. It will be apparent, however, to one ordinarily
skilled in the art, that the embodiments of the present disclosure
may be practiced without some of these specific details. In other
instances, well-known structures and techniques have not been shown
in detail so as not to obscure the disclosure.
[0037] The field of clinical genomics has largely focused on the
coding sequence of genes to predict and diagnose the
disease-causing mutations. However, it is increasingly clear that
the majority of these mutations occur outside of the coding regions
and in the regulatory and splicing regions. Some embodiments of the
present disclosure include identifying the mutations in coding and
non-coding regions and their cryptic versions, in the human genome.
Some embodiments are able to predict disease-causing mutations in
different regulatory and splicing regions and their cryptic
versions in a gene, which may occur across patients exhibiting a
particular disease, combine them to identify the genes and
mutations that cause various disease and drug response phenotypes,
and apply these discoveries to precisely diagnose individual
patients. Some embodiments also include scans and analysis in
uncharted regions of the genome called the dark matter genome to
further identify the mutations within the potential additional
genes and the regulatory regions that cause disease. Some
embodiments enable the identification of the complete pathology of
the genome and the comprehensive diagnosis of potential diseases
and drug response phenotypes in a patient. The present disclosure
thus accurately decodes and prioritizes disease-causing genes and
mutations for cancers and non-cancer disorders, and assists the
healthcare providers in clinical decisions.
[0038] Embodiments as disclosed herein also relate to the dark
matter of the genome, which encompasses 98% of the human genome
that does not code for the proteins. Only 1-2% of the human genome
encodes proteins (exons), and the remaining 98% consists of
non-coding RNA genes, untranslated regions, splice sites, branch
sites, enhancers and silencers, regulatory elements, cryptic sites
of all of the regulatory elements, and introns. It is thought that
these elements are extremely important in the function of the
genome, and mutations in them are involved in causing disease and
drug response phenotypes. In addition, there are many new genes
within the intergenic regions or spaces (i.e., between the known
genes), which are yet to be discovered, and mutations in them also
can lead to diseases. Genome Explorer focusses on these areas of
the genome to unravel their hidden wealth and to enable the
discovery of important genetic information that will advance the
understanding of the disease and drug response, ultimately
benefiting the practice of medicine.
[0039] The platform is automated for analyzing a patient's DNA, and
the genomes of multiple patients from a trio, family, cohort, and
clinical trials. It has unique modules and platforms for analyzing
the whole genome, exomes, gene panels, and individual genes in
various ways with finer and granular visualization of every gene
from every patient in a clinically relevant setting. Genome
Explorer is divided into several major platforms including Patient
Genomics.TM., Family Genomics.TM., Cohort Genomics.TM., Clinical
Trials Genomics.TM., EHR Genomics.TM., and Dark Matter
Genomics.TM.. The platform has expanded further into additional
platforms and modules for individual diseases such as hypertrophic
cardiomyopathy or neuroblastoma, or groups of diseases such as lung
cancer, colon cancer, cardiological disorders, neurological
disorders, and many other diseases including newborn, fetal,
infertility and women's diseases.
[0040] The disclosure includes a wide range and automated
sequencing data analysis platform for oncology and non-neoplastic
inherited disorders, which seamlessly provides a unified treatment
approach specific to the individual based on the variations present
in the individual's genome. The platform identifies many possible
genetic alterations such as Single Nucleotide Variations (SNV),
Insertions and Deletions (InDels), Copy Number Variations (CNV),
Structural Variations (SV), Gene Fusions, Splice acceptor and
Splice donor variations, variations at the promoter and
poly-Adenylated sites, backed by the tissue-specific gene
expression levels. The promoter, poly-A, and intronic mutations are
indicated as per the Shapiro-Senapathy algorithm to have a high
mutation impact.
[0041] In some embodiments, an evidence-driven platform
incorporates standard practice guidelines from the Association for
Molecular Pathology (AMP), the ACMG, and the College of American
Pathology (CAP). The drug-metabolizing phenotypes are defined as
per the Clinical Pharmacogenetics Implementation Consortium (CPIC)
guidelines for the patient's sequence variants. The platform
generates predictions based on the clinical history and patient
presentation available in the electronic healthcare record (EHR)
data to facilitate informed decision making. From the disease
incidence and progression history, and various pathology,
laboratory, imaging, and/or genomic sequencing and reports in the
patient's EHR, the present disclosure comprehensive framework
simplifies and enhances personalized approaches starting from
diagnosis through therapeutics and dosage recommendations.
[0042] Thus, the field has largely ignored the regulatory
mutations, giving them a low priority, and consolidated tools to
address these regions have been mostly lacking. However, recent
findings indicate that the majority of disease-causing variants
occur outside of the coding regions, and it is increasingly
becoming apparent that mutations in regulatory and splicing
elements are responsible for upwards of 60% of many diseases. The
present disclosure fulfills this need and enables the systematic
analysis of the mutations in many of the regulatory and splicing
elements. In addition, the platform aims to discover novel
disease-causing genes and diagnosing additional patients based on
mutations in different regulatory regions throughout the gene, in
addition to the CDS regions.
[0043] Some of the features for rationale, logic, algorithms,
methodologies, and discoveries implemented in some embodiments may
include:
[0044] i) Detecting causal genes for cancers and other disorders:
GE's methodology to identify the genes causal of a disease is to
determine that deleterious mutations occur within multiple
regulatory and coding elements of a gene across many patients. The
expectation or the principle is that, if the defectiveness of a
gene is a primary cause of the disease, then a deleterious mutation
at any of the regulatory or coding elements of that gene should be
able to cause the disease individually and independently. Thus,
mutations in different regulatory elements in the same gene
individually should cause the disease in different patients. For
example, in a particular gene with 20 exons, a mutation at the
donor site of exon 17 in one patient, a mutation at the acceptor
site of exon 6 of another patient, a cryptic mutation at the branch
point site of exon 14 in a 3rd patient, a mutation at the promoter
site in a 4th patient, or a particular amino acid mutation in a
coding sequence in a 5th patient, and so on, can cause the same
disease in each of these patients independently. This approach of
identifying mutations in multiple regulatory elements of the same
gene across multiple patients in a cohort analysis is the basis to
discover genes causing a disease that underlies the patient cohort
by a platform as disclosed herein. The present disclosure thus has
formulated a principle that a combination of different mutations in
different regulatory elements in the same gene in different
patients would lead to a particular disease by making that gene
defective and thus would enable the discovery of that gene in a
cohort analysis. Furthermore, although the manner in which the
mutations at the different regulatory or coding elements lead to
the defect in the protein, they make the same protein defective.
Thus, embodiments in the present disclosure are able to identify
the mechanism of a mutation causing the gene and protein defect and
present it to the clinicians.
[0045] ii) Gene signature as a predictor of a disease: The present
disclosure can bring out the complete pathology of the genome of
patients that are indicative of many potential diseases including
cancers and non-cancer disorders. GE expects that each specific
cancer or any other disease should have a unique gene signature. GE
has the ability to bring out the gene signature specific for
different cancers and other diseases. Even though majorly cell
structure, function, and regulatory genes are mostly affected, sets
of genes that are expressed in specific organs vary. It is expected
that there will be a core cancer gene signature for any cancer and
a sub-signature specific to different tissues and organs. Thus, GE
aims to find these signatures based on its ability to uncover
disease-causing mutations within the CDS and many of the different
regulatory elements. The platform produces specific signatures of
genes that exhibit deleterious mutations from cohort studies of
different cancers and diseases, which could be diagnostic marker
signatures for the different diseases. As a corollary, the presence
of a set of mutated genes or a gene signature in a patient's DNA
would signify or diagnose a particular disease in the patient.
Thus, the present disclosure is able to predict or diagnose a
disease from the genomic data obtained from the blood cells, or any
type of cell/tissue source, or from the cell-free DNA or
circulating tumor DNA from a patient. It would thus be able to
predict any cancer possible in the future in a newborn, or cancer
that is present in any of the tissue or organs in a patient,
unknown to the patient. This ability can also be used as a
diagnostic approach to predict any cancer from the data obtained
from a blood sample in a patient at any stage of the tumor
growth.
[0046] Some embodiments include a combination of certain types of
genes that are often required to cause particular cancer or a
non-cancer disease. For instance, causing cancer requires multiple
genetic and molecular steps: 1) a cell should lose its cell
division control to make it divide uncontrollably, 2) the cell
should be able to detach itself from its current position and float
in the circulatory system, 3) the cell should be able to attach
itself at another location in the body, and 4) the tumor that grows
from this cell should be able to establish its growth environments
including blood circulation, growth of blood vessels, and so on.
Thus, multiple genes have to be mutated for a cell to become
cancerous, metastasize, and establish tumors growing at other
locations in the body. The present disclosure determines, based on
its cohort studies by using its approach of detecting mutations
across multiple genetic elements, that certain necessary types of
genes are required to become defective (due to mutations in gene
expression regulation, splicing, or CDS): 1) a gene that is
required to stop or control the cell division cycle, 2) a gene that
is required in cell structure (e.g., cytoskeleton stability), 3) a
gene that is required for extracellular matrix and/or cell-cell
adhesion, 4) a gene that is required for cell detachment and
motility, 5) a gene that is required for anchoring the cancer cell
in another location, and 6) a gene that is required for
establishing the growth environment, and so on. Some embodiments
determine that a gene signature should contain the combination of
one or more defective genes from each of these types of gene/cell
functions to cause cancer and metastasis. For example, the TP53,
TAF3, or OBSCN gene that is involved in transcriptional regulation,
a PCDH, DNAH, or KIF gene that is involved in cell structure, and
MT1E and NNMT in cell migration.
[0047] iii) Family genomics: The present disclosure performs the
Next-Generation Sequencing (NGS) based genomic family analysis
including father, mother, and child (trio), or siblings,
grandparents, and other relatives such as aunts and uncles for
multiple generations (family), providing a strategy for the
identification of causal variants and genes for inherited diseases
that run in the family, and congenital disorders. The trio analysis
pipeline within the platform accurately helps to identify variants
inherited from the parents causing recessive diseases or dominant
diseases and the drug response phenotypes. It has the potential to
identify disease-causing variants based on the zygosity of many
family members and the mode of inheritance (MOI), which otherwise
is not possible by individual patient analysis. Trio/Family
analysis can be performed using either whole genome, whole exome,
or from the targeted gene panels and analyzing complete
variant/mutation information such as SNVs, indels, real and cryptic
splice sites, branch sites, enhancers and silencers, UTRs,
promoters, transcription initiators, poly-A sites (many within
exons, introns, regulatory elements, and throughout the gene) and
ncRNAs (tRNA, rRNA, miRNA, siRNA, snoRNA, snRNA, and lncRNA)
supported by gene tissue expression data (wherever applicable).
They can also be performed based on structural variants, CNVs, Gene
Fusions, and other chromosomal aberrations and rearrangements. The
family analysis can be a standard routine test in patients with
common diseases, rare genetic conditions, or having atypical
clinical presentations or undiagnosed genetic disorders, where the
cause and mode of inheritance are not ascertained. In addition, it
can aid in determining the carrier gene status of various diseases
or drug response phenotypes. The present disclosure relates to
processes for characterizing and screening for genes that are the
cause for the existence or predisposition to inherited disorders or
denovo (congenital) disorders, and ADRs, in the subject, and also
relates to therapies for prevention, therapeutics, and/or reducing
the severity. The family analysis reports the deleterious genetic
alterations at genomic regions like exons, introns, promoters,
UTRs, poly-A sites, and splice regions among the family members.
The downstream molecular effects of SNPs, small Indels, large
genomic rearrangements like gene fusion, copy number variants, and
structural variants can be easily analyzed and visualized. The
alterations observed at the coding, exonic, promoter, poly-A,
splice site, intronic, and deep intronic sites are compared among
the family members based on the genotype, zygosity, and mode of
inheritance to identify the disease-associated mutations, genes,
domains, and pathways. The distribution of mutated genes, domains,
pathways, and actionable drugs in individual patients, parents, and
the trio or family are provided to determine the mutation lineage.
Mutations in the spectrum of poly-A regions, promoter regions
(e.g., TATA box, CAAT box, GC box, and initiator regions), along
with the real acceptor, real donor, cryptic splice acceptor,
cryptic splice donor, branch regions, and cryptic exons, are
analyzed to provide comprehensive and novel insights of these
regions towards disease pathogenesis. The platform also provides
options to scan many human gene families, protein domains, and gene
super-families, and cell structure/function, and gene-expression
regulation genes, which helps in understanding the mutation
inheritance pattern across genes or multiple genes within a
family.
[0048] iv) Mode of inheritance patterns in the proband: The
zygosity and mode of inheritance of many of the different mutations
in the coding and regulatory elements are assessed throughout the
gene, multiple genes, or many of the genes in the genome. They are
also determined based on if the gene itself is normal or has become
defective due to one or more deleterious mutations in any of these
coding and/or regulatory elements within the gene (cis-acting) or
from outside the gene itself (transacting). The various types of
inheritance are determined based on the mutations in the various
genetic elements as follows.
[0049] v) Denovo: The denovo mode of inheritance includes new
mutations present in an affected child but not in either parent.
The affected child must have unaffected parents and the affected
child must be heterozygous (RV) while the parents must be
homozygous (RR or VV) at variant positions.
[0050] vi) Autosomal dominant: The autosomal dominant mode of
inheritance i.e., an affected child must be heterozygous (RV) and
have at least one affected parent. A person with an autosomal
dominant disorder has a 50 percent chance of having an affected
child with one mutated gene (dominant gene) and a 50 percent chance
of having an unaffected child with two copies of normal genes
(recessive genes). It affects males and females in the same
way.
[0051] vii) Autosomal recessive: The autosomal recessive mode of
inheritance i.e., any affected person must be homozygous to
alternate allele (VV). This DNA change occurs when both copies of
the gene are mutated. A person with an autosomal recessive disorder
inherits two mutated genes, one from each parent. These disorders
are usually passed on by two carriers having a 25 percent chance of
an unaffected child with two normal genes, a 50 percent chance of
having an unaffected child (carrier), and a 25 percent chance of
having an affected child with two recessive genes. It affects males
and females in the same way.
[0052] viii) Compound heterozygous: The compound heterozygous mode
of inheritance e.g., an affected child must be heterozygous (Rr) at
both sites whereas one of the parents must be heterozygous (RV) and
the other parent must be reference homozygous (RR) or vice versa at
the variant site.
[0053] xix) X-linked recessive: The x-linked recessive mode of
inheritance i.e., these mutations are present in genes on the X
chromosome. In affected males, one altered copy of the gene is
sufficient to cause the condition; whereas, in affected females, a
mutation would have to occur in both copies of the gene to cause
the disorder. Because it is unlikely that females will have two
altered copies of this gene, males are affected by X-linked
recessive disorders much more frequently than females. A
characteristic of X-linked inheritance is that an affected father
cannot have an affected son (e.g., no male-to-male
transmission).
[0054] x) X-linked dominant: In the x-linked dominant mode of
inheritance, the affected males are heterozygous or variant allele
homozygous, the affected females must be heterozygous, and the
unaffected must be reference allele homozygous. The girls of the
affected dad must be affected and the boys of the affected dad must
be unaffected; whereas, the mothers of affected males must be
heterozygous and affected. Also, at least one parent of affected
females must be heterozygous and affected.
[0055] xi) X-linked de-novo: The x-linked denovo mode of
inheritance i.e., these are the new mutations present in the
affected child but not in either parent in sex chromosomes. The
selection of variants based on the inheritance patterns where the
affected child must have unaffected parents and the affected female
child must be heterozygous (RV) and the affected male child must be
homozygous to alternate allele (VV) while the parents must be
reference homozygous (RR) at variant positions.
[0056] xii) Y-linked: The variants exhibit a Y-linked mode of
inheritance i.e., these mutations are present on the Y chromosome
of an affected male child which can be passed from an affected
father only. Selection of variants is based on the inheritance
patterns where the affected child must have an affected father.
Since only males have a Y chromosome, Y-linked traits are passed
only from affected father to son and rarely occur in females.
[0057] In some embodiments, a genome analysis engine as disclosed
herein performs a differential gene expression analysis directed to
genome-wide messenger RNA (mRNA) expression data. Accordingly, a
platform as disclosed herein provides higher resolution data and
explicit measurement of transcripts level for studying gene
expression via mRNA. Consequently, analyzing gene expression values
provides deep insight into the interplay between intrinsic cellular
processes and stochastic gene expression to explore biological
conditions. The expression levels for each gene are compared
between the case and control samples of a patient and the genes
that carry certain statistical cutoffs are selected for further
interpretation and validation. This type of analysis has been used
as the basis for understanding the expression level of genes that
drive physiological and disease mechanisms.
[0058] The mutations in the promoter regions of the genome,
including the different promoter boxes and transcription initiation
box are known to affect the expression of a gene's transcripts,
which are known to lead to various diseases and traits. For
example, Manganese superoxide dismutase (MnSOD) is known to prevent
cancer progression, but the expression level of MnSOD can be
reduced due to defects in the promoter region.
[0059] In addition, mutations in the 5' and 3' UTRs also are known
to be causal of various diseases and traits. The promoter, UTR,
poly-A, splicing, and other regulatory mutations in one gene can
affect the expression of not only this gene but also genes that are
regulated by its protein product. Thus, the ability to identify the
mutations in the promoters, UTRs, splicing, poly-A, and regulatory
regions, and to determine their corresponding effects on the levels
of transcripts of a gene or the target genes that are regulated by
a gene, would enable the correlations to disease conditions.
[0060] The advent of high throughput RNA sequencing technology
facilitates the efficient detection of intergenic fusion events and
acts as the most useful resource for the identification of fusion
transcripts. Gene fusions are important in the cancer research
field because of their potential as carcinogenic drivers. The RNA
gene fusion module aims at detecting the intergenic fusion
variants, unsupervised and supervised fusion transcripts, homology
scores, quality metrics, and fusion frame classification.
Identifying the gene fusion by whole genomic or exome sequencing
assays is greatly interrupted by the large intronic segments and
requires extra effort for exome enrichment analysis respectively.
However, detecting the fusion events by RNA sequencing is fast and
accurate in discovering biologically relevant fusions. Also, the
present disclosure uses a dual-mapping strategy that enhances
sensitivity and sharpens the analytical endpoints.
[0061] In some embodiments, a platform as provided herein
investigates the causality of variants in the non-coding (nc) RNA
genes which is gaining interest with the rapid discovery of
correlations between mutations in these regions and diseases like
cancers, and multisystemic disorders. The mutations that disrupt
the cellular functions which are dependent on non-coding RNAs or
the factors required for these RNA functions can be deleterious.
The ncRNA genes like tRNA, rRNA, miRNA, siRNA, snoRNA, snRNA, and
lncRNA are considered for the analysis of the pathogenicity of
these variations, which are of major diagnostic importance. Most of
these ncRNAs are also processed from their original genomic
sequences to shorter forms, often using splicing signals (e.g., in
tRNAs), and thus mutations that cause errors in the processing of
these RNAs will also be deleterious and cause disease or Adverse
Drug Reactions (ADRs). The ncRNA module in the present disclosure
provides the possibility to visualize the processing of these
ncRNAs from the genome, in tabular, graphical, and sequence
illustrations. Approaches are also provided to examine the splicing
patterns of mutant ncRNAs, other processing mechanisms, and/or the
processes by which they may lead to disease. Identifying SNPs and
Indels at the miRNA-related functional regions such as 3'-UTRs,
pre-miRNAs and others collectively called MirSNPs represents a
novel category of functional molecules that targets gene
dysregulation resulting in susceptibility to or onset of human
diseases. The deleterious mutations of mitochondrial transfer RNA
(mt-tRNA) and mitochondrially encoded rRNA (mt-rRNA) genes are the
commonest mitochondrial (mtDNA) mutations to cause many genetic
diseases. Defects in oxidative phosphorylation in mitochondria are
often associated with impairment of processes such as replication,
transcription, or translation of mtDNA, which can be due to
mutations in either of the mtDNA-encoded RNAs (tRNAs and rRNAs).
Diseases caused by mt-tRNA mutations can also affect very specific
tissue types, as in the case of neurosensory non-syndromic hearing
loss, diabetes mellitus, and a diverse range of clinical
phenotypes. Identification of disease-causing alleles at the region
of si-RNA that promotes alteration in the gene expression level is
also important to understand these diseases. In many cases, RNAs
may provide a more readily accessible target for therapy than the
defective or deficient proteins they regulate. Thus, discovering
the disease-causing mutations at the RNA genes has the potential
benefit especially for cancer specialists who focus on circulating
RNA cancer biomarkers and also produce a wealth of new therapeutic
targets, with a growing understanding of RNA biology.
[0062] In some embodiments, a platform as disclosed herein
identifies a branch point sequence (BPS) is a highly conserved
splicing signal essential for spliceosome assembly and lariat
intron formation. These are typically located within 30-50
nucleotides upstream of the 3' splice site and are required for
precise exon joining and are recognized through RNA-RNA and
RNA-protein interactions with components of the spliceosome.
Mutations within the sequences that disrupt these interactions are
responsible for 9-10% of the genetic diseases that are caused by
point mutations. Mutations localized in the branch point sequence
might lead to an exon skipping due to improper binding of the SF1
and U2 snRNP splicing proteins and disruption of the natural
acceptor splicing site. Mutations in the branch point sequence can
also cause intron retention (whole or its fragment) if they create
a new 3' splice site. The platform identifies many possible
mutation events within these highly conserved regions, and/or
associates the findings with publicly available disease and
annotation databases. The platform can also be ideal to discover
novel branch point mutations utilizing its cohort analysis or
family analysis modules. It also enables the identification of
cryptic branch point sites and the mutations within them throughout
the gene and throughout the genome. This approach of detecting the
mutations within the branch points acts as an effective strategy
for the clinicians and researchers in analyzing the splicing
defects associated with the disease. Also, branch point mutations
establish a valuable resource for further investigations into the
genetic encoding of splicing patterns and interpreting the impact
of common and disease-causing human genetic variation on gene
splicing.
[0063] Regulation and selection of splice sites is done by
trans-acting splicing activator and splicing repressor proteins as
well as cis-acting elements within the pre-mRNA itself such as
exonic splicing enhancers (ESEs) and exonic splicing silencers
(ESSs). These sequences are located within both exons and introns
that either enhance or suppress splicing. ESEs and ESSs activate or
repress splicing, respectively, from within exons while intronic
splicing enhancers (ISEs) and silencers (ISSs) function from within
introns. The cis-regulatory elements, e.g., exonic and intronic
splicing enhancers (ESE and ISE, respectively) and exonic and
intronic splicing silencers (ESS and ISS, respectively) are
recognized by specific splicing repressors and activators
(trans-acting elements) that help to properly carry out the
splicing process.
[0064] In embodiments as disclosed herein, splicing enhancers are
sites to which splicing activator proteins bind, increasing the
probability that a nearby site will be used as a splice junction.
These also may occur in the intron (intronic splicing enhancers,
ISE) or exon (exonic splicing enhancers, ESE). An exonic splicing
enhancer (ESE) is a DNA sequence motif consisting of .about.6 bases
within an exon that directs, or enhances, accurate splicing of
pre-mRNA into messenger RNA (mRNA). Most of the activator proteins
that bind to ISEs and ESEs are members of the SR protein family.
Such proteins contain RNA recognition motifs and arginine and
serine-rich (RS) domains. Likewise, splicing silencers are sites to
which splicing repressor proteins bind, reducing the probability
that a nearby site will be used as a splice junction. These can be
located in the intron itself (intronic splicing silencers, ISS) or
in a neighboring exon (exonic splicing silencers, ESS). An exonic
splicing silencer (ESS) is a short region (usually 4-18
nucleotides) of an exon and is a cis-regulatory element. ESSs
inhibit or silence the splicing of the pre-mRNA and contribute to
constitutive and alternative splicing. The majority of splicing
repressors are heterogeneous nuclear ribonucleoproteins (hnRNPs)
such as hnRNPA1 and polypyrimidine tract binding protein (PTB).
[0065] Point mutations in exons that inactivate an exonic splicing
enhancer (ESE) can create an exonic splicing silencer (ESS), which
in turn can lead to alternative events like exon skipping and
eventually a truncated protein resulting in genetic disorders.
Mutations in these regions are of very high significance as these
are implicated in numerous cancers and non-cancer disorders. Also,
the adaptive significance of splicing silencers and enhancers is
further attested by multiple studies showing that there is a strong
selection in human genes against mutations that produce new
silencers or disrupt existing enhancers. Enhancers are cis-acting
sequences that can greatly increase transcription rates from
promoters regions and activate the transcription, while the
silencer sequences that are bound by repressors have the opposite
effect by inhibiting activators and reducing transcription.
Enhancers and silencers are organized as a series of cis-acting
sequences that are bound by trans-acting regulatory proteins
similar to promoter-proximal regions. The group of transcription
factor binding sites (enhancers and silencers) allows cells to
perform logic operations and combine different sources of
information to "decide" whether to express a gene.
[0066] The present disclosure defines untranscribed regions as
sequences upstream of the gene start and downstream of the gene end
up to several 1000 bases. It is known that promoter regions with
multiple binding sites occur for over several 1000 bases upstream
of the transcription initiation site where multiple proteins bind
to regulate the gene. Similarly, gene-regulating sequences occur
for over 1000s of bases downstream of the end of the coding
sequence. The present disclosure can identify novel and known
mutations in these highly conserved enhancer and silencer regions,
untranscribed regions, and also cryptic sites and mutations in
them, and can help in understanding gene regulation, gene
expression, and splicing processes and their defects in disease and
ADRs.
[0067] DNA methylation is a major epigenetic modification that is
strongly involved in the physiological control of genome
expression. Aberrant methylation may lead to the silencing of
important genes, such as tumor suppressor genes, affecting their
related transcriptional pathways, activating proto-oncogenes, and
ultimately leading to cancer. Despite tremendous advances in
treatment for cancer, the ability to detect cancer from tumor cfDNA
fragments in blood using DNA methylation could help overcome the
limitations of organ-specific screening tests and enable the
clinical sensitivity needed for early cancer detection. The
methylation pattern is specific for the different tissues in the
body and a Methylseq database is created containing tissue-specific
methylation patterns with and without cancer. The blood sample from
an individual contains the cfDNA fragments that reflect the tissue
of their origin. cfDNA fragments are sequenced and targeted
methylation-based analysis helps in recognizing the differences in
the methylation patterns in cell-free DNA fragments released by
tumor cells in the blood. The tissue-specific methylation patterns
are used to map the location of abnormally methylated cfDNA in the
body. Detecting gene-specific DNA methylation signature in
different stages of cancer cells identifies prognostic and
predictive markers and improves treatments, thus extending the
survival rate in cancer patients. The present disclosure enables
the identification of methylation patterns in cfDNA and correlation
with the mutations in the regulatory elements of genes. It also
makes it possible to analyze these combinations of methylation
patterns and the deleterious mutations in genes to study the
inter-relationships between the two biological phenomena in
producing the disease phenotypes.
[0068] Circulating tumor cells (CTCs) analysis is an effective tool
in molecular profiling of cancer cells developed as a non-invasive,
cost-effective alternative to tissue biopsy. Higher amounts of
preoperative circulating tumor cells are known to be associated
with disease recurrence and decreased overall survival. However,
circulating tumor nucleic acids (DNA/RNA) in the liquid biopsy are
constituents of circulating cell-free (plasma/serum) which is
highly diluted by nucleic acids, extracellular vesicles, proteins,
and metabolites from cancer and non-cancer cells, thus limiting the
detection of variant allele frequencies. The sequenced and uniquely
tagged cfDNA fragments are mapped against the human reference
genome (GrCh37/hg19) and then the generated BAM is processed by
Pisces variant caller. It identifies the small variants (single
nucleotide variants (SNVs) and insertions and deletions (indels))
while filtering for mismatches due to sequencing or sample
preparation errors and includes a collapsing algorithm to rescue
variants broken up by the read boundaries. The quality of the
variants is re-calibrated with variant QScore and distinguishes the
true variants from cfDNA sequencing data against the background
noise. The reference and variant sequences (e.g., A to C SNV, or AG
to A deletion), the frequency, and a QScore are provided for each
variant indicating the confidence rate that the variant is indeed
present in the sample. Thus, low-frequency variant calling in NGS
data from a liquid biopsy can be performed with higher
confidence.
[0069] Nucleic acids secreted by the tumor cells can serve as
predictive and prognostic biomarkers. CTCs as a source of tumor
DNA/RNA can be molecularly profiled to detect informative genomic
or mRNA expression signatures for cancers and to identify genetic
mutations that predict response to targeted therapies. The present
disclosure enables the detection of deregulated gene expression
resulting in uncontrolled cell proliferation, immune regulation,
and stromal remodeling that defines cancer. Discrete alteration in
circulating RNA reflects dysregulation of cancer immunity, cell
growth, proliferation, and stromal interaction. The mutations
present in the ct or cf DNA, within the coding and many of the
regulatory regions from the genome of a patient or cohort of
patients, are detected, thus enhancing the ability to relate these
mutations with the disease. It combines the mutations from the
different regulatory regions to detect the most probable genes
causal of disease or ADR, and also in combination with the coding
regions. The pathways are identified based on the frequently
mutated genes (FMGs) and significantly mutated genes (SMGs) to
discover the molecular networks such as MAPK/RAS pathway, p53
signaling pathway, and signaling pathways affecting basic cell
structure, cell signaling, cell divisions, and actions. These
analyses provide a broadly applicable approach for non-invasive
detection of early-stage tumors that may be useful for screening
and management of patients with diseases. Early detection and
intervention are likely to be the most effective means for reducing
morbidity and mortality of human diseases. This non-invasive cancer
biomarker detection in the present disclosure will provide a more
effective and patient-friendly method for the detection,
monitoring, and treatment of cancer.
[0070] In some embodiments, a platform as disclosed herein provides
a gene expression signature. Accordingly, a database may host
manually curated gene expression data gathered from multiple public
resources and are annotated with biological conditions and ontology
terms. The platform can find the differentially expressed genes
that are up or downregulated for different cancer types and across
tumor stages. Tissue-specific gene expression data were collected
from non-diseased tissue sites for each gene and treated as a
control dataset. Similarly, disease-specific gene expression data
were collected and treated as a disease cohort. The expression
values (FPKM) are compared between healthy and disease datasets to
find the case-control Gene Expression Ratio (GER). The top 20
up-regulated and down-regulated genes constitute the expression
signature that will be unique for different diseases and across
tumor stages for a given cancer. The platform is also able to
determine the effects of mutations in the promoters, UTRs, poly-A,
splice, and other regulatory regions which affects the gene's
expression and correlates the corresponding effects on the levels
of transcripts of a gene or the target genes and the patterns of
expression (signatures) that are regulated by a gene in various
diseases from RNA sequencing data including mRNA, cfRNA, and ctRNA.
In some embodiments, the mutations and DNA methylation profiles are
integrated with RNA-sequencing data to correlate methylation
variation across tissues with differential gene expression levels.
An RNA sequencing technique is employed for the analysis of the
transcriptome and its quantification for measuring gene expression.
The transcriptional levels are represented at the logarithmic scale
for FPKM and Transcripts Per Million (TPM) for each identified
gene. The majority of differentially expressed genes are then
compared with the significant changes in DNA methylation,
indicating that DNA methylation plays an essential role in
mediating tissue differentiation. The platform employs an in-built
algorithm to investigate the profiles of mutations, gene
expression, and methylation mainly on the promoter, transcription
start sites, and other regulatory and splicing regions whether DNA
mutations, methylation, and gene expression have correlations
across each other in disease causation.
[0071] In some embodiments, a platform as disclosed herein may be
used to predict a likelihood of a disease in a newborn. The
likelihood of a patient getting a disease depends on the particular
gene(s) affected by pathogenic mutations, which can be a single
gene to multiple genes. In addition, the zygosity and MOI of each
mutation and the gene(s) themselves are also determinants of the
causation of disease. The affected or carrier state of each of the
deleteriously mutated genes in an individual can be determined
based on the mutations in one or more genetic elements in one or
more genes in one or more patients, and tabulated and graphically
illustrated. The likelihood for a disease possibility in a patient
can be determined and assigned a score, based on the total number
of affected genes from a gene signature or a panel for a disease.
This score information can then be used to predict one or more
diseases in a patient based on the particular genes deleteriously
mutated in the genome of a new patient.
[0072] In some embodiments, a platform as disclosed herein may be
used for prediction of disease and other drug phenotypes. There are
several methods we have developed to identify the genes indicating
a disease: Doublet or triplet gene signatures, Cell
structure-function based gene signatures, diagnosing disease in
asymptomatic people using weighted scores, and finding scores based
on the number of genes indicating the "Affected" status. We can
combine the scores for these different methods in a single patient
and define a combination method or algorithm to provide a FINAL
SCORE of disease(s). The top scoring diseases should be indicative
of the diseases that the individual may develop in the future, even
a newborn. The validity of this method can be tested in individuals
with multiple diseases from the EHR of a hospital system. The WGS
from the blood cells should indicate the diseases by this method.
This set of diseases can be verified by the actual diseases that
the individuals have from the EHR. Scores can be given for the
accuracy of the findings in each patient, and in a large number of
patients in total. If this cumulative score average is high enough,
like 80 or 90%, then this method can be effectively used to predict
diseases in new patients. This is similar to the CVC method we use
for CDS by combining multiple known methods and taking the average
score to predict the deleterious mutations.
[0073] In some embodiments, a platform as disclosed herein may be
configured to perform a method to form cancer gene signatures by
analyzing a large number of patients using known gene panels for
different cancers: First determine the mutated genes from the gene
panels of a particular disease or cancer (or from different gene
panels from different cancers) in one or more patients and collect
the mutated genes for each patient. This will give a list of genes
and their combinations (gene signatures) either single, double, or
higher numbers, occurring in every patient. The frequency of
patients including each of these combinations are determined and
sorted in descending order. The combinations whose frequency is
higher than a threshold are only considered and these combinations
of genes with high frequency in a large population of patients will
form multiple gene-combination signatures, which can then be used
to predict that disease in a new patient. The combinations whose
standard deviations (SD) are greater than or equal to a high SD can
be valid signatures indicative of a disease.
Example System Architecture
[0074] FIG. 1 illustrates an architecture 100 of devices and
systems for providing a map of genes and mutations thereof for an
individual or a cohort of individuals, according to some
embodiments. A server 130 may be coupled with a database 152
storing a genome sequence log for each of multiple users handling
client devices 110. Servers 130, database 152, and client devices
110 may be communicatively coupled with each other via a network
150.
[0075] Servers 130 may interact and communicate with other devices
in network 150 via any one of multiple interfaces and
communications protocols (e.g., wired, cable, wireless, and the
like). More specifically, servers 130 and client devices 110 may
include an appropriate processor, memory, and communications
capability, configured to interact with network 150 via a digital
interface. Client devices 110 may include, for example, desktop
computers, mobile computers, tablet computers (e.g., including
e-book readers), a digital stand in a retailer store, mobile
devices (e.g., a smartphone or PDA), wearable devices (e.g., smart
watch and the like), or any other devices having appropriate
processor, memory, and communications capabilities for accessing
one or more of servers 130 through network 150. In some
embodiments, client devices 110 may include a Bluetooth radio or
any other radio-frequency (RF) device for wireless access to
network 150. The memory in the client device from the retailer may
include instructions from an application programming interface
(API) hosted by server 130 (e.g., downloaded from, updated by, and
in communication with server 130). The API in client devices 110
may be configured to cause client devices 110 to execute steps
consistent with methods disclosed herein.
[0076] Network 150 can include, for example, any one or more of a
local area network (LAN), a wide area network (WAN), the Internet,
and the like. Further, network 150 can include, but is not limited
to, any one or more of the following network topologies, including
a bus network, a star network, a ring network, a mesh network, a
star-bus network, tree or hierarchical network, and the like.
[0077] FIG. 2 is a block diagram 200 illustrating an example server
130 and client device 110 in architecture 100, according to certain
aspects of the disclosure. Client device 110 and server 130 are
communicatively coupled over network 150 via respective
communications modules 218-1 and 218-2 (hereinafter, collectively
referred to as "communications modules 218"). Communications
modules 218 are configured to interface with network 150 to send
and receive information, such as data, requests, responses, and
commands to other devices on the network. Communications modules
218 can be, for example, modems or Ethernet cards. Client device
110 may be coupled with an input device 214 and with an output
device 216. Input device 214 may include a keyboard, a mouse, a
pointer, or even a touch-screen display that a user may use to
interact with client device 110. Likewise, output device 216 may
include a display and a speaker with which the user may retrieve
results from client device 110. Client device 110 may also include
a processor 212-1, configured to execute instructions stored in a
memory 220-1, and to cause client device 110 to perform at least
some of the steps in methods consistent with the present
disclosure. Memory 220-1 may further include an application 222,
including specific instructions which, when executed by processor
212-1, cause a graphic payload 225 hosted by server 130 to be
displayed for the user in output device 216. Graphic payload 225
may include multiple graphic illustrations of a nucleotide sequence
requested by the user to server 130. The user may store at least
some of the illustrations and partial nucleotide sequences from
graphic payload 225 in memory 220-1.
[0078] In some embodiments, application 222 includes a voice
navigation feature within the user interface, such that a user can
navigate everything about a patient or a cohort without having to
click any button. A set of voice commands that aids in the
navigation of the disclosed platform has been incorporated within
the application. Accordingly, application 222 may include a
clinical genomics intelligence program that detects the appropriate
events for the voice commands provided and enables navigation to
the results swiftly based on built-in data connections. This
functionality enables the user to use voice commands to know the
details of any gene such as gene name, length, number of exons,
number of domains, protein length, and gene function, and further
enabling the user to stop or know more details of the same or
different genes, mutations, disease, drugs, therapeutics, and
harmful side effects. It involves designing, implementing, and
enabling clinic-centric, patient-centric, clinician-centric, and
genome-centric questions and answers through voice.
[0079] In some embodiments, memory 220-1 may include an application
222, configured to display and process the contents in graphic
payload 225. Application 222 may be installed in memory 220-1 by
server 130, together with the installation of an operating system
that controls many hardware operations of client device 110.
[0080] Server 130 includes a memory 220-2, a processor 212-2, and
communications module 218-2. Processor 212-2 is configured to
execute instructions, such as instructions physically coded into
processor 212-2, instructions received from software in memory
220-2, or a combination of both. Hereinafter, processors 212-1 and
212-2 will be collectively referred to as "processors 212," and
memories 220-1 and 220-2 will be collectively referred to as
"memories 220." Memory 220-2 includes a genome sequence analysis
engine 242. In some embodiments, genome sequence analysis engine
242 includes a sequence scoring tool 244, a mutation tool 246, a
statistics tool 248, and an algorithm 250 to manipulate genome
sequence data and create charts and reports for graphic payload
225. Sequence scoring tool 244 parses at least a portion of a
nucleotide sequence from a genome sequence to identify a splicing
site therein. Accordingly, applications 222 may be installed by
server 130 and perform scripts and other routines provided by
server 130 to display graphic payload 225 provided by genome
sequence analysis engine 242. Mutation tool 246 may be configured
to identify protein domains affected by mutations in the nucleotide
sequence that may alter the splicing sites (according to sequence
scoring tool 244).
[0081] In some embodiments, mutation tool 246 may access a mutation
log in database 252, to identify a recurring mutation over a cohort
or a population of individuals. Statistics tool 248 may perform a
frequency analysis over the splice sites and the mutations
identified by sequence scoring tool 244 and mutation tool 246. In
some embodiments, statistics tool 248 may use mutation logs and
gene sequencing logs in database 252 to evaluate statistical data
on a nucleotide sequence for an individual or a cohort of
individuals, for analysis. Algorithm 250 may be a linear or
non-linear algorithm, including a neural network, machine learning,
or artificial intelligence algorithm used to identify and score
splicing sites (e.g., for sequence scoring tool 244). For example,
in some embodiments, algorithm 250 may include the
Shapiro-Senapathy algorithm to score a nucleotide sequence as a
splice site (e.g., a `donor` site or an `acceptor` site).
[0082] In some embodiments, database 252 includes a Regulatory
Elements Disease Database (REDDB). REDDB is a database of the genes
and signatures that it has identified based on the frequency of
patients with mutations in multiple different regulatory elements
and their cryptic versions in a cohort of patients, separately for
the genes within the white matter genome (currently known genes in
the human genome) and the genes within the dark matter genome. It
also includes the genes based on the coding sequences. Based on
cohort studies, the platform has discovered: 1) a set of genes that
are already known to cause a particular cancer (e.g., prostate
cancer), 2) a set of genes that can be defined as novel cancer
genes, and 3) a set of genes that are known to cause one or more
non-cancer diseases (e.g., Parkinson's disease or Fructose
intolerance). The latter set of genes can be confidently defined as
novel cancer genes or genes that are known to cause particular
non-cancer disorders, as they occur in a high frequency in the
cohort of cancer patients. The database includes them appropriately
as either potential novel cancer genes, or genes causal of another
particular disease that occurs in association with particular
cancer. The platform applies the information that has accumulated
in the REDDB to predict the genes that cause disease in a given
patient. Thus, it predicts and reports these genes and the details
of mutations in the clinical report of a patient with a particular
disease, or a newborn, or any individual without any apparent
disease. It can also predict genes responsible for different stages
of cancer in patients who do not yet have the knowledge of the
disease. The REDDB enables a robust backend processing, and a
user-friendly front end with various capabilities for searching and
analyzing the information of the genes, many of the genetic and
regulatory elements, mutations, patients, diseases, and other
relevant data and information. It is also linked with the modules
of individual Patient Genomics, Family Genomics, Cohort Genomics,
and Clinical Trials Genomics in the present disclosure, such that
the data can be utilized in the different modules as required. From
the fact that there are several other genes with these mutations
that occur in a high number of patients, the platform determines
that these genes have a high probability of being causal of the
particular disease, or another disease that occurs in association
with the primary disease. Thus, genome analysis engine 255 assigns
a high score of being causal of cancer or a secondary disease and
integrates them within the REDDB. Furthermore, some genes are known
to cause one or more non-cancer diseases with high frequency in the
cohort of cancer patients. REDDB includes such genes as either
potential cancer genes that are novel, or genes causal of a
particular disease that occurs in association with particular
cancer.
[0083] Server 130 may further include a genome analysis engine 255.
Genome analysis engine 255 may include multiple modules, such as
gene discovery module 260-1, an EHR module 260-2, a launch module
260-3, a report module 260-4, a pharmacogenomics module 260-5, a
cohort genomic module 260-6, a clinical trial module 260-7, a tumor
mutation burden and microsatellite instability (TMB/MSI) module
260-8, a rearrangement module 260-9, a pathogenome module 260-10, a
pathogene module 260-11, and a dark matter module 260-12
(hereinafter, collectively referred to as "modules 260").
[0084] Gene discovery module 260-1 provides gene discovery
including the analysis of deleterious genetic alterations that are
commonly seen among the individuals of the cohort and interpreted
with the functional understanding of those mutations affecting the
protein structure and function, gene families, protein domains,
biological pathways, and drug response. In this perspective, the
platform presents the opportunities for analyzing large-scale
cohort studies to investigate the downstream molecular effects of
SNVs and Indels in different regions including the coding regions,
exons, introns, promoters, poly-A sites, and the various regulatory
genetic elements within the genes and the genome. It also enables
the analysis of large genomic rearrangements including gene fusion,
copy number variants, and structural variants from the cohort of
patients, and makes informed conclusions concerning their disease
relationship.
[0085] Gene discovery module 260-1 may perform comparative genomic
analysis to provide top recurring genetic mutations affecting
protein domains, pathways, and potentially actionable drugs. Also,
the alterations observed at the promoter, poly-A, splice site,
exonic, intronic, and deep Intronic cryptic sites and cryptic exons
in genes are compared among the individuals in a cohort that helps
to identify the disease-associated domains and to group the
affected proteins into a pathway, which can provide an insight into
how this information is connected to the disease at molecular and
clinical levels.
[0086] Gene discovery module 260-1 also identifies real and cryptic
regulatory elements including promoter, poly-A, splice sites,
branch points, enhancers and silencers, and UTR, exonic, intronic,
and deep intronic mutations. Accordingly, gene discovery module
260-1 identifies the causality of variants in regulatory regions
like promoters (TATA box, CAAT box, GC box, and initiator),
untranslated regions on the 5' and 3' region of the coding sequence
(UTRs), poly-A, splice site, exonic, intronic, and deep intronic
cryptic splice, branch points and cryptic exon mutations in the
cohort individuals. Gene discovery module 260-1 also reports the
mutations observed in different samples derived from each
individual of the cohort. The regulatory site mutations are scored
using the Shapiro-Senapathy algorithm and its adaptation to fit the
length, sequence, and position weight matrices (PWMs) of the
different elements, and other relevant algorithms to identify the
potential real and cryptic splice sites, promoter and poly-A boxes,
branch sites, and other genetic elements. The scores are calculated
for the genetic variants identified at the real and cryptic sites
in the individuals of the study and compared with the original
scores to detect the alterations in the potential regulatory sites.
The mutations in these regulatory regions are known to lead to
pathological effects at the level of RNA splicing and gene
expression, which can explain the clinical phenotypes observed in
human genetic disease.
[0087] Gene discovery module 260-1 identifies genes with mutations
across multiple genetic elements of exons, splice sites, branch
points, enhancers and silencers, promoters, and UTRs. In some
embodiments, the regulatory elements that are mutated and falling
under the various combinations of the coding region, splice sites,
promoter regions, initiators, and poly-A sites and other regulatory
elements within a gene across the cohort population are discovered
and illustrated. Although aberrations in these elements are found
to be a major cause of human diseases, there is only a very limited
understanding of how regulatory element genotypes lead to specific
phenotypes. This section thus may be integrated as a single
processing component for interpreting the genetic alterations at
the coding and regulatory regions of genes occurring in various
individuals from the study to provide a better understanding of the
functional roles of how these elements can be associated with
specific phenotypes/genotypes in a population. It also provides the
unique ability to understand and discover genes that have higher
probability of being the causative agents of the disease or trait
across the cohort population by virtue of occurring in multiple
genetic elements of particular genes, rather than by relying or
focusing on the coding sequence as the main source or cause of the
disease or other phenotypes in the cohort.
[0088] The various "sites" for different regulatory elements (e.g.,
splice sites, branch sites, enhancers and silencers, promoters,
transcription initiators, poly-A sites), and their cryptic sites
are identified throughout the exons, introns, UTRs, and other
elements, throughout the gene, and throughout multiple genes or
many genes in a genome. The present disclosure works on the concept
that the probability of identifying the gene causative of disease,
trait, condition, or drug response from a cohort of patients
increases when the results from these mutations are combined from
multiple genetic and regulatory elements, and thus enables this
approach for multiple elements from a patient or cohort of
patients, and multiple genes or many genes in a genome.
[0089] Gene discovery module 260-1 identifies splicing mutations
and aberrations. In some embodiments, gene discovery module 260-1
identifies splice sites (real acceptors and donors, cryptic
acceptors and donors, real and cryptic branch points, real and
cryptic splice enhancers and silencers, and cryptic exons)
mutations classified based on pathogenicity scores. As illustrated,
the splice sites are scored using the Shapiro-Senapathy algorithm
to identify the potential splice sites. The genetic alterations
identified in the cohort study at the splice sites are calculated
for scores and compared with the original scores. The pathogenicity
is thus assigned for each variant based on differences between
original and observed scores. This section provides genes with
different types of splice site variants classified as "Pathogenic,"
"Likely Pathogenic," and "Unknown" (based on the score difference
of the mutated site compared to the original site) from the study
along with the statistics of reported samples and patients. In a
similar manner, the cryptic sites for many of the regulatory
elements including the promoters (promoter boxes), transcription
initiators, enhancers and silencers, splice donor, acceptor and
branch sites, and poly-A sites, throughout the genes, intergenic
regions, and the genome are identified and the mutations within
them are determined.
[0090] The following are the additional statistical data for the
genes handled by gene discovery module 260-1: A total number of
splice mutations is a unique count of splice mutations reported in
the study, a total number of unique genes is a unique count of
genes with splice mutations reported in the study, a real acceptor
is a unique count of real acceptor mutations reported in the study,
a real donor is a unique count of real donor mutations reported in
the study, a cryptic acceptor is a unique count of cryptic acceptor
mutations reported in the study, a cryptic donor is a unique count
of cryptic donor mutations reported in the study, a real and
cryptic branch site count indicates the number of real and cryptic
branch sites in the genes.
[0091] Gene discovery module 260-1 also identifies promoter
mutations. In some embodiments, gene discovery module 260-1
identifies promoter mutations (TATA, CAAT, CG, and Initiator boxes)
classified based on the pathogenicity scores calculated.
Pathogenicity scores may be calculated for the promoter mutations
by employing the Shapiro-Senapathy algorithm adapted from the
algorithm for splice sites. This section also provides statistical
information for the genes in different pathogenicity statuses
(e.g., Pathogenic, Likely Pathogenic, and Unknown). The information
provided in this section aids the researcher to understand and
explain the regulatory functions of these mutations in a diseased
condition. The following are the additional statistical information
collected and processed by gene discovery module 260-1:
[0092] A total number of promoter mutations is the count of
promoter mutations present in the cohort study, a total number of
unique genes is the number of unique genes which consists of
promoter mutations, a TATA box is a measure of the TATA box
mutations present in the cohort study, a GC box indicates a count
of GC box mutations present in the cohort study, a CAAT box
indicates the number of CAAT box mutations present in the cohort
study, an initiator box is the count of initiator box mutations
present in the cohort study, a Poly-A mutation that leads to
alterations in the mRNA 3'-end processing mechanisms. These changes
are implicated as a common characteristic among many endocrine,
hematological, oncological, immunological, and neurological
diseases. Gene discovery module 260-1 also provides further
evidence of these mutations and their impact on several
physiological conditions. The following are the additional
statistical information provided by gene discovery module 260-1: A
total number of poly-A mutations: Count of poly-A mutations present
in the cohort, a total number of unique genes: Unique count of the
genes which contain poly-A mutations. Additionally, gene discovery
module 260-1 identifies the top mutated genes that belong to a gene
family, which are based on common structures and functions of genes
and proteins, and aids in understanding gene groups and families
that may be involved in disease or traits in a cohort.
[0093] Gene discovery module 260-1 is configured to discover top
genes and domains, and summarizes and helps to visualize the most
frequently mutated genes across the cohort, based on the affected
protein domains present in the selected study cohort. Gene
discovery module 260-1 also summarizes the top genes containing
particular domains or group(s) of domains based on the basic
structure and/or function of the domain. Gene discovery module
260-1 has enabled the discovery that genes that cause various
cancers fall under three major groups, genes that build the basic
cell structure, genes that enable the basic functions of the cell,
and the genes that are involved in transcriptional regulation (gene
expression) in the cell. For example, protein domains such as
cadherins, dyneins, collagens, and kinesins are involved in
maintaining the structure of the cell, providing the skeleton and
stability for the cell. They are also involved in cell adhesion,
extracellular matrix, and cell motility. Mutations in the genes
containing these domains will affect the basic structure and
function of the cell, or gene regulation, leading to cancers and
non-cancer diseases. Identifying these genes that are frequently
mutated in a cohort of individuals with a particular disease or
trait will uncover the type of genes potentially responsible for
these phenotypes.
[0094] Gene discovery module 260-1 enables the identification of
the top genes based on the occurrence of mutations in these genes
in multiple genetic elements including the CDS and the various
genetic regulatory elements and their cryptic forms, and the
grouping of these genes into few major categories of cell
structure, function, and gene regulation. In some embodiments, gene
discovery module 260-1 identifies top genes (based on cell
structure, function, and gene regulation). Genome analysis engine
255 views cancers as the defect of the cell structure and function
and not as a defect of an organ, and that, cancer is a disease of
the cell, not the disease of the organ. It enables the discovery
that the mutations in the coding and the several regulatory
sequences of genes from particular gene families that affect the
basic cell structure such as the cytoskeleton, cell-cell adhesion,
and extracellular matrix, and cell functions such as signal
transduction and transcriptional regulation, are actually the basic
cause of cancer, and potentially non-cancer diseases. This
discovery potentiates the development of therapeutic drugs by
targeting these cell structure, function, and transcriptional
regulating genes, proteins, and their domains. Thus, this cohort
platform enables the grouping of different genes belonging to a
family or one of the few groups unearthed in the Gene Discovery
module, such as the protocadherins or dyneins, or transcription
regulating genes, a group of genes rather than single genes, and
using the combination of these genes as the most frequently mutated
genes in a cohort in one approach of diagnosis, treatment, or drug
development.
[0095] Gene discovery module 260-1 also identifies gene signatures
for different cancers and other diseases caused by the
participation of multiple genes that have become defective due to
mutations and are called polygenic or multi-gene disorders. The
cohort platform enables the discovery of the multiple genes that
are necessary and/or sufficient for causing disease, as it enables
the identification of more numbers of relevant genes and gene
groups based on the combination of mutations across multiple
genetic and regulatory elements. This makes it easy to form gene
signatures with deleterious mutations present in an individual and
across multiple patients, especially based on genes that belong to
a gene group such as cell structure genes (e.g., cadherins,
dyneins, and kinesins), cell function genes such as kinases and
phosphates, and gene regulation genes such as TP53, TAF, and FAT.
The platform provides several tabular details of genes based on
cell structure, function, gene regulation, domains, gene families,
and gene signatures across patients, for the user to carry out
various analyses that enable the determination of the
disease-causing genes, gene groups, and gene signatures.
[0096] Gene discovery module 260-1 may include analytical
capabilities such as tabular details of genes, including the number
of patients, samples, and mutations for every genetic element such
as real splice donor, real splice acceptor, cryptic donor, cryptic
acceptor, each of the promoter boxes, branch site, and the other
genetic regulatory elements, and their cryptic versions, are
provided. Users can analyze these genes and the frequencies of
patients and other parameters associated with each gene by
scrolling and page views, and by searching and sorting, for many of
the genes with mutations in every genetic element. In addition,
genes with mutations in CDS based on frequently mutated genes (FMG)
and significantly mutated genes (SMG) are also provided in separate
tables. The list of affected domains, pathways, and drugs with
responses are provided in separate tables for every genetic
element. The user can easily compare the details across multiple
tables for different genetic elements, thus enabling analysis,
conclusion, and discovery. Genes with frequent mutations in
multiple genetic elements in the same gene across the cohort of
patients are also provided with several tabular details, and their
graphical illustrations to identify the most clinically relevant
genes causal of a disease.
[0097] Various toggle buttons are provided to choose multiple
levels of different parameters. For example, the user can choose
the different score thresholds for cryptic sites (e.g., splice,
branch, promoter) from a score ranging from 50 to 100 for
displaying the genes with mutations in the different cryptic sites
of splice sites, branch sites, promoter boxes, and other elements
throughout the gene. The user can also choose Pathogenic, Likely
Pathogenic, and/or Unknown mutations to display each type of
regulatory element mutations. The module also enables filter
options to filter genes and details in a particular table from
another table. There exist .about.3,000 genes that have overlapping
sequence regions between two or more genes. The module provides the
ability to remove the overlapping genes, enabling tables of genes
with and without the overlaps. Furthermore, we identified that
cryptic regulatory elements that occur in many places throughout
the genes exhibit overlaps within the same type of element (e.g.,
one cryptic acceptor with another cryptic acceptor), or between
different types of elements (e.g., a cryptic donor with a cryptic
acceptor). The module provides the ability to analyze the details
of genes and genetic regulatory elements with mutations with and
without such overlaps.
[0098] Gene discovery module 260-1 provides illustrations for the
above instances and statistical information are provided with
tabular, graphical, and sequence views of genes, including the
mutations, their effects, and disease connections. Gene discovery
module 260-1 may also be configured to score and rank the causality
of genes and patient risk of disease in a cohort and its
application in individual patients' diagnosis. Genome analysis
engine 255 defines and assigns scores and ranks to genes and
patients based on mutations in regulatory and coding elements,
using the various approaches. These approaches use the data from
the total number of mutations from different regulatory elements of
genes, and the total number of patients with such mutations, in the
genomic sequences of patients within the cohort. Relevant genomics
test reports from whole exome, whole genome, or targeted
resequencing strategies are readily accessible through gene
discovery module 260-1.
[0099] Electronic health record (EHR) module 260-2 includes a large
amount of data fulfilling numerous parameters for a large number of
patients. Currently, only a small subset of these patients' genomes
have been and are being sequenced. In some embodiments, EHR module
260-2 works with an institutional EHR, and it may not modify any of
these parameters or write new data into their EHR. In some
embodiments, EHR module 260-2 is independent of an institutional
EHR, to address several such issues. EHR module 260-2 will be able
to accommodate many of these issues and provide a seamless
interface between the IEHR and many of the present disclosure
modules.
[0100] EHR module 260-2 can contain millions of patients of
different age, gender, race, ethnicity, disease, drug usage, drug
efficacy, adverse reactions, treatment outcomes, geographical
regions, and time-interval. This information enables family
genomics, cohort genomics, and clinical trial genomics. The EHR
genomics module is integrated within the Genome Explorer platform
and can also be a standalone EHR platform for all EHR
functionalities, with a focus on integrating with genomics and
precision medicine for the patient, clinician, clinical
researchers, and clinical institution. The module provides an
interface for a systematized collection of electronically-stored
health information in a digital format of a patient's electronic
health records including clinical metadata, pathology reports, and
genomic reports. These records can be shared through
network-connected, enterprise-wide information systems across
various clinical departments like cardiology and neurology,
pharmacies, and pathology labs, and across various clinical
institutions.
[0101] Based on given parameters such as gender, age range,
disease, drug treatment, efficacy and side effects outcomes,
including specific efficacy and side effects, and the genome
sequence, EHR module 260-2 may identify patients fitting these
parameters. EHR module 260-2 processes the genome data for this
patient cohort (e.g., genes and biomarkers for the particular
phenotype(s)). In some embodiments, EHR module 260-2 uses a patient
selection module within genome analysis engine 255 to retrieve
patient data. In some embodiments, EHR module 260-2 uses the voice
navigation feature to capture the parameters. As the different
parameters are not given in a highly defined or structured manner,
we need to use special algorithms to capture these parameters from
the unstructured data from the EHR of different patients, which
have been input by various clinicians and healthcare providers,
which thus will have a lot of variations.
[0102] EHR module 260-2 includes patient metadata including the
patient's age, gender, habits such as smoking, alcohol, and drug
usages, diseases that the patient has been suffering from, and
medicines that the patient has been taking, and pathology data
including the tissue sections images, Immunohistochemistry (IHC),
Fluorescence in situ hybridization (FISH), and other relevant
information. The EHR includes the information on whether the DNA
from the patient has been sequenced, the nature of the DNA (WGS,
WES, TRS), the sequence format (MAF, VCF, FASTQ), and a link for
their locations.
[0103] Based on given parameters such as gender, age range,
disease, drug treatment, efficacy and side effects outcomes,
including specific efficacy and side effects, and the genome
sequence, we can isolate the patients who fit these parameters. We
can subject the genome data for this patient cohort through the
disclosed platform and get the genes and the biomarkers for the
particular phenotype(s). We can use the patient selection module
within GE or the Voice Navigation to capture the parameters. As the
different parameters are not given in a highly defined or
structured manner, we need to use special algorithms to capture
these parameters from the unstructured data from the EHR of
different patients, which have been input by various clinicians and
healthcare providers, which thus will have a lot of variations.
[0104] EHR module 260-2 may also include meta information with
patient demographics, medical history, medication, allergies,
risks, habits, immunization status, vital signs, and personal
statistics like age, BMI, and billing information.
[0105] EHR module 260-2 enables access to relevant patient
information from and into the EHR including the patient meta
information, disease, drug, and genomic sequences (DNA and/or RNA)
or its weblinks in an automated way. Additionally, it has automated
the use of this information in processing, analysis, and reporting
of the patient's genomic data and his or her clinical, disease, and
drug response outcomes. Once the genetic/genomic test is ordered by
the clinician and the corresponding genomic sequencing has been
completed, and the data links become available in EHR module 260-2,
the platform automatically locates the sequenced data and starts
the analysis. Based on the test and input file format, the platform
automatically selects the analysis pipeline with the most suitable
parameters and integrates the clinical report to EHR module 260-2,
upon completion of the analysis.
[0106] In addition, different institutions have different EHR
systems (e.g., Cerner, Epic), and it will be difficult to customize
different requirements in each of them. To resolve this, EHR module
260-2 interfaces between genome analysis engine 255 and each of the
EHRs from different Health Systems.
[0107] In some embodiments, EHR module 260-2 is spread across
various categories and modules such as EHR, Order Sequence, Launch
Analysis, Clinical Report, Pharmacogenomics module (PGx), TMB/MSI,
Rearrangement, Pathogene, and Pathogenome. Clinician centric user
interface that includes a navigation pane, and a drop-down menu
showing different platforms (e.g., Patient genomics, Family
genomics, Cohort genomics, and Clinical trial genomics) and modules
to conduct various types of clinical, translational, and genomic
analyses using a given patient's genome data.
[0108] In some embodiments, EHR module 260-2 automatically
downloads data from the IEHR into the GE EHR system that are
relevant to genomics, and to provide various kinds of analytical
capabilities and metrics for the administration personnel and
researchers. Thus, EHR module 260-2 enables various genomics
related analysis to the clinical researchers, and
department/hospital administrators and executives. For example, EHR
module 260-2 retrieves and sorts a variety of data such as the
number of sequenced patients in various common and rare diseases,
number of patients with WGS, WXS, and TRS sequences, and the number
of cohort studies or clinical trials carried out. In addition, EHR
module 260-2 provides a standalone EHR system for patient
administration for small to medium clinics and primary healthcare
facilities that are emerging in the US to serve a large population
of patients.
[0109] Launch module 260-3 launches a genome analysis when the
sequencing for a chosen genomic library strategy is completed and
the raw data is linked to the patient EHR. In some embodiments,
launch module 260-3 provides a genomic test report once the
analysis is completed successfully.
[0110] Report module 260-4 includes pathology reports including
various reports of diagnosis for a disease like
Immunohistochemistry (IHC), biopsy, magnetic resonance imaging
(MRI), Fluorescent In-situ hybridization (FISH), and radiology
images along with regular blood, urine, and saliva laboratory test
reports.
[0111] Pharmacogenomics (PGx) module 260-5 enables the discovery of
a patient's response to a drug that may be taken based on the
patient's genomic variations within the genes that transport or
metabolize, or in any way react with the drugs within the patient,
even before the patient has taken the medicine. In some
embodiments, PGx module 260-5 determines the pharmacogenomic status
of the drug-metabolizing genes to predict the response of the
patient as recommended by Clinical Pharmacogenetics Implementation
Consortium (CPIC) guidelines (panel of 11 genes) and the eMERGE-PGx
network (panel of 82 genes). PGx module 260-5 expands further with
more genes, more pathogenic mutations, and information for more
drugs that ensue in the future.
[0112] In some embodiments, PGx module 260-5 produces a report
including mutations in the drug-metabolizing genes and their
corresponding metabolizing-phenotype of normal, intermediate,
rapid, or poor metabolizers as identified in the patient are
indicated. The drugs are flagged to avoid use, use with caution, or
use as directed based on the clinical association. Table I
illustrates a list of pharmacogenomics tests available in PGx
module 260-5 to analyze the drug-metabolizing genes and predict the
drug response.
TABLE-US-00001 TABLE I Pharmacogenomics Test For Drug Responses The
pharmacogenomics analysis includes 11 genes (CYP1C9, CYP2C19,
CYP2D6, CYP3A5, CYP4F2, DPYD, NUDT15, SLCO1B1, TMPT, UGT1A1 and
VKORC1) as per the recommendations of the Clinical Pharmacogenetics
Implementation Consortium (CPIC) guidelines. Based on the genotype
of these drug metabolizing genes, the diplotypes are computed to
determine the metabolizing phenotypes The genes reported below were
found to be mutated and shown to have clinical association with
drug metabolism for different medication categories. Gene Variant
Haplotype Diplotype Priority CYP2C19 rs12769205 No function *35/*35
High Risk Poor Metabolizer CYP4F2 rs2108622 Normal Function *1/*3
High Risk Decreased Function Intermediate Metabolizer TPMT r2842934
Normal Function *1S/*1S Low Risk Normal Metabolism
[0113] PGx module 260-5 determines a drug metabolizer status and
drug dosage based on mutations in a combination of multiple genes.
In some embodiments, the drug metabolizer status of a gene is
determined based on the combination of variants and the
haplotypes/diplotypes. In some embodiments, a method for
determining a drug metabolizer status uses the pathogenicity of
mutations to first determine if a gene becomes defective and, if it
does, it is considered as a non-metabolizer. The method equates
this status to a poor-metabolizer. In some embodiments, the method
includes information on the zygosity of mutations, wherein
deleterious mutation in one allele is an intermediate metabolizer
and in both alleles is a non-metabolizer.
[0114] In some embodiments, the metabolizer status of a gene is
determined based on the mutations in at least one of the genetic
elements in the drug metabolizing genes. When a drug metabolizing
gene has at least one pathogenic mutation in any one of the genetic
elements and is homozygous deleterious allele (VV), then it becomes
a non-metabolizer (poor). Whereas, if that mutation is
heterozygous, it is an intermediate metabolizer, since one allele
has become defective and the other allele is normal. Using this
approach, some embodiments determine the metabolizer status of a
combination of more than one gene, and also determine the dose of
the drug based on this combination. As an example, PGx module 260-5
may perform the following sequence of steps: A drug is metabolized
by two genes--CYP2D6, DYPD; CYP2D6 is mutated (VV or RV). When
CYP2D6 is VV or RV and DYPD is not mutated, then the metabolism
status of the drug based on combination of genes is "intermediate."
When both genes are mutated, CYP2D6 has one VV and DYPD has only
RV, then PGx module 260-5 determines a status as between "poor" and
"intermediate" metabolizer. When both genes are mutated, CYP2D6 has
one VV and DYPD has one VV, then PGx module 260-5 identifies the
status as a "non-metabolizer," or a "poor" metabolizer.
[0115] In some embodiments, PGx module 260-5 includes a quantity of
a drug metabolized per unit time by different genes. For instance,
when the gene DYPD metabolizes 100 units of a particular drug, and
the gene CYP2D6 metabolizes 20 units of the same drug, then PGx
module 260-5 incorporates this information to determine the dose,
in addition to the metabolizer status of a combination of different
genes. When a VV variant is present in both genes, PGx module 260-5
may drastically reduce the medication dose to about 1/100th to
1/10.sup.th of a regular dose. When a VV variant is present in one
gene and an RV variant is present in the other gene, PGx module
260-5 may reduce the dose to 1/4th of the regular dose. When the
two genes have an RV variant, PGx module 260-5 may determine a 1/2
reduced dose relative to the regular dose. When one gene has an RR
variant and the other gene has an RV variant, PGx module 260-5 may
determine a 3/4 of a regular dose. When more than 3 genes are
involved in the metabolic chain for a given drug, PGx module 260-5
applies a similar analysis using the total number of genes and the
number of alleles with Pathogenic mutations.
[0116] Cohort genomics module 260-6 enables genomic research of
cohorts of patients exhibiting specific diseases with the aim of
finding the genes and their defects that are causative of the
disease or adverse drug reactions in the patients. The basic
concept employed in the GE cohort genomics platform is that the
disease-causing mutations can occur in any part of a gene including
CDS or any of the several genetic regulatory elements. It
facilitates the discovery of various details of these defects in
particular genes, including within different types of genetic
elements such as exons, introns, splice sites, branch sites,
promoters, poly-A sites, cryptic versions of many of these
regulatory motifs and elements, and 5' and 3' UTRs. By enabling the
identification of genes containing a combination of deleterious
mutations within multiple genetic elements across different
patients, this platform has the potential to uncover the genes with
the highest probability of causing the disease, trait or
conditions, and the adverse drug reactions (ADRs) in a cohort of
patients than focusing mainly on the coding sequence (CDS) of the
protein.
[0117] In some embodiments, cohort genomics module 260-6 interacts
with gene discovery module 260-1, EHR module 260-2, launch module
260-3, and pathogenome module 260-10 to prepare a cohort report.
The cohort report may include visual elements provided to
application 222 in graphic payload 225, and may also include
tabulated information for storage in database 252. The elements in
the cohort report provide knowledge about the characteristics of
the patients in the cohorts and the study, listed as follows:
Disease: Disease/phenotype of the individuals present in the
cohort; Number of Patients: Total number of patients involved in
the study; Unique Mutations: Total number of unique mutations
observed in the patients; Unique Mutated Genes: Total number of
unique mutated genes observed in the patients; and PubMed Id: The
identifier of the published article if the data is sourced from a
published source.
[0118] The cohort report is a comprehensive report of many of the
findings from the cohort analysis in tabular, graphical, and
sequence illustrations. They include the tabular details for the
mutations in CDS and each of the different regulatory elements and
their cryptic versions. For each table, graphical illustrations are
provided depicting the details of the genes, patients, mutations,
and other relevant information with popup windows and mouse overs.
The cohort report also gives summary findings in graphical and
tabular forms. In some embodiments, the cohort report includes
summary statistics: This section presents the statistics of
frequently occurring causal mutations distributed across genes in
the group of individuals. The section also delivers complete
details of those mutations affecting the domains, gene families,
pathways, and actionable drugs. Thus, it provides the
identification of the presence of causal and driver mutations in
population data sets which is of clinical and biological interest.
In some embodiments, the cohort report includes key genomic
findings from cohort analysis: This section provides the
distribution of mutated genes among the individuals affecting
protein domains, pathways, and potential actionable drugs. The top
recurring genes with more mutations provide an insight into those
driver genes and mutations responsible for the phenotypes or
certain characteristic behavior of the disease associated with the
cohort individuals. The protein domains encoded by top recurring
mutated genes can be a traceable target for new drug development.
The pathways affected by the commonly recurring genes deliver
information about the molecular mechanisms which regulate the
disease. The section also provides the statistics of domains,
pathways, and actionable drugs commonly observed in the cohort
study. In some embodiments, the cohort report includes
comprehensive characterization of mutated genes: This section
summarizes the distribution of genes having actionable mutations,
affected pathways, and protein domains. Additionally, it gives the
drug information applicable to the particular gene. The
availability of this information illustrates the clinical
significance of the gene in a cohort. In some embodiments, the
cohort report includes a distribution of affected domains: This
section summarizes the top domains which occur commonly within the
mutated genes in the cohort. It aids in sharpening the functional
interpretation of the impact of domains, as they are the embodiment
of the entire gene. Thus, facilitating the identification of
hotspot mutations in actionable protein domains provides insights
to the researcher on the relationship between the gene, domain,
protein, and disease. In some embodiments, the cohort report
includes top genes (based on mutation frequency): This section
summarizes and helps to analyze and visualize the frequently
mutated genes among the cohort, within different genetic elements
individually or in combination. In some embodiments, the cohort
report includes top genes (based on domain): This section
summarizes and helps to analyze and visualize the frequently
mutated genes, based on the affected protein domains present in the
selected study cohort. In some embodiments, the cohort report
includes top genes (based on pathway): This section summarizes and
helps to analyze and visualize the top mutated genes based on the
affected pathways in the selected cohort study. It also facilitates
in unraveling the disease-causing mechanism and provides a deeper
understanding to the researcher about the molecular functions of
the particular gene and the affected pathway.
[0119] In some embodiments, cohort genomics module 260-6 produces a
mutational landscape report. The mutational landscape report
provides a multitude of analysis and visualization features to
advance our understanding of the large-scale genomic data from the
patient cohorts. The cohort-based large-scale characterizations
often produce large amounts of data in the form of somatic
single-nucleotide variants (SNV) and small insertion/deletions
(indels). These data provide a baseline for many analyses such as
driver gene detection, pathway analysis, mutational signatures,
gene interactions, and survival analysis. On the other hand,
visualization of these complex and heterogeneous data plays key
roles in genomic studies, when the clinicians find it difficult to
interpret the clinical significance of the data. In some
embodiments, the mutational landscape report includes co-occurrence
and mutual exclusivity and refers to two or more mutated genes that
tend to be positively or negatively correlated among different
samples in a cohort. A pair-wise Fisher's Exact Test is used to
detect such a significant pair of genes. The test is used to
support co-occurrence when the number of samples with alterations
in both genes is significantly higher than expected by chance.
Likewise, it suggests mutual exclusivity when the number of samples
with alterations in both genes is significantly lower. The efficacy
of this test depends on the hypothesis that the genes' alterations
across samples are independent and identically distributed. In some
embodiments, the mutational landscape report includes the
transition-transversion ratio and refers to the distribution of two
types of genetic mutations in the cohort of patients considered to
have distinct effects on gene regulation and protein-coding
factors. Transitions are DNA mutations that specifically exchange a
one-ring pyrimidine with another pyrimidine or a two-ring purine
for another purine. Transversion, in contrast, are mutations that
change the nucleotide base from a purine to a pyrimidine or vice
versa. It is well known that Transitions are enriched over
Transversion in protein-coding regions of the human genome. This
plot summarizes SNVs from 12 types of transitions and transversions
into six different categories that start with `C` or `T` in the
opposite strand. The stacked bar plot (bottom) shows the
distribution of mutation spectra for every sample in the MAF file.
In some embodiments, the mutational landscape report includes the
significant pathways that control cell cycle progression,
apoptosis, and cell growth, based on the frequent genetic
alterations distributed across the patients in the cohort that are
reported. The fraction of genes with at least one alteration in
each of the pathways is computed based on the total number of genes
(N) involved in the pathway. The fraction of samples with at least
one alteration in each of the pathways are also evaluated. A sample
was considered as altered in a given pathway if one or more genes
in the pathway contained a recurrent or known driver alteration.
The plots are shown for the fraction of affected genes in each
pathway and the fraction of samples having mutated pathway genes.
In some embodiments, the mutational landscape report includes
significantly mutated driver genes that are identified from the
patient cohort. The concept is based on the fact that most of the
variants in disease-causing genes are enriched at hot-spots.
Protein altering mutations of each gene across the cohort are
evaluated looking for those protein residues having multiple
mutations barely expected by chance. Second, these positions are
thereafter grouped to form mutation clusters or hotspots. Third,
each cluster is scored with a figure proportional to the percentage
of the gene mutations that are enclosed within that cluster and
inversely related to its length. The gene clustering score is
obtained as the sum of the scores of many clusters (if any) found
in that gene. Finally, each gene clustering score is compared with
the background model to obtain a significance value. The background
model is obtained performing the same steps as above but assessing
only coding silent mutations. In some embodiments, the mutational
landscape report includes a survival analysis. To prepare the
survival analysis, cohort genomics module 260-6 is configured to
group samples based on the survival status (alive or deceased), the
survival time, and the mutation status of significantly mutated
genes across the cohort. Kaplan-Meier estimate is used to measure
the fraction of subjects living for a certain amount of time after
treatment. The effect of an intervention is assessed by measuring
the number of subjects survived or saved after that intervention
over a period of time.
[0120] In some embodiments, cohort genomics module 260-6 includes a
family genomics report. The family genomics report includes a
trio/family analysis of detailed clinical report depicting variants
or identifying shared chromosomal regions among affected family
members along with the mode of inheritance and pedigree information
which represents the familial relationships, gender, ethnicity, and
phenotypic information of the individuals in the given family. This
helps in analyzing hereditary patterns and phenotypic factors of
genomic variants/mutations to quickly identify and understand these
patterns in the patient's family history. The family genomics
report includes patient meta-information, testing indications, key
findings or absence of findings, conclusions, and recommendations.
A clinical summary of the genomic findings precedes the clinical
report that compiles the primary diagnosis based on the disease
indications, secondary findings based on the ACMG 59 genes, and the
present disclosure's proprietary disease panels, pharmacogenomic
indications, therapeutic indications, and the suggested clinical
trials. The detailed information about the variants such as the
affected genes, variant's consequence, variant allele frequency,
and zygosity for each family member, mode of inheritance, and the
associated phenotypes of the specific variants are provided along
with the minor allele frequency, and pathogenicity class including
the ACMG 2015 guidelines and the present disclosure's pathogenicity
criteria based on the score differences for regulatory elements
from the reference sequences and genes. The mutational consequences
are overlaid on the genome-scale illustration and displayed across
the family members showing the pattern of inheritance along with
the relevant mutation details. The mutations can also be selected
based on the pathogenicity, type of genetic elements including
coding (CDS), regulatory and splicing elements, particular genetic
elements, disease panels, or the mode of inheritance as an
interactive visualization. These variants are also reported as per
the HGVS nomenclature along with Refseq accession numbers, genome
build, and official gene names, as per Human Genome Organization
(HUGO) guidelines. Actionable variants are highlighted along with
therapies approved by regulatory agencies like the FDA. The
clinical report is divided into sections of summary statistics,
test performed block, therapeutic indications, promoter, poly-A,
exonic, intronic, and deep intronic mutations, variant
interpretation, ACMG incidental findings, drug details, gene
details, and variants of uncertain significance (VUS). For
significant variants not included in the system generated clinical
report in the diagnosis, the clinician can bookmark variants for
the disease which is considered important for the disease, and edit
any portion of the clinical report and customize the clinical
report. These findings can positively impact clinical decisions and
can modify treatments and clinical care. Mutations identified
within the family are stacked and overlaid on the genome-scale
illustration as a pathogenome frequency plot with multiple options
for interactive visualization. The mutations can be selected by
disease, consequences, domain, pathway, gene family, and can be
grouped by either gene or mutation. The mutation position and
frequency are plotted for individual genes as a Pathogene Frequency
Plot. Mutations for one individual within the family are plotted on
an illustration with multiple tracks like consequence,
pathogenicity, ACMG, MMR, PGx, and eMERGE for deeper analysis in
individual patient pathogenome. This feature enables the selection
of a particular patient from a group of patients in the Family,
Cohort, or Clinical Trials modules, and displays the Pathogenome
visualization plot for that patient. Similarly, the genes and
mutations are stacked for many individuals within the family
together for a comprehensive view in the Family Pathogenome
illustrations.
[0121] Cohort genomics module 260-6 includes the information on
mutations within the genetic elements in one or more patients in
the EHR. This dashboard will be connected to the institutional EHR
(IEHR) and EHR (GE-EHR) in such a way that when a new patient is
processed in genome analysis engine 255, his/her sequence details
and clinical reports will be automatically updated in the cohort
genomics dashboard. Thus, it becomes a repository of clinical
reports of each of the processed patients through the present
disclosure. This repository can be used to perform several cohort,
family, and clinical trial projects for diagnosis, therapeutics, or
PGx in real time for any disease, drug, or side effect phenotypes
at any given time. Cohort genomics module 260-6 illustrates metrics
about different genetic elements and can be viewed in real time for
one patient or 1000 patients directly uploaded from a real-time
EHR.
[0122] In some embodiments, cohort genomics module 260-6 provides a
real-time interaction with pathogenome module 260-10. Cohort
genomics module 260-6 analyzes real-time data in the framework of
pathogenome module 260-10. Additionally, it has the ability to
visualize one or more patients in a Pathogenome framework, wherein,
when a single patient is chosen, the Individual Pathogenome
framework will be executed, and when more than one patient is
chosen, then a Cohort Pathogenome framework will be executed.
[0123] In some embodiments, cohort genomics module 260-6 displays
the metrics and statistics of each of the genetic elements for each
of the 20,000 genes from one or more patients in various ways in
real time, including frequency plots of various parameters as in a
pathogenome plot both graphically and in tabular form. In some
embodiments, cohort genomics module 260-6 graphically depicts each
of the multiple patients in bands on a single line and highlights
the ones having mutations in the genes or genetic elements. In
addition, it enables the selection of various gene panels based on
several criteria such as diseases, gene families, domains, and
pathways.
[0124] In some embodiments, cohort genomics module 260-6 enables
the selection of various gene panels based on several criteria such
as diseases, gene families, domains, and pathways. Furthermore, it
enables the selection of patients based on various parameters such
as disease, drugs taken, gender, age, tumor or disease stage, drug
response phenotypes, and tissue type, to carry out cohort analysis.
It also provides statistics of many of the parameters from the EHR
and in real-time. For each element, a table icon provided will
enable the respective table with many of the details of genes,
mutations, and patients.
[0125] Cohort genomics module 260-6 may include several sub-modules
as follows:
[0126] Cohort Builder sub-module: a component that facilitates to
select variant call files such as VCF or MAF files of patients in
combination with patient metadata and other relevant information
from GE EHR or public resources and create a cohort. The patients
can be searched automatically by GE's pre-defined rules or based on
user-defined criteria like disease, age, gender, race, ethnicity,
drug usage, ADRs, drug efficacy, or any other available metadata to
create super-cohort or divided into sub-cohorts based on user
discretion. The cohort builder can also use the "Bulk upload"
feature to upload multiple VCF or MAF files together from S3, FTP,
HTTPS, or from the local computer. The selected VCF files are
converted to MAF files or vice-versa and processed along with the
attached metadata for the cohort analysis. The cohort builder can
also split the cohort MAF or VCF file(s) into individual patient
VCF files and can produce clinical reports for each patient from
the cohort in an automated manner.
[0127] Launch sub-module: performs identifying the group of
patients with a particular condition, disease, and drug or other
treatment from the EHR data that forms the cohort, the set of genes
that will be tested across the cohort, super-cohort or sub-cohort
of patients, and the tissue, blood, saliva or other samples from
each patient from which the DNA/RNA will be studied. The launch
sub-module may include several panels, as follows. Somatic cancer
gene panel: A panel of somatic cancers with the associated genes.
Germline cancer gene panel: A panel of hereditary cancers with the
associated genes. Non-cancer Inherited Disorders gene panel: A
panel of inherited non-cancer disorders with the associated genes.
Industrial gene panel: Various disease/research gene panels from
IonAmpliseq, Illumina, and Oncomine. ACMG 59 gene panel: The list
of 59 genes from the American College of Medical Genetics and
Genomics (ACMG). Specific mutations in these genes are known to be
causative of disorders with defined phenotypes. Drug metabolizing
gene panel: The genetic variants in the drug-metabolizing genes
produce various impacts on the degree of converting the drugs into
water-soluble metabolites. Based on the impact of mutations in
these genes, the metabolizer. The status of these genes is
classified as "Normal Metabolizers," "Poor Metabolizers,"
"Intermediate Metabolizers," and "Indeterminate." Human genes
panel: Researchers can select any gene or all genes from the
available "All human gene" panel, listing all .about.20,000 human
genes.
[0128] Patient/Sample & Gene selection sub-module: enables the
analysis between a specific set of genes and a specific set of
patients from the cohort to facilitate biomarker discovery.
[0129] Patient Group creation sub-module: enables the creation of a
user-defined patient group (cohort) or subgroup (sub-cohort) from
the EHR. EHR in a health institution would contain a large number
of patients with various conditions or diseases (including cancers
or non-cancer disorders), who have been, or are undergoing
different types of treatment (e.g., drugs, surgery or other
treatment), and have undergone various tests (e.g., blood, biopsy,
imaging, immunohistochemistry). The user can search the EHR data
and select patients that satisfy various conditions (e.g., males,
females, smokers, non-smokers, patients with a specific disease or
condition such as heart attacks or lung cancer, or have undergone
various treatments such as warfarin or clopidogrel), and create one
or more patient groups (as shown below). The patient group creation
sub-module also enables the selection of user-defined sample(s) for
specific patients and creates sample subgroups, to study the
sample-specific characteristics, including the comparison between
primary vs metastatic cancer biopsy or blood/saliva/lymph samples
from patients, timeline experiments of disease progression or
therapy (e.g., chemotherapy), mutational differences over different
TNM stages, tumor heterogeneity, tumor microenvironment(s) and
sub-clonal studies.
[0130] Gene Group creation sub-module: creates user-defined gene
groups or subgroups and/or selection of standard industry gene
panels, which facilitates the clinical researcher for targeted
biomarker discovery. The inclusion and exclusion category
facilitates the researcher to select, add, and/or exclude
particular genes from all or subsets of human genes, based on the
research requirements.
[0131] Clinical trial module 260-7 combines the gene discovery
platform with pharmacogenomics for a cohort study. The analysis to
outline and study the drug responses from a patient cohort is
enabled by a user-driven patient identifier and the gene panel
input from the meta-information. Meta information becomes a matrix
of individual patient EHR, combined into a tabular format to
present the clinical presentations, disease condition, disease
(e.g., cancer) stage, various pathological test statuses,
disease-relapse, surgical resection, blood workup, drug efficacy,
etc., for all the patients present in the cohort study. This gives
a focused view of the broad spectrum of variables in determining
drug response. The analysis results are presented in the clinical
trial report, cohort report, and the pathogenome as statistical
evaluations or visualization modules.
[0132] Clinical trial module 260-7 unifies cohort disease and
genomic-data led research and/or discovery of varied drug responses
and reactions. The key influencing factors like age, personal
habits (exposure to smoke, previous chemotherapeutic therapies,
previous radiotherapies, and the like), genomic mutations, blood
profiles, cancer stages, etc., for short-term exposure, can be
explored to predict the drug efficacy responders and disease
regression, and adverse drug reactions and antagonistic reactions
in responders and non-responders with disease progression.
Long-term implications such as drug tolerance, drug safety, and
accumulation of toxicity based on the genomic signatures to
elucidate progression-free survival duration of the disease relapse
can be scrutinized for evidence-based clinical interpretation.
Clinical trial module 260-7 enables cohort-based drug efficacy and
adverse drug response prediction for both established and
investigational drugs from a group of patients with the same
clinical presentations, and personalized therapeutic indications
based on the individual's genomic information.
[0133] Clinical trial module 260-7 includes the following features:
Disease: disease diagnosis of the patients populating the cohort.
Number of Patients: number of patients populating the cohort.
Unique Mutations: total number of unique mutations reported in the
cohort. Unique Mutated Genes: total number of unique mutated genes
reported in the cohort. PubMed Id: Article identifier of the
published study reporting the cohort metadata, clinical
presentation, methodology of various investigations performed, and
their outcomes when the study is based on a published report.
[0134] Clinical trial module 260-7 is configured to group a subject
cohort based on the efficacy and side effects of a drug consumed by
two or more patients during a clinical trial. In some embodiments,
clinical trial module 260-7 determines the frequently mutated genes
in each group to find mutations and genes that are effective for a
drug and cause side effects in patients having these mutations,
during a clinical trial. In some embodiments, clinical trial module
260-7 is configured to collect data from multiple EHRs from various
institutions and integrate it into a single EHR to perform clinical
trial studies from patients with a particular disease or drug
response phenotype across multiple institutions, thus paving the
way for interoperability and cohort of cohort studies. In some
embodiments, clinical trial module 260-7 is configured to
graphically visualize a variety of data such as the number of
sequenced patients exhibiting various common and rare diseases,
number of patients using a particular drug with a specific type of
drug response, number of patients with WGS, WXS, and TRS sequences,
and the number of cohort studies or clinical trials carried
out.
[0135] Tumor mutation burden and microsatellite instability
(TMB/MSI) module 260-8 provides a diagnostic immunotherapy
biomarkers (TMB and MSI) status from a subject's sequence. In some
embodiments, TMB/MSI module 260-8 measures a number of somatic
protein-coding mutations occurring in a tumor specimen per million
base pairs. For example, in some embodiments, TMB/MSI module 260-8
may classify results as TMB-High: with above 20 mutations per
megabase (mutations/Mb), TMB-Intermediate: about 6-19 mutations/Mb,
whereas TMB-Low may be about 5 mutations/Mb, or less. An MSI is an
FDA-approved immunotherapy biomarker across many major solid tumor
types. In some configurations, MSI is caused by insertion or
deletion of repeating bases during DNA replication. In some
configurations, MSI results from the failure of the mismatch repair
system (MMR) to correct DNA replication errors, resulting in an
abnormally high frequency of genetic mutations. Accordingly,
TMB/MSI module 260-8 classifies results as: MSS (Microsatellite
stable) status, and MSI-H (Microsatellite Instability--High)
status. Based on the TMB and MSI status of the patient, the
response to immune checkpoint inhibitors is predicted. TMB/MSI
module 260-8 enables the study of the mismatch repair genes (MMR
genes) or many human genes.
[0136] Rearrangement module 260-9 is configured to display the
details of the patient's defects in each gene in the regions where
a structural variation (e.g., translocation, transversion,
inversion, deletion, and insertion) has occurred in graphical
displays, depicting the details of the variation at the macro- and
molecular level as to the point of the variation within one or more
genes, and further drilling down into the sequence views of the
structural alteration, with many of the elements of the involved
chromosome(s) and genes displayed on the gene sequence,
representing them in different color codes, with the pre-alteration
and post-alteration details. Rearrangement module 260-9 is also
configured to display the details of the patient's defects in each
gene in the regions where a copy number variation (CNV) has
occurred in graphical displays including the genomic or genetic
regions that have been amplified (or deleted), and depicting the
details of the amplification (or deletion) at the macro- and
molecular level, and further drilling down into the sequence views
of the amplification (or deletion), with many of the elements of
the CNVs displayed on the genome and gene sequence, representing
them in different color codes, with the pre-CNV and post-CNV
details. Rearrangement module 260-9 is also configured to display
the details of the patient's defects of the two genes that are
fused together in graphical displays and depicting the details of
each gene at the macro- and molecular level, and further drilling
down into the nucleotide and protein sequence views of each gene
and fused chimeric gene, with many of the elements of the fused
breakpoint displayed on the genome and gene sequence. Rearrangement
module 260-9 is also configured to visualize many of the
chromosomal rearrangements identified in the particular patient.
The breakpoints of rearrangements are labeled and the gene/sequence
structures that are affected due to chromosomal rearrangements (SV,
CNV, and gene fusions) are shown with graphical, tabular, and
sequence illustrations.
[0137] Pathogenome module 260-10 integrates genomic data
visualization with extensive patient records. Pathogenome module
260-10 enables the discovery of genes causal of disease and adverse
drug reactions (ADRs). This approach enables the illustration and
visualization of the deleterious mutations in many of the genes in
the genome depicted to scale in one view. Pathogenome module 260-10
also enables the frequency of the deleterious mutations and genes
from a cohort of individuals. Thus, pathogenome module 260-10
enables the visualization of particular molecular and clinical data
types by taking into account complex relationships between patients
in a family or cohort.
[0138] Pathogenome module 260-10 supports the interactive analysis,
creation, and refinement of patient groups, disease groups, and
genetic elements groups, and visualizes the corresponding landscape
of genes, mutations, and genetic elements on the genome-scale. It
encompasses the whole genome details to successively finer levels
of the chromosome, gene, and genetic elements, down to the level of
sequence.
[0139] Pathogenome module 260-10 enables the visualization and
analysis of the genes and genetic elements that are causal or
involved in disease from the whole human genome in a simplified
manner, and the navigation from the whole genome to chromosomes to
single genes. Pathogenome module 260-10 also illustrates different
genetic elements and the sequences of genes and genetic elements
that incorporate the variants and mutations at different
levels.
[0140] Pathogenome module 260-10 provides visualization approaches
including and beyond particular molecular and clinical data types,
taking into account complex genomic relationships between patients
in a family or cohort, and the commonalities of genes and mutations
that are causal or contributing to the particular disease, trait,
or ADR in the cohort of patients or population. It further
facilitates the unraveling of mysteries hidden in the gene and the
genome with the help of modules such as Gene, Protein, Promoters,
Splice sites, Poly-A site, Branch Sites, Cryptic sites, Gene
Fusion, CNV, and SV views for many of the protein-coding genes, and
many of the ncRNA genes in the genome, by depicting the various
kinds of mutations within them.
[0141] Pathogenome module 260-10 illustrates genetic alterations
identified in the patients' genes under each genetic or genomic
category such as the regulatory regions, coding regions,
pharmacogenes, MMR (Mismatch Repair) genes, TMB (Tumor Mutation
Burden), and Microsatellite instability (MSI), enabling
high-resolution analysis. With the high influx of genomic data from
patient cohorts, the frequency of the mutations is visualized along
with the zygosity, inheritance patterns of genes, and mutations in
the individual or in the cohort of patients from the whole genome
in one view.
[0142] Pathogenome module 260-10 empowers unraveling the various
abnormalities identified in the individual's genome to facilitate
the clinician in the decision-making process. The module is a
one-stop solution for the users to visualize the summary of genetic
alterations identified in the individual from the whole genome in
one view. The variants identified in the individual's genes under
each genetic category across the whole genome, such as coding and
many of the regulatory elements, Pharmacogenes, MMR (Mismatch
Repair) genes, TMB (Tumor Mutation Burden), and Microsatellite
instability (MSI), or gene panels from medical guidelines such as
ACMG or the present disclosure's gene panels or user-specified
panels can be visualized, providing high-resolution analysis.
Non-coding RNA genes and the mutations within them in a patient
genome are also illustrated in genome-scale on the Pathogenome
platform. With the high influx of genomic data, Pathogenome
facilitates answers to numerous disease-related queries.
[0143] Pathogenome module 260-10 may include a gene family section
to display tracks specific to gene families. The mutations in genes
from each gene family are plotted in the tracks with a statistical
view on gene families and pathogenicity. This helps the researchers
or clinicians to get a better understanding of the various gene
families which might be responsible for the disease and drug
response pathogenesis. Pathogenome module 260-10 may include a
protein domain section to display tracks specific to genes that
contain particular protein domains. The mutations in genes from
different protein domain groups are plotted in the tracks with a
molecular view on the domains and pathogenicity. This helps the
researchers and clinicians to get a better understanding of
mutations in the genes containing various domains that might be
responsible for disease and drug response pathogenesis. Pathogenome
module 260-10 may include genes for cell structure, function, and
gene regulation section to display tracks specific to genes that
specify cell structure, function, or gene regulation such as
transcriptional regulation. The mutations in these types of genes
are known to cause many diseases including cancer and non-cancer
diseases. Mutations in these genes are plotted in the tracks with
molecular views to determine its impact on the cell structure,
function, or gene regulation and disease pathogenesis. This helps
the researchers or clinicians to get a better understanding of the
various ways the disease is caused, and thus predict the mechanisms
which might be responsible for the disease and drug response
pathogenesis, and to define precise diagnosis and treatment.
[0144] Pathogene module 260-11 helps to visualize the mutated
genes, proteins, and many of the regulatory elements such as
splice, promoter, and poly-A mutations in a particular patient or a
patient group along with the supporting evidence. In this approach,
the module acts as an ingenious visualization portal for genetic
variations like SNPs, Indels in exons, introns, genes, protein
domains, regulatory elements, UTR, promoters, splice sites, as
calculated by algorithms such as the Shapiro-Senapathy, and
supported by various public databases. The visualization framework
of the pathogene module is similar for any of the four modules
(Patient Genomics, Family Genomics, Cohort Genomics, and Clinical
Trials Genomics). Pathogene module 260-11 provides a visualization
portal for genetic variations like SNPs, Indels in genes, proteins,
regulatory elements such as promoters, splice sites, branch points,
poly-A, and other structural rearrangements for every gene from
every patient in various Pathogene tabs enabling a high-resolution
analysis. In some embodiments, pathogene module 260-11 aids in
representing genes and structural elements showing different types
of mutations from the patient, and the known mutations from the
different databases such as dbSNP, ClinVar, and COSMIC depicting
the CDS, and many of the regulatory elements, and categorizing them
into clinical significance, molecular consequence, and variation
type, and pathogenicity based on the SIFT and/or PolyPhen scores.
By accessing pathogene module 260-11, clinicians can easily review
the meta-information, pathology reports, and available genomic
reports to decide the appropriate sequencing strategy required for
disease prognosis.
[0145] A dark matter module 260-12 performs operations and searches
in a portion of the genome known as the "Dark Matter Genome." Genes
occur within the dark matter genome at high frequency, exhibiting
high scores for many of the coding and regulatory elements and
their cryptic versions in these genes, just as they occur within
the currently known genes in the NCBI/ENSEMBL data sources. This
discovery provides a high level of confidence for the existence of
genes within the dark matter genome, in addition to the currently
annotated genes in the human genome. Thus, the present disclosure
approaches this problem with the same methodologies to find genes
causing different disease and drug response phenotypes by exploring
the dark matter genomes of cohorts of patients with various
diseases.
[0146] Dark matter module 260-12 focuses on the clinical
application of the dark matter genome to discover the genetic
information that relates to disease and drug response in patients
with various diseases, and applies this information in clinical
practice and research. It aims to decipher the important coding and
regulatory regions within the dark matter genome and discover the
mutations that occur within them in different diseases, by
uncovering them in patient cohorts with various underlying
diseases.
[0147] In addition to identifying the disease-causing mutations in
the currently annotated genes in NCBI/ENSEMBL resources, dark
matter module 260-12 approaches this problem by detecting the
deleterious mutations from many of these elements within the genes
occurring in the dark matter genome. Dark matter module 260-12
utilizes the S&S method and other relevant algorithms that are
used for detecting the splice sites and mutations in them and
modifies them appropriately to develop unique scoring methods for
the different regulatory elements by applying the unique PWMs based
on the consensus sequences and the specific lengths of the
respective elements. With this basic approach, dark matter module
260-12 identifies deleterious mutations enriched within the
different regulatory regions of particular genes in the dark matter
genome in different diseases. Furthermore, it discovers the
deleterious mutations that are also enriched in cryptic sites for
the different regulatory elements that occur throughout the
sequences of various genes in the dark matter genome.
[0148] Dark matter module 260-12 works on its basic principle that
deleterious, disease-causing mutations would be enriched in the
genes that cause the disease in a patient cohort across many of the
genetic elements including the CDS and the different regulatory
elements in the gene, such as the promoter, UTR, splice donor,
acceptor, and branch sites, enhancers and silencers, and poly-A
sites, and their cryptic versions throughout the gene sequence.
Thus, the platform approaches the discovery of disease-causing
genes by identifying the deleterious mutations in multiple
different regulatory elements and their cryptic versions throughout
a gene across the patient cohort, and throughout many of the genes
in both dark and white matter genomes of the patients in the
cohort.
[0149] In some embodiments, dark matter module 260-12 is configured
to identify the regulatory motifs and elements, including the
promoters, splice acceptors, and donors, enhancers and silencers,
and poly-A, and their cryptic versions, and CDS, using the S&S
and other relevant algorithms and their modified versions in the
genes from the dark matter genome. In some embodiments, dark matter
module 260-12 is configured to identify the deleterious mutations
in the CDS, and many of the different regulatory motifs and
elements, from the genetic variants present in the patient's
sequence, using the S&S and other relevant algorithms and their
modified versions in the genes from the dark matter genome. In some
embodiments, dark matter module 260-12 is configured to identify
the dark matter genes that are frequently mutated in the CDS and
the regulatory regions in a cohort of patients with a given
disease. In some embodiments, dark matter module 260-12 is
configured to accumulate many of the mutation information from many
of the different genetic and regulatory elements and their cryptic
versions in different genes, including the frequency of patients
and frequency of mutations within each gene and the details of each
of the regulatory mutations within each regulatory element, in
addition to the CDS mutations. In some embodiments, dark matter
module 260-12 is configured to combine the set of genes from the
white matter genome and the new dark matter genome to present
integrated information of the potentially disease-causing genes for
the cohort. In some embodiments, dark matter module 260-12 is
configured to create a database for the dark matter genes and for
the integrated genes from different cohorts with the same
underlying disease, and for many cancer and non-cancer diseases. In
some embodiments, dark matter module 260-12 is configured to create
a database for the dark matter genes and for the integrated genes
from different cohorts with the same underlying disease. and
determining a gene signature for each of the different cancers and
non-cancer diseases. In some embodiments, dark matter module 260-12
is configured to identify cell structure, cell function, and
transcriptional regulator genes from the dark matter genome. In
some embodiments, dark matter module 260-12 is configured to
determine the signatures of causal genes for disease within the
white and dark matter genomes, based on the requirement of genes
for the basic cell structure, function, and regulation to become
defective by deleterious mutations. In some embodiments, dark
matter module 260-12 is configured to identify mutational
signatures in the different regulatory elements and CDS in
particular genes in the white and dark matter genomes.
[0150] FIG. 3 is a block diagram illustrating a genome sequence
analysis strategy 300, according to some embodiments. The patient
genomics platform is for analyzing an individual patient's genome
(DNA or RNA), raw data 301, and determining the pathogenic
mutations in each of the .about.20,000 protein-coding genes and
several thousand non-coding (nc) RNA genes within the human genome.
The platform identifies the variants in the genes, transcripts,
proteins, and ncRNAs and correlates their impact on potential
diseases that the patient might currently have or will have in the
future. The platform may also provide insight into gene expression
by looking at customized high-throughput NGS analysis of messenger
RNA (mRNA) sequencing data and performs a quality check to ensure
the quality of raw data, route 305. The module provides a QC report
which can spot biases in the library material or during sequencing
and compiles important statistics on NGS data, such as per-base and
per-sequence quality scores and GC content. The platform provides a
comprehensive clinical report of the patient, with key genomic,
diagnostic, and therapeutic findings, and a customized list of key
suggestions for the primary and secondary diseases, drug dosage and
treatment, Adverse Drug Reactions (ADRs), and further genomic-based
suggestions that would provide support for clinical decisions, and
validation 312 of the same.
[0151] The platform is enabled with customized high-throughput NGS
analysis workflows for individual samples, case-control, family
analysis, cohort analysis, and clinical trial analysis. The
analysis can be performed using raw sequencing data 305 or variants
VCF file format 307. Relevant, clinically useful genomic details
are given in the clinical report to aid the healthcare providers in
clinical practice. Several visualizations are provided for the
clinicians and researchers to gain a deeper understanding of the
genes, mutations, and clinical outcomes.
[0152] A primary analysis 310 involves a quality check of the raw
genomic data to avoid data anomalies. A QC report translates any
biases in the sequence library material, sequencing errors,
contamination of the data via other biological sources, and any
remaining adapter contents. In some embodiments, the QC report
includes: a Sequence length distribution, a Per base sequence
quality, a Per sequence quality, a Per base sequence content, a Per
sequence GC content, a KMER content, and an Adapter content.
Anomalies can be removed using automated trim/filter options with
the default and customizable parameters. The output is then ready
for processing in secondary analysis 320.
[0153] Secondary analysis 320 involves alignment, variant
identification, and annotation required for the tertiary downstream
analyses. Alignment includes mapping the cleaned sequence reads
against the human reference genome (GRCh37/hg19) and is performed
using a fast and accurate read aligner BWA-MEM, based on the
Burrows-Wheeler Transformation (BWT) algorithm. Read alignment
information is reported in SAM/BAM format, which is used for
calculating the depth of coverage and calling variants.
Post-alignment QC includes examining sequencing alignment data
according to the features of the mapped reads and their genomic
properties. This performance assessment provides an overall view
that helps detect biases in the sequencing and/or mapping, which
must be removed before calling the variants. The unaligned reads
are removed before variant calling and used to estimate any
possible non-human contamination. The aligned reads are processed
to de-noise by removing duplicate reads and reads mapping to
multiple positions, followed by sorting and indexing the BAM file.
Post-Alignment QC improves the accuracy and quality of the further
variant analysis. Variant calling includes identifying high-quality
Single Nucleotide Variants (SNVs) and small insertions and
deletions (InDels), using variant callers like GATK, VarScan,
Freebayes, Pisces, and Samtools. The likelihood data for each
genotype is calculated and variants 322 in each sample are
identified with respect to the reference genome. The variants are
reported in the standard Variant Call Format (VCF) file.
[0154] Secondary analysis 320 may also include variant annotation,
which includes a functional annotation of high-quality variants and
is performed using Ensembl VeP (Variant Effect Predictor) to
predict the effects of variants on genes and classify them into
synonymous, non-synonymous, missense, or silent consequences.
Multiple in-silico prediction algorithms are utilized to grade the
variants for their level of penetrance and classify the variants as
deleterious or benign. The present disclosure integrates
well-curated population databases such as ExAC, ESP, and 1000
Genomes; Disease annotation Databases such as dbSNP, dbNSFP,
COSMIC; Phenotype Databases such as ClinVar, OMIM, HPO, MONDO; Drug
databases such as PharmGKB and Drug regulations such as FDA, EMA,
PMDA, HCSC, and clinical trials. The annotated VCF file is
compressed and stored in a MySQLite database, facilitating readily
accessible and easy data extraction using simple SQL queries.
[0155] In some embodiments, genome sequence analysis strategy 300
may include the following features to assign the functional
information for the variants: A Genomic annotation, e.g., UCSC
Reference genome--GRCh37/hg19, ENSEMBL and Variant Effect Predictor
(VEP); functional consequences, wherein variants are annotated
using ENSEMBL VEP to determine the consequences viz. synonymous,
non-synonymous, nonsense, splice acceptor, splice donor, or
frameshift events; population databases, wherein a minor allele
frequency of variants across healthy population databases helps
distinguish common polymorphisms from disease-causing variants.
[0156] Some databases used in genome sequence analysis strategy 300
may include 1000 Genomes (1 KG)--Phase 3, NHLBI Exome Sequencing
Project (ESP), and Exome Aggregation Consortium (ExAC).
[0157] Genome sequence analysis strategy 300 may also access
disease databases to identify variants with known disease
associations, as established in disease and phenotype databases to
enhance the differential diagnosis. Some examples of these
databases may include: ClinVar, Catalogue of Somatic Mutations in
Cancer (COSMIC), dbNSFP, dbSNP, Online Mendelian Inheritance in Man
(OMIM), and Human Phenotype Ontology (HPO). In addition to the
above, other variant information like conservation, computational
predictions, and PubMed evidence are compiled and classified for
clinical reporting.
[0158] A tertiary analysis 330 may include variant prioritization
331-1, variant classification 331-2, and a variant interpretation
331-3, based on the consensus from pathogenicity classifiers,
clinical significance as reported by well-established databases 332
(e.g., genes, diseases, and drugs), appropriately annotated 335,
and professional practice guidelines. In some embodiments, variant
prioritization 331-1 identifies disease-causing variations among
dozens or hundreds of detected variants and identifies a functional
impact of variants.
[0159] Variant prioritization 331-1 employs many major in-silico
classifiers to determine the likelihood of the variants being
pathogenic or benign. Disease-causing potential of variants is
calculated based on amino acid structural substitutions using
physical and comparative considerations, protein functions, and
evolutionary conservation of amino acids. Multiple in-silico
pathogenicity classifiers are used to rank variants based on
weighted deleteriousness scores, and predicts the functional
impact, such as FATHMM, LRT (Likelihood Ratio Test), MetaLR,
MetaSVM, Mutation Assessor, Mutation Taster, Polyphen2, PROVEAN,
and SIFT.
[0160] Variant classification 331-2 classifies variants as per the
ACMG evidence and classification guidelines, which specify that
clinical reporting must clearly distinguish Benign and Likely
Benign variants from Variants of Uncertain Significance (VUS) and
Pathogenic variants. The present disclosure four-tiered variant
classification system specifies the amount and quality of evidence
required to classify genetic variants as per the Joint Consensus
Recommendation of the Association for Molecular Pathology (AMP),
American Society of Clinical Oncology (ASCO), and College of
American Pathologists (CAP). Table II below lists some of the codes
provided in variant classification step 331-2, associated with
pathogenic evidence--Strong, Moderate--or benign evidence, and the
like (e.g., as per ACMG-AMP).
TABLE-US-00002 TABLE II Strong Pathogenic Evidence VS1 Null variant
(nonsense, frameshift, canonical +- 2 splice sites, initiation
codon, single or multi-exon deletion) in a gene where LOF is a
known mechanism of disease S1 Same amino acid change as a
previously established pathogenic variant regardless of nucleotide
change S2 De novo (both maternity and paternity confirmed) in a
patient with the disease and no family history S3 Well-established
in vitro or in vivo functional studies supportive of a damaging
effect on the gene or gene product S4 The prevalence of the variant
in affected individuals is significantly increased compared with
the prevalence in controls Moderate Pathogenic Evidence M1 Located
in a mutational hot spot and/or critical and well-established
functional domain (e.g., the active site of an enzyme) without
benign variation M2 Absent from controls (or at extremely low
frequency if recessive) in Exome Sequencing Project, 1000 Genomes
Project, or Exome Aggregation Consortium M3 For recessive
disorders, detected in trans with a pathogenic variant M4 Protein
length changes as a result of in-frame deletions/insertions in a
non-repeat region or stop-loss variants M5 Novel missense change at
an amino acid residue where a different missense change determined
to be pathogenic has been seen before M6 Assumed de novo, but
without confirmation of paternity and maternity Supporting
Pathogenic Evidence P1 Co-segregation with disease in multiple
affected family members in a gene definitively known to cause the
disease P2 A missense variant in a gene that has a low rate of
benign missense variation and in which missense variants are a
common mechanism of disease P3 Multiple lines of computational
evidence support a deleterious effect on the gene or gene product
(conservation, evolutionary, splicing impact, etc.) P4 Patient's
phenotype or family history is highly specific for a disease with a
single genetic etiology P5 A reputable source recently reports
variant as pathogenic, but the evidence is not available to the
laboratory to perform an independent evaluation Strong Benign
Evidence A1 Allele frequency is >5% in Exome Sequencing Project,
1000 Genomes Project, or Exome Aggregation Consortium S1 Allele
frequency is greater than expected for the disorder S2 Observed in
a healthy adult individual for a recessive (homozygous), dominant
(heterozygous), or X-linked (hemizygous) disorder, with full
penetrance expected at an early age S3 Well-established in vitro or
in vivo functional studies show no damaging effect on protein
function or splicing S4 Lack of segregation in affected members of
a family Supporting Benign Evidence P1 A missense variant in a gene
for which primarily truncating variants are known to cause disease
P2 Observed in trans with a pathogenic variant for a fully
penetrant dominant gene/disorder or observed in cis with a
pathogenic variant in any inheritance pattern P3 In-frame
deletions/insertions in a repetitive region without a known
function P4 Multiple lines of computational evidence suggest no
impact on gene or gene product (conservation, evolutionary,
splicing impact, etc.) P5 The variant found in a case with an
alternate molecular basis for disease P6 A reputable source
recently reports variant as benign, but the evidence is not
available to the laboratory to perform an independent evaluation P7
A synonymous (silent) variant for which splicing prediction
algorithms predict no impact to the splice consensus sequence nor
the creation of a new splice site AND the nucleotide is not highly
conserved
[0161] Variant interpretation step 331-3 may be implemented as
recommended by the professional practice guidelines from NCCN,
ASCO, ESMO, and ACMG. Variant interpretation step 331-3 ensures
that the disclosed platform adheres to professional practice
guidelines and evidence-based guidelines from clinical regulatory
bodies for validation and reporting of NGS-based diagnostics in
regular clinical practice. The variants are classified in tiers
based on clinical pathogenicity and drug ability as per ACMG
guidelines which are seamlessly incorporated to inform management
decisions and interventions (cf. Table II). In order to prioritize
and interpret the variants, the biomarkers with therapeutic,
prognostic, and/or diagnostic significance are reported to empower
clinicians and other healthcare decision-makers around the world.
Some of the clinical guidelines may include the following
professional organizations and groups: the National Comprehensive
Cancer Network (NCCN), the American College of Medical Genetics
(ACMG), the College of American Pathologists (CAP), and the
American Society of Clinical Oncology (ASCO).
[0162] Variant interpretation 331-3 provides automated variant
interpretation for the pathogenic and likely pathogenic mutations
identified by the present disclosure processing workflow (as
outlined in the workflow). The interpretation explains the mutation
consequences (e.g., missense, nonsense, synonymous, splice
acceptor, splice donor, frameshift and In-frame InDels, etc.),
mutation type (single nucleotide variation, insertion, deletion),
and its effect on the protein in the CAP suggested HGVS syntaxes.
The interpretations are supported by the consensus of in-silico
pathogenicity prediction tools, ClinVar expert panel review, PubMed
evidence, and the minor allele frequencies from the healthy
population databases such as 1000 Genomes, ExAC, or ESP. Table III
below demonstrates the ability of the platform to designate the
variant, and providing the detailed interpretation of the variant
with its zygosity, pathogenicity, frequency, and other relevant
details. More specifically, Table III includes a stop gained single
nucleotide transversion mutation in the BRCA2 gene of a subject,
classified as pathogenic due to variant consequences reviewed by
ClinVar experts or having major consensus across most widely used
in-silico pathogenicity prediction tools. Some variants may be
reviewed by a ClinVar expert panel, and may be cited in medical art
publications. The variant is not hitting any domain and may be
present in the conserved region. The variant may be reported in the
ACMG list of incidental findings and COSMIC-Cancer Gene Census. The
variant is present in the healthy population databases such as 1000
Genomes, ExAC, or ESP with 0.0001 minor allele frequency.
TABLE-US-00003 TABLE III BRCA2 Con- Zygo- Variant sequences rs ID
VAF sity P Chr13:32,914,137 C > A Stopped Rs80359785 Case:
Hetero- Gained 72% zygous ENSP00000439902.1: p.Ser1882Ter
[0163] Curated assertions and drug labels, and approval statements
337 for the targeted therapeutic, prognostic, and diagnostic
biomarkers as approved by following regulatory agencies and making
it an accurate, intuitive, and efficient tool for clinicians to
improve patient care: the Food and Drug Administration, USA (FDA),
the European Medicines Agency (EMA), Health Canada (HCSC), and the
Pharmaceuticals and Medical Devices Agency, Japan (PMDA).
[0164] In some embodiments, genome sequence analysis strategy 300
prioritizes potential clinical trials for the targeted variants,
empowering the clinician with current evidence-backed options.
Tertiary analysis step 330 enables a healthcare provider or a
clinical or pharmaceutical researcher to conduct a deep analysis of
many of the variants, pathogenic mutations, and diseases hidden
within the genome of the patient. Detailed information accounting
for many possible mutation types (splice mutations, promoter
mutations, poly-A, coding mutations, DNA Gene Fusion, Structural
Variants, and Copy number variants) are provided and supported by
various quality metrics (Variant statistics, Alignment statistics,
Raw data QC) and are provided in this module. These variants can be
further evaluated and bookmarked to be included in the customized
clinical report.
[0165] In some embodiments, tertiary analysis step 330 for mRNA
employs the reads that were previously aligned to the reference
genome into transcript structure (transcript assembly process) and
estimates the abundance levels of genes and transcripts. The
expression levels are reported in terms of Fragments Per Kilobase
of Exon per Million mapped fragments (FPKM) and Transcripts Per
Million (TPM) units. FPKM is a normalized estimation of gene
expression calculated from the number of reads mapping to each
particular gene sequence by taking into account gene length and
sequencing depth. The expression values of genes and transcripts
are compared across samples post-transcript-assembly process. The
differentially expressed genes are identified from the merged
transcript GTF file of case and control samples. The significant
changes in the gene/transcript level expression between the samples
are annotated and reported.
[0166] A clinical reporting step 340 includes a comprehensive
clinical report of the patient, with key genomic, diagnostic, and
therapeutic findings, a customized list of key suggestions for the
primary and secondary diseases, drug dosage and treatment, Adverse
Drug Reactions (ADRs), and further genomic-based suggestions that
would provide support for clinical decisions. Clinical reporting
step 340 incorporates industry best practices in the implementation
of NGS-based diagnostics that live up to the highest technical
standards, quality controls, secondary findings, and variant
interpretation and prioritization. Genome sequence analysis
strategy 300 follows recommendations outlined by clinical
regulatory bodies for pipeline development, validation, quality
management, and regular updates, and interfaces seamlessly with
hospital EHRs, and genetic counselors. In some embodiments, genome
sequence analysis strategy 300 meets and exceeds clinical reporting
best practices by integrating clinical reporting guidelines
established by professional regulatory bodies like CAP and ACMG,
depending on the patient's disease.
[0167] A clinical report includes a diagnosis 341-1, a prognosis
341-2, and therapeutic decisions 341-3. In some embodiments,
at-a-glance relevant details are in a clear, concise format,
including patient meta-information, testing indications, key
findings or absence of findings, conclusions, and recommendations.
A clinical summary of the genomic findings precedes the clinical
report that compiles the primary diagnosis 341-1 based on the
disease indications, secondary findings based on the ACMG 59 genes
and proprietary disease panels, pharmacogenomic indications,
therapeutic indications, immune checkpoint inhibitors based on the
immunotherapy biomarker status, and the suggested clinical trials.
The variants are tier-classified and Tier-I, II, and III are
reported in descending order of potential significance. Tier-IV
variants (Benign/Likely benign) are not included in the report, as
per CAP guidelines. The variants are reported as per the HGVS
nomenclature along with Refseq accession numbers, genome build, and
official gene names, as per Human Genome Organization (HUGO)
guidelines. The clinical report clearly differentiates somatic and
germline variants and provides Zygosity information and mode of
inheritance. Actionable variants are highlighted along with
therapies approved by various regulatory agencies and suggested by
professional guidelines.
[0168] The clinical report may include sections such as clinical
report summary, test performed block, therapeutic indications,
immunotherapy markers, promoter, 5' & 3' UTR, poly-A and
exonic, intronic, and deep intronic splicing mutations, variant
interpretation, ACMG incidental findings, copy number variants
report, structural variants, gene fusions, PGx report, drug
details, gene details, and variants of uncertain significance (VUS)
as detailed below. For variants not included in the system
generated clinical report for the diagnosis, the clinician can
bookmark any variant of their choice and add such variants to the
report. The clinical report can further be customized by editing
the clinical summary, case history and findings, testing
indications, variant interpretations, and therapeutic indications
to suit the clinician's requirements.
[0169] In some embodiments, the clinical report prepared in step
340 may include multiple sections, as follows. A clinical report
summary may include a synopsis of clinical findings identified in a
patient and described in the clinical report. The section contains
key information such as primary diagnosis, secondary diagnosis,
targeted therapy, immunotherapy, pharmacogenomics, and clinical
trials. A primary diagnosis may identify and illustrate deleterious
genes and variants that are causal for the disease and are analyzed
in various aspects and clinically interpreted referring to the
condition to which the majority of the signs, symptoms, and
clinical phenotype of an individual are applied. A secondary
diagnosis contains the ACMG and secondary findings which impose the
co-existing phenotypes in a patient or develop subsequently and
have the potential to impact a patient's severity of illness and
risk of mortality. Immunotherapy includes the status for two
emerging diagnostic biomarkers, Tumor Mutation Burden (TMB) and
Microsatellite Instability (MSI) are provided which plays an
important role in predicting response to immune checkpoint
inhibitors across various tumor types. Pharmacogenomics may include
the drug-metabolizing phenotypes of 11 major drug-metabolizing
genes and the gene set from the eMERGE consortium, and their effect
on different medications are provided as per the recommendations of
the Clinical Pharmacogenetics Implementation Consortium (CPIC)
guidelines. These predicted drug responses can help clinicians to
make informed drug administration decisions. Targeted therapy and
Clinical Trials may indicate disease-associated therapeutic
procedures approved by various drug regulatory agencies like FDA,
EMA, PMDA & HCSC, and are provided along with available
contraindicated and/or investigational therapies associated with
the targeted mutations identified in the patient. The available
clinical trials of the suggested drugs are described suggesting the
clinical phases of targeted therapy. A summary of statistics
presents the analysis outcomes and reports the unique number of
genomic alterations, applicable Immuno-marker status, therapies
(approved, contraindicated, investigational), and the potential
clinical trials. Table IV, below, illustrates the summary tags of
the clinical report briefing the statistics of mutations,
immunomarker status, suggested therapies, and available clinical
trials.
TABLE-US-00004 TABLE IV Genomic Findings Immunomarkers Therapy
Clin- Tier Tier TMB Investi- Contra- ical 1 II (mut/MB) MSI
Approved gation indicated Trials 2 0 127 Stable 2 0 0 1
[0170] Table V is a tabular reporting of the biomarkers indicated
as pathogenic, likely pathogenic, or drug response for the
patient's disease, their respective tier classifications, mutation
consequences, Zygosity, and phenotype (wherever applicable). The
actionable and significant variants are tagged for enhanced user
understanding. Table V indicates the framework to report the
biomarkers identified in the genomic test along with the
consequences, and other relevant details.
TABLE-US-00005 TABLE V Test Performed For Breast Cancer Genomic
testing was performed to detect the presence of point mutations,
short insertion/deletions in the protein-coding regions, and
exon-intron boundaries associated with Breast Cancer, 2 gene(s)
(shown below) found to be mutated in this sample. Biomarkers Tier
Consequences P BRCA2 p.Ser1882Ter Tier 1 Stop Gained P NCOR1
p.Glu1860Ter Tier 1 Stop Gained
[0171] Table VI is a tabular reporting of the targeted therapeutic
indications and is provided for the patient's disease-specific
genomic alterations classified as approved, contraindicated, or
investigational therapy. The contraindications or the drugs which
need to be avoided are suggested as per the guidelines. Therapies
are reported with respect to the specific biomarker variants,
approved agencies, other approved cancer types, and the clinical
trials. Table VI depicts the targeted therapies suggested for each
genomic alteration identified in the patient. The module provides
therapies approved by drug regulatory agencies, contraindicated,
and investigational therapies.
TABLE-US-00006 TABLE VI Therapeutic Indications Approved For Other
Clinical Therapy Biomarkers Agencies Cancer(s) Trials Talazoparib
BRCA2 FDA EMA Pancreatic, II(1) p.Ser1882Ter Prostrate, Ovarian
Otaparib BRCA2 NCCN FDA Pancreatic, -- p.Ser1882Ter EMA HCSC
Prostrate, PMDA Ovarian Therapies With Potential Benefit Olaparib,
Talazoparib Therapies With Lack of Benefit No relevant therapies
are available Investigative Therapies No relevant therapies are
available
[0172] Table VII illustrates immunotherapy markers such as TMB, as
a measure of the number of somatic protein-coding mutations
occurring in a tumor specimen per million base pairs, and the MSI
caused by frameshift mutations of dinucleotide repeat bases during
DNA replication and the failure of the mismatch repair system (MMR)
to correct these error status are calculated in the patient's
genome. More specifically, Table VII illustrates the immune
biomarkers (TMB and Microsatellite status) identified in the
patient. Based on these biomarkers, immunotherapies are recommended
for the patient to enhance the clinical outcomes.
TABLE-US-00007 TABLE VII Tumor Mutation Burden (TMB) Microsatellite
Instability Status HIGH STABLE
[0173] In some embodiments, clinical reporting step 340 includes
reporting mutations located in the splice sites (exon-intron
boundaries), cryptic splice sites within exons and introns,
untranslated regions (UTRs), poly-adenylation sites (Poly-A),
and/or promoter regions, that lead to defective or non-functional
proteins. The splicing mutations (real and cryptic splice acceptor
and donor mutations) are scored using the Shapiro-Senapathy
algorithm (S&S algorithm), the promoter, branch point, UTR, and
poly-A mutations are scored using algorithms built on S&S and
other established scoring algorithms, and those that are determined
to be pathogenic and likely pathogenic are reported.
[0174] Some portions of the report provided in clinical reporting
step 340 may be as follows. An ACMG Incidental findings section
reports 59 curated genes for reporting of incidental findings in
clinical exome and genome sequence analysis as recommended by the
American College of Medical Genetics and Genomics (ACMG) which are
known to be implicated in a number of diseases including cancers
and non-cancer disorders with potentially actionable outcomes. The
platform identifies pathogenic and likely pathogenic mutations in
these genes in a patient and reports the ACMG indicated
diseases.
[0175] A copy number variants (CNV) section includes copy number
variation and involves alterations in the number of copies in
specific regions of an individual's DNA, which can either be
amplified or deleted. Such events are identified from the patient
DNA in the analysis and reported for disease association. CNV
involves alterations in the number of copies in specific regions of
an individual's DNA, which can either be amplified or deleted. The
copy number changes are inferred by processing the reads aligned
against the reference genome (hg19). The candidate breaks are
identified and supporting read depths are normalized to the
reference and corrected for several systematic biases to calculate
log 2 copy ratios. A built-in segmentation algorithm is used to
infer discrete copy number segments with the default threshold
value for amplification (>=0.2 log 2 ratio) and deletion events
(<=-0.25 log 2 ratio).
[0176] A structural variants (SV) section includes by deletions,
insertions, duplications, or inversions of chromosomal segments, or
rearrangements of the chromosomal locations either to other
chromosomes (interchromosomal rearrangement) or within a chromosome
(intrachromosomal rearrangement). Such events are identified in the
analysis and reported for disease association. Structural variation
refers to large-scale structural differences in the genomic DNA, as
a result of chromosomal rearrangements like deletion, duplication,
insertion, inversion, and inter- and intra-chromosomal
translocations. These structural variants are predicted using read
pairs that are mapped with unexpected separation distances or
orientation. The module estimates the parameters of the insert size
distribution and goes through reads to identify Discordant Read
Pairs (DRPs) i.e., reads with abnormal orientations or insert sizes
(either too small or too large) and clusters them to identify the
structural variants in the patient.
[0177] A gene fusion (GF) events section includes gene fusion
events. Gene fusion is a phenomenon in which the whole or parts of
two genes are juxtaposed and fused into a single chimeric gene. The
fusion can result from structural rearrangements like
translocations, inversion, amplification, or deletions. Such events
are captured in the analysis and reported under individual known
gene fusions for the indicated disease and novel gene fusion tables
under this section. A pharmacogenomics report reveals the drug
response by identifying the individual's genotype and its effect on
different medications. These findings help clinicians make informed
drug and dose administration decisions based on the patient's drug
metabolizer status, e.g., normal, intermediate, poor, or rapid
metabolizer. A drug details section outlines the drug information
for those indicated as suitable for the patient in terms of the
trade name, adverse drug reaction, pathway information, approved
annotation labels, and ongoing clinical trials. The side effects
due to the prolonged usages of the drugs are also provided as per
the PubMed evidence. A gene details section provides details for
many of the mutated genes indicated in the patient and are
summarized under this section as per the definition and functional
implications reported by NCBI. A variant of uncertain significance
(VUS) section includes mutations whose connection to diseases have
not been established and are classified as variants of uncertain
significance as per the ACMG guidelines. These mutations are thus
recommended to be reported as per the good clinical practices under
CAP guidelines.
[0178] A variant report may include a well-structured
mutation-centric report that outlines many of the variants present
in the patient classified based on the pathogenicity. Each variant
is infused with evidence derived from well-established
databases--both healthy and disease-centric. The entire report can
be explored using various filters to cater to specific user needs.
The clinician can bookmark to add any biomarker variants for the
disease which is deemed important for the disease. A regulatory
genetic elements report includes a well-structured splice, poly-A,
and deep intronic mutation report that culminates many of the
pathogenic and likely pathogenic mutations scored using the S&S
and other algorithms. Each variant is reported with its reference
and mutated base sequences, and the percentage difference scores
are calculated by the algorithms that translate into the
significance of disease causation. A splice site mutations report
includes mutations in the boundary of an exon and an intron (splice
sites) and cryptic splice sites that exist throughout the gene
including exons and introns, or changes in the activity of splicing
events by mutations in the spliceosome proteins and RNA, and leads
to a profound impact on the regulatory and accuracy of the function
of the proteins leading to pathogenesis and disease progression.
The real and cryptic splice acceptor and donor and cryptic exon
mutation events and aberrations are identified using the
Shapiro-Senapathy algorithm and prioritized based on the percentage
score difference. A promoter mutations report includes mutations in
the promoter regions and can disrupt the normal gene activation and
transcriptional initiation and alter the binding ability of
DNA-sequence motifs by various protein factors that functionally
interact with them, thus leading to disease pathogenesis. The
pathogenic promoter defects are identified based on an algorithm
adapted from the Shapiro-Senapathy algorithm suited to the length
and sequence of the promoter and other elements and prioritized on
the percentage score difference. A UTR mutations report includes
mutations in the untranslated regions and can disrupt the normal
gene translation, and thus lead to disease pathogenesis. The
pathogenic 5' UTR and 3' UTR defects are identified based on the
Shapiro-Senapathy algorithm and prioritized on the percentage score
difference. A Poly-A mutations report includes mutations in the
3'-untranslated region having the polyadenylation sites that
contributes to mRNA stability, translation control, nuclear export,
and cellular functions of growth proliferation and differentiation,
thus becoming one of the critical events that regulate diverse
molecular aspects of mRNA metabolism. Therefore, a disruption in
these sites has been known to cause diseases where the pathogenesis
has been outlined to include a varied concentration of mRNA
processing factors, RNA-binding proteins, and the global
transcriptome changes of the cellular signaling pathway. The
mutations in the 3'-UTR affecting the signals, as well as those
identified by the adaptation of the Shapiro-Senapathy algorithm,
are prioritized as pathogenic events. A DNA gene fusion report
includes DNA fusion events that are identified by locating the
candidate breaks with two or more clipped reads. The breaks
supported by at least three unique discordant reads with one of the
two pairs longer than 10 bp and matching more than 70% of the split
length is considered as a fusion event. The functional consequences
of genomic fusions are annotated with genes, fusion domains in a
strand-specific manner, and frequency of the events in disease
databases like cosmic. The known fusion event(s) that are likely to
have an association with the disease is reported along with the
novel fusion events. The reported gene fusion events are described
with the breakpoints, fusion orientation, potential targeted
therapy, and the clinical trials.
[0179] In some embodiments, the report includes an expression level
of the genes, annotated, and with the log fold change and p-value
estimating the significance level of expression. The top
significant up-regulated and down-regulated genes are
differentiated and visualized as a volcano plot with the log fold
change and p-value which refers to the analysis and interpretation
of differences in the abundance of gene transcripts within a
transcriptome. The log of fold-change is used so that changes in
both directions (up and down) appear equidistant from the center,
whereas the data points with low p-values (highly significant)
appear towards the top of the plot. It enables visual
identification of the most meaningful and statistically significant
genes. The corresponding transcripts, coverage, FPKM, and
Transcripts Per Million (TPM) levels of each identified gene are
reported. The functional enrichment analysis (Gene Ontology and
Pathway analysis) is also performed for the top significant
differentially expressed genes and based on the level of gene
expression, the affected pathways, molecular function, cellular
components, and biological processes are reported with the
significant p-value.
[0180] Other elements in the report provided in clinical reporting
step 340 may include the following. A variant statistics including
statistics and quality metrics are summarized to indicate the
chromosome-wise variant summary, mutation spectrum, amino acid
spectrum, top 50 mutated genes, ts/tv count, and distribution of
functional consequences along with the depth and quality
distribution of variants identified in the patient. An alignment
statistics is summarized to indicate the chromosome-wise
distribution of aligned sequence reads, coverage, mapped bases, and
insert-size distribution in the patient's sequence alignment map. A
raw data QC including various sequence read statistics and quality
metrics are summarized to indicate the per-base sequence content,
per-base quality scores, GC content, sequence quality, length
distribution, Kmer content, and adapter content in the patient's
sequenced reads. PGx genes including patient mutations in the
drug-metabolizing genes are plotted, and interactive graphical
interpretations are shown together on the gene structure and
sequence landscape for a comprehensive representation of many of
the possible mutation types (splice mutations, promoter mutations,
UTR, poly-A, and coding mutations). eMERGE genes including the
metabolizing functions of 82 or more pharmacogenes which are
critical for the implementation of "precision medicine" are
provided as per the study outcome of the eMERGE-PGx project. The
detailed sequence view and various genic properties of these
pharmacogenes are shown using interactive visuals. MMR genes
including the patient mutations in the Mismatch repair genes (MMR)
are plotted on the gene structure and genomic sequence map on the
chromosomal scale for many of the possible mutation types (splice
mutations, promoter mutations, poly-A, and coding mutations) for
comprehensive visualization. Many genes including the patient's
mutated genes are sorted in descending order so that the genes with
the highest number of mutations are prioritized. Many of the
possible mutations (splice mutations, promoter mutations, poly-A,
and coding mutations) in a given gene are plotted and shown
together on the gene structure and sequence landscape for an
encyclopedic view.
[0181] FIG. 4 illustrates copy number variants 400 and associated
genes 403 in a chromosome 401 for a selected disease or trait 405,
according to some embodiments. The present disclosure can diagnose
the patient genome for various genomic rearrangements that
encompass structural variants, copy number variants, and gene
fusions. These rearrangement events are visualized on the genomic
scale in an interactive graphical interpretation. The events are
also shown at the sequence level, depicting the exact points of
rearrangements.
[0182] Copy number variation (CNV) involves alterations in the
number of copies in specific regions of an individual's DNA, which
can either be amplified or deleted. The chromosome wise events 415
reported from the patient's genome can be visualized on the
chromosomal structure and the corresponding genes that are
partially and completely amplified or deleted through interactive
visualization before amplification 421 or after amplification 423.
The type of event to be selected (e.g., amplification, partial
amplification deletion, and the like) is selected via tab 411. The
chromosome to be analyzed may be selected through tab 413, and the
CNV events are selected through tab 415.
[0183] In some embodiments, a message 425 may indicate that no
complete genes have been found for the selected event in tab 415.
Gene tab 403 gives the details of genes in the genomic location
that are present at a variable copy number in comparison with a
reference genome. The copy number variants at the genomic location
refer to the part of the genome that is either amplified or deleted
at large-scale. Gene tab 403 provides information about the genes
that are duplicated or deleted due to copy number alterations. It
is thought that large-scale chromosomal rearrangements also give
rise to various genetic diseases, which can be viewed through
disease tab 405. With reliable detection of CNVs, the driver
mutations for various diseases are identified for a single subject,
helping in developing personalized medicines when integrating with
other genomic features.
[0184] A detailed result table 420 indicates the chromosome, the
start and stop nucleotide location of the CNV event, the size
(e.g., number of nucleotides involved), the chromosome arm, the arm
fraction, the chromosome fraction of the segment mean, the number
of markers, the P-value, and the size class involved in the
event.
[0185] FIG. 5 illustrates structural variants 500 and associated
genes in a chromosome, according to some embodiments. The view may
be toggled between chromosome 501, gene 503, and disease 505.
Structural variations (SV) are characterized by type 511 such as
deletions, insertions, duplications, or inversions of chromosomal
segments or rearrangements of the chromosomal locations either to
other chromosomes (interchromosomal rearrangement) or within a
chromosome (intrachromosomal rearrangement). SV events 515 reported
from the patient's genome can be visualized on the chromosomal
structure with the corresponding genes that are partially and
completely amplified or deleted.
[0186] SV events 515 are also reported based on SV containing genes
and on the basis of diseases. Gene tab 503 lists the genes present
in the genomic location where deletions, insertions, duplications,
or inversions of chromosomal segments between the chromosomes
(interchromosomal rearrangement) or within a chromosome
(intrachromosomal rearrangement) are identified in a patient. Gene
tab 503 provides information about genes affected due to structural
alterations between or within the chromosomes. Disease tab 505 aids
in providing the clinical phenotypes that are caused by structural
alterations. For example, the Philadelphia chromosome is the first
recurrent genetic alteration found to be associated with human
cancer, chronic myeloid leukemia (CML). This occurs due to
interchromosomal translocation involving the ABL gene on chromosome
9, a tyrosine kinase, and the BCR gene on chromosome 22.
[0187] A view of the chromosomes before deletion 521A and after
deletion 523A illustrates the specific portions deleted in the
nucleotide string (see positions in the chromosomes). An expanded
view before deletion 521B and after deletion 523B clearly indicates
the missing portion of the sequence. A detailed result table 520
indicates the position and orientation of the first and second
chromosome involved in this specific deletion event, and the type
and size of the deletion, a score value, and a number of reads.
[0188] FIGS. 6A-C illustrate block diagrams for methods of
processing 600A, analyzing 600B, and reporting 600C genomic data
from multiple patients in a cohort and family genomics application
of a genome sequence analysis engine, according to some
embodiments. Genomic data from multiple patients in a cohort are
extracted via the EHR interface, enabling the framework for
formulating the project plan, processing the data by parsing into
various implemented modules, and illustrating and visualizing the
results in an interactive framework.
[0189] Family genomics as disclosed herein contains
meta-information of family members where the next-generation
sequencing (NGS) based genomic family analysis can be performed
between father, mother, and child (trio), or siblings,
grandparents, and other relatives such as aunts and uncles for
multiple generations. The module facilitates testing for the
inheritance pattern of deleterious mutations that are causal for
the disease and carried across the family members. It comprises
various sections enabling the user to select the individuals from a
family and panel of genes satisfying the study parameters.
[0190] A patient group creation section provides the
meta-information of all the individuals from a family in a tabular
format facilitating the user to select the patients based on the
study design. The user is given the option to create multiple
groups within a family and multiple analyses can be performed
within a family. A gene group creation section allows the user to
select the genes or create a gene panel upon which the analysis to
be performed to derive the Zygosity or mode of inheritance of the
mutations on these disease-associated genes across the family
members. A patient and group selection section manages the created
patient groups and gene groups can be chosen as per the study
design and analysis can be launched for the Family Genomics
module.
[0191] FIG. 6A illustrates an electronic health record review
method 600A that includes: a meta-data review 602, a patient
selection 604, and a gene selection 606. The genes may be separated
into inclusion genes 608-1 and exclusion genes 608-2.
[0192] FIG. 6B illustrates an analysis launch method 600B that
includes: analyzing, reviewing, and formulating a project plan 610,
and launching the analysis 612.
[0193] FIG. 6C illustrates a processing method 600C that includes:
creating a mutation annotation file (MAF) with selected criteria
614. The MAF is a tab-delimited file format which contains the
mutation information from the VCF (Variant Call Format) files.
[0194] Step 616 includes annotating actionable mutations using the
gene explorer engine knowledge base. Step 616 includes annotating
information such as mutation pathogenicity, gene pathway, gene
mutation actionable drugs, protein domains, and splicing (site)
annotations are added to the MAF file. Step 616 also includes
annotating pathogenicity criteria based on the consequences,
implications, and in silico pathogenicity scores for the identified
SNPs and Indels.
[0195] Step 616 includes calculating a pathogenicity status for
each variant identified in the individuals of the cohort study as
per the ACMG guidelines. In some embodiments, step 616 includes
selecting at least one of the following pathogenicity criteria:
Pathogenic; Likely Pathogenic; Drug Response; Variants of Uncertain
Significance (VUS); Benign; Likely Benign. Step 616 may also
include identifying pathways, actionable drugs, and domains.
Accordingly, in some embodiments, step 616 includes collecting
information on the affected domains, pathway, and actionable drugs
and facilitates a deeper understanding of the functions of the
mutations identified in the cohort. Step 616 may include adding
this information into the MAF file with the help of the genome
analysis engine curated knowledgebase. The genome analysis engine
knowledgebase is an aggregated data collected from several publicly
available databases to provide comprehensive genomic information
and is inbuilt in the present platform.
[0196] In some embodiments, step 616 includes processing the
clinical trial cohort and annotating relevant information like
pathogenicity, pathway, actionable drug, domain indications, and
regulatory and splice information. In some embodiments, step 616
may also include the following steps:
[0197] Analyzing drug-metabolizing genes as per the recommendations
and rules outlined by Clinical Pharmacogenetics Implementation
Consortium (CPIC) guidelines for 11 genes. Furthermore, different
gene sets such as the eMERGE genes or any other gene sets that are
determined to have drug response/metabolizing associations, or the
set of many genes in the genome are also analyzed for frequently
mutated genes exhibiting associations with drug response
phenotypes, such as high, medium, or low drug efficacy or adverse
drug reactions (ADRs), and different grades and intermediates
between the high and low phenotypes.
[0198] Outlining, based on the haplotype, diplotype significance of
the mutations, drug metabolizer status (normal, intermediate, and
poor metabolizers) classification rules. Scanning each of the
individual genomic signatures against these criteria and the drugs
indicated to be "use as directed," "use with caution," and "drugs
to avoid" are assigned.
[0199] Associating genes to drugs: For many of the mutated
drug-metabolizing genes reported, the corresponding drug
categories, sub-categories, and drug members (monotherapy and
combination therapy) are reported in a tabular format. They are
presented in different sections as per the drug-metabolizing status
of normal (low risk), intermediate (medium risk), and poor
metabolizers (high risk) through a risk selection drop-down. Based
on the chosen risk criteria, the user can study a few specific
genes with the metabolizing impact as per convenience. Analyzing
the data based on the genes and mutations associated with high,
medium, and low drug efficacy and adverse drug reactions, and the
combinations such as high efficacy/low ADR, low efficacy/high ADR,
and other permutations and combinations of drug responses. Table
VIII illustrates a framework providing the status of
drug-metabolizing genes and their impact on different categories of
drugs.
TABLE-US-00008 TABLE VIII Gene Drug Association This section
provides the reported drug-metabolizing genes and their impact on
drugs along with the drug-categories. Select Risk Low Risk Show 10
entries Gene symbol Drug Category Sub-Category Search Search Search
Search CYP2C19 Drospirenone + Hormonal -- Ethinyl Contraceptives
Estradiol CYP2C19 Iguratimod Rheumatology -- CYP2C19 Leflunomide
Rheumatology -- CYP2C19 Donepezil Psychiatry Acetylcholinesterase
(AchE) inhibitor CYP2C19 Carisoprodol Pain Analgesic, Management
Anesthesiology
[0200] In some embodiments, step 616 includes annotating splice
mutations. In some embodiments, the pathogenicity for the splice
mutations is calculated by employing the S&S algorithm. The
bases ranging for the real and cryptic splice sites (acceptors and
donors) are scored using the Shapiro-Senapathy algorithm. The sites
scoring more (or less) than user-specified or inbuilt score
threshold (cut-off) within exons and introns are determined as
potential cryptic splice sites. The chromosomal positions of the
identified splicing mutations are scanned against the original
cryptic and real splice sites positions built into the present
disclosure or user-specified, and if matched, the scores are
calculated for the mutated splice sites. The original scores and
observed scores are analyzed for assigning the pathogenicity
depending on how they vary from the original sites.
[0201] Step 618 includes applying a sequence analysis algorithm
(e.g., the S&S algorithm, and the like).
[0202] Step 620 includes preparing and providing descriptive
statistics. The descriptive statistics may include a mutation
count, a mutation distribution, a mutation pathogenicity, affected
domains and pathways, and actionable drugs for selected diseases.
In some embodiments, step 620 includes measuring various features
of genes, mutations, domains, genetic elements, pathways, patients,
and other factors to estimate the probability of the factors
associated with disease and the clinical genotypes and phenotypes
distributed among the individuals in the cohort. Additionally, in
some embodiments, step 620 includes evaluating the number of
patients reported with the respective mutations and genes with
mutations. This factor provides an understanding of how the
mutations or genes with mutations are distributed among the
individuals with similar disease phenotypes. These types of
mutations might act as a driving factor for the study of phenotypes
or associated traits of the population. In some embodiments, step
620 includes evaluating unique protein domains which are commonly
affected across the patient group or cohort. This provides an
understanding of the functional part of the protein in the
associated study phenotype. The domain can be a targetable region
which can be studied in drug discovery. In some embodiments, step
620 includes evaluating the number of unique actionable drugs from
the mutations observed in individuals. This leads to an
understanding of potentially actionable variants in the group of
individuals exhibiting similar phenotypes. As the root of precision
medicine, this can initiate the experimentation of the
effectiveness of a drug that is approved for a particular phenotype
caused by a genotype (variant) for its effectiveness on a different
phenotype caused by the same genetic variant or by different
mutations causing a defect in the same gene or underlying
biochemical function. In some embodiments, step 620 includes
evaluating unique pathways affected by the commonly mutated genes.
This aids in understanding the multiple commonly affected pathways
that associate with the disease, determining the molecular
mechanisms of the disease phenotype in the group of individuals
with the underlying disease, and targeting drug development. This
platform systematically identifies the pathway(s) affected by the
specific gene mutations that may be involved in the cause of the
disease. In some embodiments, step 620 includes evaluating the
number of unique mutations in the regulatory regions: promoters, 5'
and 3' UTR, splice sites, branch sites, cryptic splice sites,
cryptic branch sites, cryptic exons, enhancers (ESEs, ISEs) and
silencers (ESSs, ISSs), and poly-A sites, in the genes across the
patient group. The mutations in these sites are implicated in a
large fraction of human diseases, and diseases in other animals,
plants, and microbial organisms. Understanding the importance of
the regulatory regions and the impact of mutations in them will
enable the identification of the molecular causes of disease in the
cohort. This will enable the development of targeted diagnosis and
treatment.
[0203] In some embodiments, step 620 includes measuring various
features to estimate the uncertainties of the factors associated
with disease and the clinical phenotypes distributed among the
individuals in the cohort. The analysis supports to evaluate the
following statistics: a number of patients reported with the
respective mutations; a number of unique protein domains which are
commonly affected; a number of unique actionable drugs for the
mutations observed in individuals; a number of unique pathways
affected by the commonly mutated genes; a number of unique
mutations in regulatory regions (promoters, splice sites, branch
sites, cryptic splice, branch points, enhancer/silencer sites, and
Poly-A sites); a number of unique mutations in the pharmacogenes is
computed to indicate the number of mutations affecting the DMG for
unique patient identifiers. Many of these statistics are determined
for a group of genes or many genes from the genome.
[0204] In some embodiments, step 620 includes preparing a
pharmacogenomic summary statistics report including an exhaustive
statistical reporting of the pharmacogenomics genes analyzed. The
genes mutated with the metabolizing status, the risk status, and
the number of patients presented with the mutation, and other
details concerning the mutations, patients, and type of genetic
elements, are reported in a tabular format. Table IX illustrates a
framework providing the statistics of the drug-metabolizing status
of the genes based on the mutations in patient cohorts.
TABLE-US-00009 TABLE IX Pharmacogenomic Summary Statistics The
module provides statistics of the drug metabolizer status for genes
associated with different drug categories in the patient cohorts.
Show 10 entries Gene symbol Metabolizer Status Priority Number of
Patients Search Search Search Search CYP2C19 Intermediate
Metabolizer Low Risk 1
[0205] Step 622 includes analysis of pharmacogenomics genes for
adverse drug reactions (ADRs) and drug efficacy.
[0206] FIG. 7 illustrates a patient risk assessment chart 700 based
on a genome analysis, according to some embodiments. Column 701
lists different patients, and columns 702 indicate a selected
drug-metabolizing gene (DMG). For many of the patients represented
vertically, a respective gene map is reported horizontally, and
those patients which carry mutations in any of the DMG are
indicated for their risk assessment as per a color-coded key to
enable a panoramic view of the pharmacogene status: 710A for high
risk (Red), 710B for medium risk (green), 710C for low risk
(Yellow), and no risk assessed 710D may also be included. In some
embodiments, genes and mutations in the different groups of
patients who have been assessed to exhibit low 710C, medium 710B,
or high 710A ADRs (side effects) are determined to be the causative
of these drug response phenotypes. Details of these mutations and
the coding elements, regulatory elements, and ncRNA genes in which
they occur and associate with the different patient groups are
provided for analysis in tabular, graphical, and sequence views,
and the tools for these analyses.
[0207] In some embodiments, risk assessment chart 700 may be
provided a clinical trial module (e.g., clinical trials module
260-7). Risk assessment chart 700 accounts for the mutations
present in the patient group concerning the metabolizing status of
affected genes. The genes that are responsible for metabolizing the
drugs are analyzed in the patient group. Depending on the impact of
mutations, the risk of a patient for the respective drugs is
classified as "High," "Medium," or "Low." The drugs are further
categorized as "Use as Directed by Physician," "Drugs to avoid,"
and "Use with caution" based on the patient's risk towards the
drugs. The data from a clinical trial cohort, and from multiple
similar trials for a drug, are accumulated and appended to a
database of drug response genes and details, which can be used in
treating individual patients.
[0208] FIG. 8 illustrates a map 800 of an mRNA gene sequence
containing an exon, and a mutation overlaid on it, provided by a
genome sequence analysis engine, according to some embodiments. A
selector 820 enables the user to select a database source. A
navigator 805 over an RNA gene 801 enables the user to view the
different components such as an Exon 811, a mutated RNA gene 813,
and an RNA exon 815. Map 800 also includes a sequence view 821 of
RNA gene 801.
[0209] An mRNA gene sequencing module processes the paired-end
sequencing data of the case sample and aligns it to the human
reference genome (hg19/GRCh37) and transcriptome. The resulting BAM
file is remapped from transcriptome locations to genomic locations
and the quality scores are recalibrated for the paired BAM file.
This allows the reads to align with the known transcript sequences,
including exon junctions and unannotated mRNAs. This dual mapping
strategy retrieves only the best alignment reads, including those
reads mapping to multiple locations (both transcriptomic and
genomic) and collapsing into single genomic coordinates. The fusion
genes are identified based on discordant read pairs (reads mapping
to different protein-coding genes) and junction spanning reads
(reads mapping to the exon-exon junctions) from RNA sequence data,
and filter out the fusion candidates observed in normal controls
lookup, and the fusion candidates with highly similar patterns in
sequence (BlastN e-value .ltoreq.0.001). The remaining fusion
events are annotated using an in-house developed ensemble-based
SQLite database to provide the frame, affected domains, and the
estimated oncogenic potential of newly discovered gene fusions.
This module uses several hallmark features and utilizes a bayesian
classifier to provide the probability of a given gene fusion being
a driver mutation. The identified fusion events are parsed against
the manually curated disease fusion repository and visualized along
with the transcript and coverage details. The mRNA gene sequencing
may also be combined with gene fusion, as disclosed herein.
[0210] In some embodiments, a genome analysis engine as disclosed
herein (e.g., genome analysis engine 255) may include RNA
sequencing to quantify the expression levels of alternatively
spliced genes and the identification of differentially regulated
isoforms or exons across samples. The RNA isoform expression
quantification uses Bayesian inference to compute the probability
of reads originated from a particular isoform and enables a more
extensive and accurate analysis of alternative splicing, at either
the exon or isoform level. The isoforms are commonly referred to as
different forms of a gene that perform similar or different
functions. They often play a distinct role in cancer-related
hemostatic and non-hemostatic pathophysiologic processes, such as
thrombosis, angiogenesis, tumor growth, and metastasis, and act as
cancer biomarkers in predicting drug response. The reads from
either the single or paired-end RNA sequencing data are aligned to
the human reference genome (hg19) and pre-computed human
alternative splice junctions. The insert length distribution and
standard deviation are calculated for the paired-end sequencing
data. The aligned reads are then mapped to isoforms and represented
as binary matrices corresponding to the compatible isoforms. By
computing the probability distribution (psi) from the matrix over
the given reads, the exon/isoform expression levels (psi) are
calculated along with the confidence intervals using Bayes rule.
Bayes rule states that this distribution is proportional to the
product of our expectation about the value (delta psi) and the
likelihood of observing the reads given psi. Thus, the significance
and magnitude of changes (isoforms) are computed using Bayes rule.
The set of events that meet the Bayes rule may be displayed
graphically as differentially expressed exons/isoforms.
[0211] Using mRNA sequencing, a genome analysis engine as disclosed
herein may perform exon-centric analysis: Accordingly, the genome
analysis engine estimates expression levels of exons, and
determines the alternative splicing at the level of individual
splicing events, e.g., the inclusion levels of a particular cryptic
exon, or the use of a particular alternative splice site. The
genome analysis engine may also perform isoform-centric analysis.
Accordingly, the genome analysis engine may estimate an expression
level of whole transcripts, and estimates the expression level of
each individual isoform of a gene.
[0212] The genome analysis engine may summarize the differentially
expressed isoforms and exons identified in the sample with the
following features: chromosomal coordinates of mRNA isoforms, and
the number of reads mapping to isoforms along with the confidence
interval. The Sashimi plot highlights the differentially expressed
isoforms of alternative splicing sites at 3' and 5' end, exons, and
retained introns. With the spliced alignments of reads from a
sample, a region of interest is visualized in a Sashimi plot as (i)
Representing the read densities (normalized by genomic region
coverage and length) from the alignment of exons. (ii) The splice
junction reads are drawn as arcs connecting a pair of exons, where
arc width is drawn proportional to the number of reads aligning to
the junction (or to the log of this number). The aberrant isoforms
in a patient or a cohort are reported based on mutations in many of
the genes in the genome of a patient in many of the genetic
elements including the regions of promoters, donor splice sites,
acceptor splice sites, branch point sites, exon, and intron
splicing enhancers (ESEs & ISEs) or silencers (ESSs &
ISSs), poly-A sites and regions, and the cryptic sites of many
regulatory elements, individually and in various combinations.
Accordingly, a genome sequence analysis module as disclosed herein
determines the isoforms and the mutational sources of these
isoforms across the genes and the genome, and to illustrate these
events in detailed graphical representations, tabular descriptions,
and sequence illustrations that are useful to the practicing
clinicians, and clinical and pharmaceutical researchers.
[0213] FIGS. 9A-C illustrate workflow chart 900A and displays 900B
and 900C, respectively, of a pathogenome module (e.g., pathogenome
module 260-10) in a genome sequence analysis engine, according to
some embodiments. Workflow chart 900A is for a pathogenome 960
procedure that includes patient genomics 962, cohort genomics 964,
family genomics 966, and clinical trial genomics 968.
[0214] FIG. 9A illustrates workflow chart 900A, wherein patient
genomics 962 includes tracking categories 912, such as: disease
track 914, which displays pathogenicity, mutational consequence in
the genetic elements of disease associated genes with complete
statistical details. All variants track 916 displays pathogenicity
and consequence for gene variants. Secondary findings track 918
displays mutations identified in ACMG59, industrial panels, and GE
disease panels. Pharmacogenomics track 920 displays
drug-metabolizing genes, as per CPIC guidelines and 82
pharmacogenes from eMERGE database. Rearrangements track 922
displays copy number variants, structural variants, and gene fusion
events. Gene family track 924 displays mutations from gene
families. Immunotherapy track 926 displays the status of
immunotherapy biomarkers (e.g., TMB and MSI). In some embodiments,
patient genomics 962 is further divided into different modules
including EHR, Launch, Report, PGx, TMB/MSI, Rearrangement,
Pathogene, and Pathogenome, each of which are subdivided into
additional modules. The workflow and descriptions of each platform
and module are described below.
[0215] Cohort genomics 964, family genomics 966, and clinical trial
genomics 968 may include the following steps:
[0216] Step 902 includes determining elements of frequency plots,
e.g., by receiving user options to enable features: study name,
SNVs, promoters, Poly-A sites, splice sites, disease panels,
mutational consequences, domains, pathways, and group excluded by
(for refining the analysis).
[0217] Step 904 performs mutation frequency plots illustrating the
mutation profiles.
[0218] Step 906 includes preparing a cohort pathogenome frequency
plot. In some embodiments, step 906 includes displaying frequency
plots for different types of genomic alterations (e.g., CDS,
promoters, Poly-A, RNAs, branch points, and splice sites) from the
patient group.
[0219] Step 908 includes preparing a pathogene frequency plot and
displaying gene structure and base position with the domains,
mutations, and frequency details.
[0220] Step 910 includes providing an individual patient
pathogenome that displays mutations from ACMG, PGx, eMERGE, and MMR
genes in the genomic scale, enabling the clinician and researcher
to customize the tracks and helps to carry out various
patient-centric analysis.
[0221] Step 912 includes preparing a family pathogenome that
displays the mutations across the family members with zygosity,
inheritance patterns, and other mutation details. In some
embodiments, step 912 includes displaying the differences in
mutation between father, mother, affected child, and additional
family members including the siblings of the child, father, and
mother as well as grandparents and great grandparents of the
child.
[0222] FIG. 9B illustrates display 900B including statistics of
variants identified in the individual under each pathogenicity
criteria: all variants 932, pathogenic 934, likely pathogenic 936,
drug response 938, VUS 940, and others 942. Display 900B provides a
pathogenicity plot to visualize the pathogenicity of the mutations
for the selected patient. The gene name, pathogenicity, and the
number of mutations are displayed on the mouse over of the bar
plot.
[0223] Display 900B illustrates the entire nucleotide string 901,
including all the chromosomes (per user selection). Options to turn
ON/OFF the visualization for each following category include
several features, as follows. Regulatory Elements 951, which
represent the mutations in real and cryptic splice sites, real and
cryptic branch sites, enhancers and silencers, promoter regions,
UTRs, and poly-A regions. SNPs/Indels in these regions are known to
be implicated in drug response and/or disease. Also, these
mutations have the potential to alter the level of gene expression.
Pharmacogenomics 952, which represent the mutations in 11 PGx genes
and 82 eMerge pharmacogenes identified in the individual. Secondary
Findings 953, which displays the mutations reported in the ACMG59
gene panel and GE disease panels that are outside of the disease of
the patient. Rearrangements 954, which represent the copy numbers
(amplification and deletion), structural chromosomal aberrations
(inversions, translocations, insertions, and deletions), and
intergenic fusions identified in the individual. Gene family 955, a
schematic representation of many of the variants in a patient's
genome in one view, overlaid against each of the 23 chromosomes
juxtaposed consecutively. The location of many of the .about.20,000
human genes and interspersed elements are represented to scale on
the line representing the length of the human genome, and against
the chromosomes consecutively represented to scale. Different
categories of genetic and genomic features are depicted as tracks
to represent the genome-wide distribution of these elements. This
view thus includes the visualization of the patient's variants in
many of the genes in the human genome, and the diseases and other
conditions that may be associated with these genes, elements,
variants, and mutations. Immunotherapy 956, which represents the
status of immunotherapy markers of the individual and status of MMR
genes shown as a track with pathogenicity of mutations reported in
the individual. In the All category 957, all chromosomes are
included in display 900B.
[0224] FIG. 9C includes display 900C with a pathogenome
illustration showing the pathogenicity of genes in a patient's
genome. In some embodiments, display 900C may enable the user to
visualize and analyze multiple tracks for different modules
implemented in the present disclosure. It facilitates the
understanding of the key features and distribution of the patient's
variants and mutations from particular genes in a genome. Each of
the tracks is in the form of a bar-plot representing the different
kinds of variants within a gene. Each track is named and shown with
the legends on the mouse over. On clicking the legends, a
statistical and colorful visualization appears in a separate pop-up
showing complete details of the specific tracks like the gene
distribution and pathogenicity distribution with the table
containing the details of the reported genes in the track. Based on
the pathogenicity categories of the mutation present, the bars in
each track are color-coded. The following are the tracks which can
be visualized in this section.
[0225] In some embodiments, a disease track displays the
pathogenicity, mutational consequence, and mutations identified in
the CDS. The pathogenicity track plots many of the categories of
the disease-associated genes with complete statistical details. The
consequence and CDS track bar plots representing the mutations are
colored based on the following consequences: Start retained
Variant; Start lost; Synonymous (silent) Variant; Missense Variant;
Inframe Insertion; Inframe Deletion; Stop gained; Frameshift
Variant; Coding Sequence Variant; Stop lost; Stop retained Variant;
Incomplete terminal codon Variant; Protein altering Variant.
[0226] Display 900C may support many variants, and display the
pathogenicity 980, and consequence of many genes reported in the
patient. Pathogenicity track plots 981, 983, 985, and 987
illustrate many of the categories of the disease-associated genes
with complete statistical details. The type of mutations observed
in various regions of the whole genome can be tracked from here.
The SNP-indel 991, impact 993, and other database toggle options
995 are shown, to view the mutations falling under any of these
categories.
[0227] The genes and mutations reported in the patient are scanned
against various panels like ACMG59, industrial panels, and GE
disease panels and plot them on the genomic scale. This track
indicates the mutations that are most likely to be associated with
that individual's set of signs and symptoms and thus provides a
medical benefit by preventing or better managing health conditions.
It also depicts the genes that indicate the potential diseases that
the patient may not currently exhibit, but may express in the
future. The variants identified in these genes would indicate the
"Secondary Findings" in the particular patient. The bar plot
representing the mutations in these genes are color-coded to
explain the consequences of variations and mutations in the genetic
sequences from the patient.
[0228] FIG. 10 illustrates a chart 1000 with mutation distributions
for genes 110A having pharmacologic impact, according to some
embodiments. Chart 1000 is a statistical representation of
mutations occurring in pharmacogenes based on pathogenicity 101B. A
pharmacogenomics table 1020 aids in visualizing the variants
identified in 11 drug-metabolizing genes as per the recommendations
of the Clinical Pharmacogenetics Implementation Consortium (CPIC)
guidelines and the set of 82 pharmacogenes from the eMERGE-PGx
project. This section also helps in educating the clinician and the
researcher about the functional outcomes of the mutations and their
impact on drug metabolism, which would indicate the patient's
response to various drugs.
[0229] FIG. 11 illustrates tracks for rearrangements 1100 in the
genomic structure such as copy number variants (CNV) 1110,
structural variants (SV) 1120, and gene fusions (GF) 1130 provided
by a genome sequence analysis engine, according to some
embodiments. Rearrangements 1100 provide a visualization of the
tracks for copy number variants, structural variants, and gene
fusions identified in the patient's genome, depicted on a genomic
scale. This track is displayed in the form of tiles instead of the
default bar-plot, where on mouse over the type of event and
position information can be studied. This section aids in
visualizing the different types of intergenic fusions that occur in
the individuals. The molecular details of gene fusions are
differentiated and color-coded along with the original genes and
orientation details. This section also aids in visualizing
different types of structural variants reported in the individuals.
The types of structural variants are differentiated and color-coded
and provided with the chromosomal position details. This
visualization can assist in understanding the genetic and genomic
changes due to CNVs, which can be predictive of disease prognosis
and therapeutics. Many of these events are illustrated in detail in
tabular, graphical, and sequence views, with easy guides to each of
the different events and molecular details.
[0230] FIG. 12 illustrates an immunotherapy track 1200 including a
tumor mutation burden (TMB) indicator 1210 and a microsatellite
instability status (MSI) indicator 1220, according to some
embodiments. Immunotherapy track 1200 aids in assessing the status
of immunotherapy biomarkers (e.g., TMB indicator 1210 and MSI
indicator 1220) in the individual. Immunotherapy track 1200
provides insights into the context of cancer immunotherapy
biomarkers in each individual's genetic makeup, their behavior, and
interactions with the immune system. This analysis aids the
clinicians to determine if the individual can benefit from
immunotherapies. This track also aids in visualizing the mutations
in the Mismatch Repair (MMR) genes depicted on the genomic scale.
Mismatch repair is a process that facilitates the identification
and repairing of erroneous changes in the genome. The genes
involved in this biological process are said to have the utmost
clinical significance, as a small change in this set of genes can
lead to the accumulation of mutations in many genes and adverse
disease conditions. This track helps in understanding the functions
of the mutations identified in the MMR genes, and their adverse
outcomes, within an immuno-therapy analysis. A status track of
mismatch repair (MMR) genes including immune biomarkers, according
to some embodiments may help track the status of MMR genes depicted
on a genomic scale and provide the status of immune biomarkers (TMB
and MSI).
[0231] FIGS. 13A-D illustrate a customizing framework 1300A
configured to provide a cohort pathogenome frequency plot 1300B, a
pathogene frequency plot 1300C, and an individual pathogenome 1300D
(hereinafter, collectively referred to as "frequency plots 1300"),
according to some embodiments.
[0232] FIG. 13A includes framework 1300A, a study cohort name 1301A
lists the patient datasets created for the analysis, for which
genomic data visualization can be employed. Study cohort name 1301A
selects lists of many of the patients involved in the selected
module (e.g., cohort genomics module 260-6, or clinical trial
module 260-7), and allows the user to select the patient of
interest for viewing the Pathogenome of that patient. The patient
selection enables the clinician and researcher to customize the
tracks displayed in this section and thus helps to carry out
various patient-centric analysis as the user desires in an
interactive manner. The patient selection feature also helps to
visualize the mutated genes identified in the selected patient,
measured in a genomic scale. The bar plots displayed in this
section are color-coded based on the pathogenicity and mutational
consequences reported in the patient.
[0233] An SNV 1303 lists the genes identified with single
nucleotide polymorphisms from the patient group. A promoter 1305
illustrates the genes identified with variants in their promoter
regions (TATA box, CAAT box, Initiator box, and GC box) and the
transcription initiation site, from the patient group. A Poly-A
site 1307 illustrates the variants identified in the Poly-A sites
from the patient group. A splice site 1309 lists the variants
identified in the various splice sites (Real splice acceptor, Real
splice donor, Cryptic splice acceptor, and Cryptic splice donor)
from the patient group. A select by disease feature 1311 shows the
genetic alterations identified in the genes associated with the
selected disease panels. The feature integrates many of the
oncology and inherited disorders; upon selection, the patient
samples are scanned against the genes associated with these disease
panels. This approach allows the clinicians and investigators to go
beyond analyzing simple relationships and creating the potential to
reveal the less obvious and indirect molecular causes of many
diseases. A select by consequence feature 1312 illustrates the
genomic data based on the mutation consequences identified in the
patient group. In some embodiments, customizing framework 1300A
also provides a consequence plot feature to visualize the
consequences of the mutations with respect to the chromosomal
positions, as selected by feature 1312. The gene name, consequence,
number of patients, and the number of mutations are displayed on
the mouse over of the bar plot. A select by domain feature 1313
illustrates the genomic data based on the affected protein domain
identified in the patient group. A select by pathway feature 1314
illustrates the genomic data based on the impaired molecular
pathways identified in the patient group. A select by family
feature 1315 allows the user to select subjects that belong to the
same family or cohort. A "group by" feature 1316 assists the
researchers in visualizing the genomic data based on two different
approaches: the number of mutations and the number of patients. An
excluded by (%) feature 1317 helps to visualize the frequency plot
above the cut-off value selected based on either a number of
mutations or patients to refine the results. The cut-off value
ranges, for example, from 1-10%.
[0234] FIG. 13B includes cohort pathogenome frequency plot 1300B
that illustrates top mutated genes (which can be grouped by either
number of patients or number of mutations 1322) in a chromosome
1301B reported in a cohort. Chromosome 1301B is selected from a
genome list 130B. On clicking stacked gene bars 1310, the related
information like number of affected patients with affected domains,
pathways, and related drug information are shown in the appropriate
table. A color code 1320 for the stacked gene bars illustrates the
different mutations found in a given gene in chromosome 1311 (e.g.,
SNV, Real Acceptor Splice site, Real Donor Splice Site, Cryptic
Acceptor splice Site, Promoter TATA Box, Promoter CAAT Box,
Promoter Initiator box, Promoter GC Box, and the like). The
ordinate (Y-axis in cohort pathogenome frequency plot 1300B)
indicates the number of individuals in the cohort having the
associated mutated gene, wherein the mutation is indicated by color
code 1320 and the gene is specified by its location in chromosome
130B (X-axis position). Cohort pathogenome frequency plot 1300B may
include a box 1321 to hide/show genetic elements in the frequency
chart.
[0235] Cohort pathogenome frequency plot 1300B facilitates the
visualization of a frequency plot of every gene from the whole
genome that is mutated across the cohort of patients, grouped by
either number of patients or a number of mutations. This
visualization technique is integrated with the identification and
characterization of the different types of genomic alterations
(e.g., SNVs, Promoters, Poly-A, and Splice sites) for every gene
from the patient group. This visualization framework displays the
mutated gene details like gene name, gene ID, start and end
position of a gene, chromosome, and the mutation category on mouse
over. The other visualization approaches employed in this
pathogenome module are customized based on the selection of
mutations in different features from a gene, such as real donor,
cryptic acceptor, promoter, and many other coding and regulatory
elements, from this section. This section thus opens unprecedented
possibilities for understanding the characteristics and behavior of
the molecular details in large patient groups, which aids in
understanding and determining the causality of disease or drug
response phenotypes. The whole genome is shown in the form of a
chromosomal ideogram 130B of the human genome and shown in a
horizontal view, where chromosomes are placed horizontally from
chromosome 1 to 22, and the sex chromosomes. This feature is
facilitated with selective focus on visualizing a particular
chromosome and many of the genes with the identified mutations
within the chromosome. Chromosomal ideogram 130B displays the
chromosomes linked together end to end, to illustrate the whole
genome in a horizontal view. Many of the genes and mutations are
depicted on the genome-scale for visualization and analysis. In
addition, selecting a specific chromosome will expand it and
display many of the mutated genes present in that chromosome for a
particular patient, or from the group of patients from Family,
Cohort, or Clinical Trials, for a finer and more detailed
analysis.
[0236] FIG. 13C includes pathogene frequency plot 1300C results,
obtained by clicking on a chromosome in genome 1301A to illustrate
a defect gene within cohort pathogenome frequency plot 1300B in
detail.
[0237] Pathogene frequency plot 1300C facilitates the visualization
of particular genes with mutations across the cohort of patients,
with several details including gene length with the base position
in a horizontal view, and the frequency of different types of
mutations across the gene depicting them on the different
regulatory and coding elements, including the promoters,
transcription initiator, exons, introns, splice junctions, 5' and
3' UTRs, and poly-A regions. The exons are displayed on the gene
view along with the encoded domains below the exons, and the
mutations identified from the patient group as a frequency plot
with the position of the mutation on the X-axis and the number of
patients with the mutation on the Y-axis. The mutations are
represented in the form of a needle which displays the mutation
details like mutation position, exon number, amino acid change, the
pathogenicity of the mutation, encoded domain id, and the number of
patients and the mutations in popups on mouse over. Thus, this
section empowers the clinicians and researchers with a deeper
understanding of the identified gene, patient, domain, and several
mutation details. The frequency plot can be initiated by selecting
from the Pathogenome frequency plot, or from dropdowns on the
Pathogene frequency plot window.
[0238] Frequency plot 1300C illustrates the positions 1331 and
frequency 1312 of mutations 1341 reported in a selected gene 1335.
Pathogenome frequency plot 1300C is a more finely granular view of
the frequency plot 1300B, wherein the X-axis positions 1331
includes a fewer nucleotide basis. Accordingly, while frequency
plot 1300B illustrates an entire chromosome, frequency plot 1300C
indicates the same frequency values 1312 (Y-axis) for gene 1335, in
either a compact view 1351 or an expanded view 1352.
[0239] In some embodiments, the gene is marked on the exact
position 1331 of the selected chromosome to scale, indicating the
number of patients with the specific mutation or specific genetic
element types such as CDS or any of the regulatory elements. The
gene and mutation details are displayed on mouse over. An ideogram
of the selected chromosome is displayed in a compact view 1351 with
the cytogenetic locations based on the bands and their chromosomal
arm. An expanded view 1352 is displayed by clicking on the specific
mutation position. Pathogene frequency plot 1300C enables the genes
organized into gene families to be specifically displayed on the
pathogenome view, and the genes that are mutated. Additional
related information, like the number of affected patients (e.g.,
frequency 1322) with affected domains, pathways, and related drug
information is also provided for the selected gene, or gene groups
such as containing a specific domain, or genes that belong to a
particular gene group such as a gene family or cell structure
genes. Furthermore, each chromosome is shown in expanded view 1352
with many of the possibilities of illustrations and analytical
capabilities described above.
[0240] FIG. 13D includes pathogenome 1300D having several rows
1331D illustrating, along the entire genome of a selected patient
1301D, the chromosome, the consequences (color-coded as per a chart
1321D) of associated mutations, the pathogenicity of the mutation
(color-coded green, for light or no pathogenicity, and red, dark
red, or purple for high pathogenicity), ACMG, PGx, eMERGE, and MMR,
among others. The ACMG, PGx, eMERGE, and MMR tracks indicate
mutations identified in these various gene panels for the selected
patient of the cohort. Chart 1321D may indicate the type of
mutation (e.g., silent, missense, intron, splice site, nonsense,
5'UTR, 3'UTR, splice region, frameshift deletion, 5 flank,
translation start site, nonstop, and the like).
[0241] FIG. 14 illustrates portions of a family pathogenome 1400,
according to some embodiments. Family pathogenome 1400 facilitates
the visualization and analysis of mutated genes identified across
the family members. Family pathogenome 1400 is shown for father,
mother, and child separately marked with the identified mutations.
Family pathogenome 1400 aids in explaining genetic conditions by
looking at differences in mutations between father, mother, and the
affected child. This feature allows for the depiction of the
pathogenome of additional family members including the siblings of
the child, father, and mother as well as aunts, uncles,
grandparents, and great grandparents of the child, and for the
analysis of the relationship of each mutation and mutated gene
across the family members, in addition to the zygosity and
inheritance patterns. In some embodiments, family pathogenome 1400
illustrates the genes and mutations from the child and parents in a
family genomics sub-module of a cohort genomics module (cf. cohort
genomics module 260-6), depicting the consequences, inheritance,
and pathogenicity for the identified mutations on the genomic
scale.
[0242] The consequences and pathogenicity are shown on the genomic
scale for the identified mutations. The module also aids the
clinician and the researcher in identifying the commonly mutated
genes and the inheritance patterns among the parents and the child,
and in turn, facilitates in the interpretation of this analysis.
Furthermore, it enables the illustration and analysis of genes and
mutations from the child, siblings, parents, and uncles, aunts, and
grandparents, and great grandparents on both sides. Pathogenome
1400 displays a pathogenome visualization plot for a patient
selected from a group of patients in a cohort module or a clinical
trials module (cf. cohort genomics module 260-6 and clinical trial
module 260-7). A pathogenicity tab 1411, a disease tab 1413, and a
category tab 1415 (e.g., autosomal dominant, autosomal recessive,
denovo) provide more options for the user.
[0243] Pathogenome 1400 includes several rows 1431 illustrating,
along the entire genome, the chromosome, and the parent
(mother/father)-child mutation landscape. A color code 1421
indicates which mutation in the child comes from either parent
(mother/father). A chromosomal ideogram 1401 illustrates the
chromosomes in the human genome, laid horizontally, in
sequence.
[0244] FIG. 15 illustrates a diagram 1500 of a pathogene module (cf
pathogene module 260-11) in a genome sequence analysis engine,
according to some embodiments. Pathogenome module may include or
interact with a patient genomics module 1560-1, a cohort genomics
module 1560-6, a family genomics sub-module 1560-2, and a clinical
trial genomics module 1560-7 (cf, cohort genomics module 260-6 and
clinical trial module 260-7).
[0245] Select by Patient 1510A: This feature lists many of the
patients present in the selected module. This helps in carrying out
individual patient-centric analysis. Select by Gene 1510B: This
feature lists many of the mutated genes identified in the selected
patient. The feature applies to the visualization and analysis of
Gene, Protein, UTR, Promoter, Poly-A, Real and Cryptic splice,
branch, promoter, etc., sites, Gene Fusion, CNV, and many other
genetic elements and Structural Variants, each provided under
specialized modules. By selecting by gene, the pathogene module
plots patient's mutations and known mutations from dbSNP, Clinvar,
and COSMIC databases, among others. In some embodiments, the
pathogene module categorized the mutations into clinical
significance, molecular consequence, variation type, and
pathogenicity or deleteriousness.
[0246] Gene 1521 displays the mutations. In some embodiments, gene
1521 includes a Sequence view that displays a gene sequence with
genetic elements and mutations. This section assists in visualizing
the mutations identified in the selected gene along with
annotations from publicly available databases. Information related
to the selected gene such as chromosome, Gene ID, symbol, length,
and strand is displayed in this section. The gene ontology
(molecular function, biological process, subcellular location) and
the associated disease phenotype are provided to get complete
knowledge on the characteristics of a gene before acquiring its
role in regulating disease progression. This gene visualization
module is similar for many modules (cf. modules 260).
[0247] Protein 1522 includes a protein overview that displays the
domains and mutations in a protein. Protein 1522 also includes seed
and full alignment views that display six signatures: 20 colors,
positive and negative, hydro, cryptic splice, alternative splice,
and whole protein signature. Protein 1522 also displays cryptic
splice sites and the number of samples for each variant from a
database (e.g., COSMIC) including domain position, to form a
variant density plot.
[0248] Promoter 1523 includes a gene plot that displays the
transcript with mutations in promoter boxes. In some embodiments,
promoter 1523 displays a sequence view with the sequence of
promoter boxes including mutations.
[0249] Splice/Cryptic/Branch point 1524 displays a compact view
with real/cryptic/branch points on transcript from one or more
exons and introns. In some embodiments, splice/cryptic/branch point
1524 displays an expanded view that enables detailed analysis in
splice sites and exons. In some embodiments, splice/cryptic/branch
point 1524 displays a sequence view with splice elements and
mutations.
[0250] Poly-A 1525 includes a gene plot that displays a transcript
with mutations in the Poly-A region. In some embodiments, Poly-A
1525 displays a sequence view of the Poly-A region with
mutations.
[0251] Gene Fusion 1526 includes a gene plot that displays two
fusing genes and fused chimeric genes. In some embodiments, gene
fusion 1526 displays an expanded view of the complete fusion event.
In some embodiments, gene fusion 1526 displays a sequence view
including sequence details of the fusion event.
[0252] Chromosomal rearrangements 1527 displays a large-scale
structural alteration and copy number alteration.
[0253] Evidence 1528 displays in a compact view a gene structure
and mutations. In some embodiments, evidence 1528 includes an
expanded view that displays sequence reads with mutations,
statistics, zygosity, and mutation annotation format (MAF) file
details.
[0254] FIG. 16A illustrates the structure of an entire gene 1600A
in a compact view 1651 and in an expanded view 1652 provided by a
genome sequence analysis engine, according to some embodiments. A
select gene tab 1611 enables the user to select a gene. A general
information table 1610 indicates the chromosome, a gene ID, a gene
symbol, a gene length, and a strand for the selected gene. A
mutation toggle 1603 and a view sequence toggle 1605 in a field
1620 enable the view of mutations and sequence for the user,
respectively. This section displays the structure of the entire
transcript including the promoter, coding, and non-coding regions,
splicing sites, Poly-A sites, along with the mutations 1641
identified in the gene from the patient. The details are displayed
on the mouse over of the mutation needles 1631 which are overlaid
on the transcript in compact and expanded views, including a
nucleotide number axis 1601A.
[0255] FIG. 16B illustrates a nucleotide sequence view 1600B of a
genome, according to some embodiments. Sequence view 1600B displays
the nucleotide sequence 1601B of the selected gene and shows the
details of mutations 1641 identified in the gene in the patient on
a user mouse over. In some embodiments, the specific location of a
nucleotide (e.g., base number), may be indicated on a user mouse
over of the nucleotide. A menu 1620 lists display options for the
user, such as "real acceptor," "real donor," "CDS view" toggle, and
"mutation" view toggle. A table 1630 indicates the graphical codes
for the different elements illustrated, e.g., 5' and 3' UTRs,
coding exons, alternative exons, start codons, stop codons, and
mutations.
[0256] FIG. 17 illustrates a visual plot 1700 of genome mutations,
according to some embodiments. Visual plot 1700 illustrates
mutations found in the protein-coding regions of the genome and
displaying the respective domains predicted by the selected
algorithm. A gene selection tab 1711 enables the user to access a
selected gene. A general information field 1710 includes a
chromosome number, a gene ID, a gene symbol, a gene length (e.g.,
number of nucleotide bases), and the strand. The user may select in
tab 1712 among several options such as protein overview, cryptic
splice sites, and variant density.
[0257] A protein overview 1715 visualizes mutations 1741 which
occur in the coding region of the genome, and their effect on the
translated protein, per request by the user via a toggle 1703. A
field 1720 lets the user select one or more database sources for
searching mutations 1741 (e.g., dbSNP, ClinVar, COSMIC, Patient,
and the like).
[0258] A compact view 1751 and an expanded view 1752 are available
to the user. In some embodiments, coding exons of a selected
transcript are depicted as grey ovals 1701A (compact view 1751) and
1701B (expanded view 1752). Structural regions 1760-1 and 1760-2
(hereinafter, collectively referred to as "structural regions
1760") of the encoded protein are predicted by an algorithm (cf
algorithm 250). Besides plotting patient mutations on the gene, the
protein structure and sequence, the known mutations from the
different databases such as dbSNP, ClinVar, and COSMIC depicting
the CDS, and many of the regulatory elements are represented and
categorized into clinical significance, molecular consequence, and
variation type, and pathogenicity based on the SIFT and/or PolyPhen
scores.
[0259] Protein overview 1715 may include two different alignments
between an amino acid sequence in the protein and the nucleotide
string in the coding gene. A seed alignment includes a set of
curated amino acids that are matched to their corresponding
nucleotide sequence in the genome. The seed alignment tends to have
lesser amino acids compared with full alignment. A full alignment
contains the set of amino acids produced by the algorithm or
database selected (e.g., Pfam using Hidden-Markov models, and the
like). The full alignment tends to have more amino acids when
compared with the seed alignment. Both types of alignment may be
visualized in different genomic signatures, per user selection,
namely: 20 colors, Positive-Negative, Hydro, Cryptic splice,
Alternative splice, and Whole protein signature. The 20 colors
signature displays the amino acids of the selected domain in 20
different colors along with its positions and sequence. The hydro
signature displays the amino acids of the selected domain based on
the hydropathy of the individual amino acid. In some embodiments,
the hydropathy signature may include a heat map like legend
indicating color code and the hydropathy values for each of the 20
amino acids.
[0260] FIG. 18 illustrates a protein signature 1800 in a genome
sequence analysis engine, according to some embodiments. When the
image of a protein domain is clicked (e.g., structural regions 1760
in visual plot 1700), the variable amino acid sequence plot of the
protein domain from the protein is displayed with color codes for
the amino acids, which may be extracted from the PFAM multiple
sequence alignment of the protein domain, or some other database.
Protein signature 1800 displays the true amino acid 1805 (including
the three basis coding the amino acid 1807) in any given position
1801C, and a list of allowed amino acids 1851 in one half (e.g., in
green at the bottom) and the non-allowed amino acids 1852 above (in
red). Mutations 1841 in an amino acid are shown in red if it is in
the allowed or non-allowed portion. Protein signature 1800 allows
the user to see if the mutation is truly deleterious 1852 or benign
1851. A window 1853 with mutation details may pop for display to
the user upon mouse over of any of mutations 1841.
[0261] FIGS. 19A-C illustrate gene fusion mutation events in charts
1900A, 1900B, and 1900C (hereinafter, collectively referred to as
"gene fusion charts 1900") leading to a chimeric gene, according to
some embodiments for a selected gene fusion 1911. Gene fusion
charts 1900 provide visualization of gene fusions, which are
characterized as a phenomenon in which the whole or parts of two
genes are juxtaposed and fused into a single chimeric gene. The
fusion can result from different types of chromosomal aberrations,
like translocations, inversion, amplification, or deletions.
[0262] FIG. 19A illustrates fusion chart 1900A including a gene
plot for the visualization of the genes 1901-1 (gene A) and gene
1901-2 (gene B) involved in the fusion phenomenon and followed by
the fused chimeric gene. A field 1910 indicates the fusion pair,
the names or coordinates for gene A and gene B, the event and
frame. The breakpoints 1941-1 and 1941-2 (hereinafter, collectively
referred to as "breakpoints 1941") in both genes 1901 are
highlighted (red) and on mouse over, the position of the breakpoint
is displayed. The fused point on chimeric gene 1901-3 is
highlighted with position and other details.
[0263] Fusion chart 1900A illustrates a nucleotide sequence 1901-1
from gene A that fuses with a nucleotide sequence 1901-2 from gene
B to form a nucleotide sequence 1901-3 in the chimeric gene.
[0264] FIG. 19B illustrates fusion chart 1900B including an
expanded view of fused genes in sequence 1901-3 from genes 1901-1
and 1901-2. A fusion point 1921 (possible out-frame) is
illustrated. The details of fusion point 1921 may be displayed on a
mouse over.
[0265] FIGS. 19C-1, 19C-2 and 19C-3 illustrate a fusion chart 1900C
including a sequence view of the fusion event. This subsection aids
in visualizing the nucleotide and protein sequences of the normal
genes and fused chimeric genes. The sequences are marked with 5'
and 3' UTRs, coding exons, alternative exons, start codons, and
stop codons to visualize the nucleotide and amino acid sequences of
the normal genes and fused chimeric gene. Fusion chart 1900C
illustrates gene A sequence 1903-1, gene B sequence 1903-2, and
fused gene sequence 1903-3 (hereinafter, collectively referred to
as "nucleotide sequences 1903"). In nucleotide sequences 1903, a
mouse over each nucleotide causes the display to indicate the
position of the nucleotide in the genome. Fusion chart 1900C
indicates real acceptor sites, real donor sites, alternative exons
start codons, and stop codons by toggling a color indicator 1930
`on` or `off.`
[0266] FIGS. 20A-G illustrate mutation visualizations 2000A, 2000B,
2000C, 2000D, 2000E, 2000F, and 2000G (hereinafter, collectively
referred to as "visualizations 2000") provided by a genome sequence
analysis engine, according to some embodiments. Visualizations 2000
enable the user to overlay known mutations on a gene transcript and
view their clinical impacts. To do this, the user may select a
mutation database source (e.g., dbSNP, ClinVar, COSMIC, or
Patient), then select a "mutation impact" category to filter the
types of mutations. When the clinical significance category is
selected, the user may select a mutation score source (SIFT or
PolyPhen) and view the details simply by mouse over. Visualizations
2000 may include a compact view 2051 and an expanded view 2052 of a
nucleotide sequence 2001. In compact view 2051, an entire gene
transcript is displayed, enabling quick access to cryptic sites and
exons that occur within the gene. In expanded view 2052, the user
may have a detailed analysis of cryptic sites and exons that occur
within a selected portion of the gene. Moreover, the user may
scroll to the left or right of nucleotide sequence 2001 using a
sliding bar 2053, to scan the entire gene in expanded view 2052, if
desired. By scrolling sliding bar 2053 to the left or right, the
sequence position being analyzed in expanded view 2052 will be
indicated in compact view 2051 with a grey bar.
[0267] Visualizations 2000 provide a summary and a view of the
mutations identified in the promoter region of every gene from a
particular patient, and from every patient in a cohort of patients.
The promoter is defined as the region in the upstream portion of a
gene, which helps in the initiation of transcription. Alterations
in this region are said to cause changes in the protein-coding
element (mRNA) and lead to a disease state. The potential promoter
regions are predicted by employing an adaptation of the
Shapiro-Senapathy algorithm suited to the sequence, PWM, and length
of the different promoter elements. This module aids in visualizing
the four different types of promoter boxes: TATA, GC, CAAT, and
INIT (Initiator) boxes. This promoter visualization module is
similar for four different objectives (comprehensive, cohort, trio,
and clinical trials). Visualizations 2000 enable the clinician and
researcher to visualize the entire transcript along with the
mutations occurring in the promoter region. The different types of
promoter boxes and exons are color-coded. On mouse over of the
mutation needles, the mutation details are displayed.
[0268] Visualizations 2000 also provide a view of the promoter
sequence along with the mutations occurring in this region. Besides
plotting patient mutations, the mutations curated by databases like
ClinVar, dbSNP, and COSMIC may be plotted, which are based on the
mutation impact and clinical significance. Promoters identified in
the selected gene are tabulated below the sequence view with the
following information: the position, sequence, and the score for
the respective promoter type, and mutation details. Visualizations
2000 also provide a view of the mutations identified in the poly-A
region of every gene from a patient or group of patients. Poly-A
sites are defined as those which are present in the downstream
region of a gene and facilitate the regulation of transcription and
gene expression. Aberrations in this region are said to have
modulating effects leading to many different disease states. This
poly-A visualization module is similar for many modules (cf.
modules 260).
[0269] Visualizations 2000 also illustrate cryptic splice sites
present in the particular gene. The coding exons for the selected
transcript are depicted in the form of grey ovals, and the Pfam ID
is overlaid as colored lines. Different color codes are given to
represent different splice sites along with the scores, and they
are as follows: cryptic acceptors in red, cryptic donors in green,
and real sites in blue. It also provides a compact and expanded
visualization that enables a detailed analysis of cryptic splice
sites.
[0270] In some embodiments, visualizations 2000 may also display a
variant density: This section aids in the visualization of the
number of samples for each variant in the COSMIC database along
with the domain position. According to the number of variants
present in a position, the plot is color-coded, and on mouse over,
the mutation details are displayed. (Positions with a single
variant are depicted in red, and positions with more than one
variant are depicted as follows: two variants->blue; three
variants->green; four variants->yellow; more than four
variants->magenta.)
[0271] Visualizations 2000 provide a gene plot and a sequence view.
The gene plot aids in visualizing the entire transcript along with
the mutations occurring in the Poly-A region. The different
elements of Poly-A elements and exons are color-coded. On mouse
over of the mutation needles, the mutation details are displayed in
compact and expanded views. The sequence view facilitates
visualizing the Poly-A sequence along with the mutations occurring
in this region. Besides plotting patient mutations, the mutations
curated by databases like ClinVar, dbSNP, and COSMIC are also
plotted, which are based on the mutation impact and clinical
significance. Visualizations 2000 also provides a visual
interactive interface for the mutations identified in splice sites,
cryptic splice sites, and cryptic exons. These abnormalities are
now known to cause approximately 50% of diseases including major
cancers and non-cancer disorders. Identifying and locating the
mutations in those regulatory elements of a gene helps in
understanding the importance of those regions across the whole
genome and the occurrence of causative mutations in these regions
contributes to the majority of human diseases. The potential
cryptic splice sites identified using the Shapiro-Senapathy
algorithm are scanned for the mutations from the patient data. This
splice/cryptic visualization module is similar for many modules
(cf. modules 260).
[0272] FIG. 20A illustrates visualization 2000A including a
sequence view of an intron with the complete details of mutations
identified within the splice sites in the patient. In some
embodiments, visualizations 2000 also display evidence affirming
the existence of the identified variants in the sequence reads from
the NGS data. The coding exons are displayed on the canonical
transcript with the identified mutations. The mutations are shown
exactly in their respective position in the transcripts. The
complete details of the mutations are also given along with
supporting evidence. In some embodiments, visualizations 2000 also
display the structure of a gene in a compact and expanded view with
the identified mutations, with their evidence in sequence reads
shown in a separate view. Besides, the sequenced reads from the
case and control samples of the patient are aligned and scanned to
derive the frequency of mutated bases within these reads. In some
embodiments, visualizations 2000 include a complete color-coded
visualization of these aligned reads against the human reference
gene sequence and the frequency of alleles at the point of mutation
is provided.
[0273] In some embodiments, visualizations 2000 enable the
identification of the zygosity of a mutation and the gene with the
following features, which adds to the complete details of the gene,
variants, and associated phenotypes. In some embodiments,
visualizations 2000 includes transcript details with CDS position
and strand information, and variant details with the hgvsc codon
and amino acid syntax along with variant type and variant quality
information. In some embodiments, visualizations 2000 also display
the pathogenicity of the variant with scores generated by multiple
in-silico pathogenicity prediction algorithms. In some embodiments,
visualizations 2000 displaying the frequency of the variant in
healthy population databases (1 KG, ESP, ExAC) is also determined,
and the phenotypes sourced from public databases (HPO, MONDO)
associated with the gene and variant.
[0274] Visualization 2000A identifies mutations in the promoter
region of a selected gene from a particular patient, selected from
a cohort. A menu 2011 enables the user to select between a gene, a
protein, a gene fusion event, a splice/cryptic site, a promoter, a
poly-A site, and evidence display. Visualization 2000A illustrates
a promoter site and associated mutations 2041A over a nucleotide
string 2001. The gene transcript is displayed in a compact view
2051 and in an expanded view 2052, enabling a quick scan of the
real splice sites, cryptic sites, and exons that occur within it.
In expanded view 2052, a slider 2053 allows the user to move easily
along nucleotide string 2001. The different non-coding, partially
coding, and coding exons are displayed with cryptic splice site
acceptors, cryptic splice site donors, real splice site acceptors,
real splice site donors, and cryptic exons. Visualization 2000A
enables a quick view of real and cryptic splice sites, and cryptic
exons, and mutations 2041A that occur within them per user
selection in menu 2011, as indicated in table 2021A. Mutations
2041A are identified at the splice sites in the patient with
complete details of the mutations upon mouse over. Interactive
scaling and color coding allows the user to focus on a specific
portion of the sequence and track the different types of splice
regions in any part of the gene. Mutations 2041A may also include
mutations from different publicly available databases.
[0275] FIG. 20B illustrates mutation visualization 2000B including
TATA boxes 2020-1, CAAT boxes 2020-2, GC boxes 2020-3, initiator
boxes 2020-4, and promoter mutations 2041B within nucleotide
sequence 2001. A control box 2021B allows the user to turn display
functionalities "On/Off" such as "mutation," "sequence expand,"
"show all boxes," and the like. The user may also select a database
source (dbSNP, ClinVar, COSMIC, Patient, and the like).
[0276] The promoter is identified as the region from -200 to +1
wherein +1 is the transcription start site. Any occurrences of the
promoter boxes within this sequence that pass the selected score
threshold are displayed within tables 2030-1, 2030-2, 2030-3, and
2030-4 (hereinafter, collectively referred to as "tables 2030").
TATA box (table 2030-1), CAAT box (table 2030-2), and GC box (table
2030-3) are derived from the region between -3 and -200. The
initiator is shown at positions -2 to +6. By clicking on a sequence
within any one of tables 2030, the user may view the sequence in
the promoter sequence.
[0277] FIG. 20C illustrates mutation visualization 2000C including
an mRNA nucleotide sequence 2023 wherein the strongest poly-A
signal 2043C is AATAAA, and a mutation 2041C. A second signal
ATTAAA may function at a similar strength as poly-A signal 2043C,
and is illustrated in mutation visualization 2000C.
[0278] FIG. 20D illustrates mutation visualization 2000D including
mutations 2041D in splice sites and cryptic splice sites, which are
known to cause a large proportion of many diseases, including
cancers and other diseases. A field 2023D indicates the chromosome,
gene, gene symbol, gene length and strand of the selected gene. The
Shapiro-Senapathy algorithm, among other algorithms, can be used to
detect disease-causing mutations indicated in mutation
visualization 2000D. When a splice site or a cryptic splice site
mutation that is known to cause a disease occurs in a patient's
genome sequence, it is identified using the Shapiro-Senapathy
algorithm or some other algorithm and is displayed in mutation
visualization 2000D in the gene structure and in the gene
sequence.
[0279] Real exons and splice sites are displayed in the selected
transcript as shown in key 2021D. Any cryptic splice site and
cryptic exons that occur within the transcript are also
displayed.
[0280] FIG. 20E illustrates mutation visualization 2000E including
a sequence view 2003 of a selected intron, including real acceptor
sites, real donor sites, cryptic acceptor sites, and cryptic donor
sites (as indicated in table 2030).
[0281] FIG. 20F illustrates mutation visualization 2000F including
an evidence module to affirm the reported variants (including menu
2011). The first section shows the canonical transcript and coding
exons of the selected gene. The mutations 2041F in the selected
gene are represented in the form of red lines along with the exons
showing the respective coordinates and mutation information on
mouse over. Upon clicking the mutation of interest, a second
section of the evidence block (the nucleotide view) displays the
aligned mutations in case and control bands. Towards the left of
the nucleotide view, the color key and the nucleotide composition
for each mutation are displayed.
[0282] FIG. 20G illustrates mutation visualization 2000G, which
displays the sequenced reads from the control sample 2023G-1 and
case sample 2023G-2 of a patient, and aligns the sequence reads to
illustrate and derive the frequency of mutated bases and other
nucleotides within these reads. A menu 2021G allows the user to
select between a visualization, details, evidence, and
phenotype.
[0283] A missense single nucleotide mutation (chr4:
187,549,428C>T) affecting the protein (ENSP00000406229-2:
p.Ala1564Thr) is detected in the FAT1 gene of a subject. This
mutation is classified as unknown due to variant consequences
reviewed by ClinVar experts or having major consensus across most
widely used in-silico pathogenicity prediction tools. This variant
is not cited in PubMed clinical. The variant is not hitting any
domain and is present in the conserved region. This is not reported
in the ACMG list of incidental findings but reported in
COSMIC-Cancer Gene Census. The variant is present in the healthy
population databases such as 1000 Genomes, ExAC, or ESP with
0.256626074499 minor allele frequency.
[0284] FIG. 21 illustrates a variant density plot 2100 in a
chromosome 2101 with positive 2110 and negative 2120 strand genes,
according to some embodiments. A frequency value 2102 indicates the
number of patients or subjects having a given base 2115 (e.g., A,
T, G, C) for a given strand gene (positive or negative). The user
may select the chromosome to inspect with variant plot 2100.
Moreover, any portion of the genome can be magnified, revealing
finer structures and details, including the regulatory and splicing
patterns of the genes. Variant details such as reference and
altered base 2115, type of variant, and variant position in
chromosome 2101 are displayed within a block and are displayed on
hovering a pointer over a desired variant. Variant density plot
2100 may be associated with a selected cohort of subjects or
individuals selected by a cohort genomics module (e.g., cohort
genomics module 260-6).
[0285] FIG. 22 illustrates a distribution 2200 of mutations in the
introns 2210A and exons 2210B, representing a mutation distribution
within an exon and an intron, according to some embodiments.
Distribution 2200 indicates a frequency 2202 and an intron position
2201A or an exon position 2201B for a mutation. Frequency 2202 may
be determined based on a subject population or cohort selected by a
cohort genomics module (e.g., cohort genomics module 260-6).
[0286] FIG. 23 illustrates a strip 2300 including an overview of
diagnosis 2302, therapeutics 2304, pharmacogenomics 2306, and Rx
check 2308, according to some embodiments. Toggles 2321 enable the
user to select CDS, RSE, ncRNA, or GSV. In some embodiments, strip
2300 is configured to offer a simplified summary of a subject's
genome analysis (e.g., "a 30 second summary").
[0287] Therapeutics block 2304 enables the display of therapeutic
drugs based only on particular mutations of the genes (regardless
of whether they are pathogenic or not) when it is turned off, and
based only on the pathogenic mutations of genes, when it is turned
on. Pharmacogenomics block 2306 enables the display of a
pharmacogenomics report, where the pathogenic mutations in at least
one of the genetic elements along with the zygosity information are
considered to predict the adverse drug response phenotypes and
recommendations. Pharmacogenomics block 2306 displays a
pharmacogenomics reporting --the (*) nomenclature system--. In some
embodiments, pharmacogenomics block 2306 may include an EHR Drug
Alert. Accordingly, drugs being consumed by the patient and
reported in the patient EHR metadata are processed by the
pharmacogenomics module (e.g., pharmacogenomics module 260-5) to
predict adverse drug reactions (drug response phenotypes). These
drugs and their recommendations are reported within the EHR Drug
Alert block. In some embodiments, pharmacogenomics block 2306 may
report other drugs. Accordingly, drugs approved by FDA and other
agencies, or under investigation, having pharmacogenomics
recommendations based on the mutations in the drug metabolizing
genes in the patient are shown in a separate section that can be
accessed by clicking on the link provided.
[0288] A toggle menu 2321 enables the user to select a CDS, RSE,
ncRNA, and GSV data. A summary block 2330. Summary block 2330
includes points from the clinical report blocks such as diagnosis,
therapeutics, and pharmacogenomics is shown here for a quick
perusal by the clinician or provider. A CDS block 2350 indicates,
for the selected search from toggle menu 2321, the salient aspects
of the genome analysis. For example, CDS block 2350 may indicate,
as a diagnosis, that the patient has mutations in specific genes
(further indicating the position and nature of the mutations), and
that such mutation may be associated with certain disease (e.g., a
type of cancer, and the like). In some embodiments, summary block
2350 may indicate, in therapeutics, a recommended type of drug, or
a contraindicated drug (if available), or even if there are any
investigational drugs that could potentially benefit the patient.
Summary block 2350 may also include an immunotherapy recommendation
and clinical trials that the patient may be able to participate in,
based on therapeutics and immunotherapy recommendations.
[0289] In some embodiments, CDS block 2350 is displayed for a quick
perusal by the clinician or provider. In some embodiments, summary
block 2350 may include a report from a cohort genomics module
(e.g., cohort genomics module 260-6, including a family genomics
report), including various blocks for diagnosis, therapeutics, and
pharmacogenomics. In these blocks, details such as the frequency of
different alleles (RV, VV; R--Reference allele, V--Variant allele),
samples, and patients for the genes are provided. Included is also
a Summary block with clinically relevant information. In some
embodiments, a clinical trial module (e.g., clinical trial module
260-7) may include blocks for high efficacy and high side effects,
high efficacy and low side effects, low efficacy and high side
effects, low efficacy and low side effects, high efficacy, low
efficacy, high side effects, and low side effects are provided with
information on frequency of different alleles (RV, VV), samples,
and patients for the genes are provided.
[0290] FIG. 24 illustrates steps in a method 2400 for studying a
disease or other inherited trait using a gene explorer platform,
according to some embodiments. Each one or more of the steps in
method 2400 may be performed at least partially by a processor
executing instructions stored in a memory of a client device or a
server communicatively coupled with each other via communications
modules accessing a network, as disclosed herein (e.g., processors
212, memories 220, communications modules 218, client device 110,
and server 130). In some embodiments, at least one or more of the
steps in method 2400 may be performed by an application hosted by
the server and installed in the client device, the application
including a graphic display for illustrating the results of at
least one or more of the steps in method 2400 (e.g., application
222 and graphic payload 225). In some embodiments, method 2400 may
be at least partially performed by a genome analysis engine in the
server, the genome sequence analysis engine including a sequence
scoring tool, a mutation tool, a statistics tool, and an algorithm
tool (e.g., genome sequence analysis engine 242, sequence scoring
tool 244, mutation tool 246, statistics tool 248, and algorithm
250). Further, in some embodiments, one or more of the steps in
method 2400 may be performed by a gene discovery module, an EHR
module, a launch module, a report module, a pharmacogenomics
module, a cohort module, a clinical trial module, a TMB/MSI module,
a rearrangement module, a pathogenome module, a pathogene module,
and a dark matter module interacting with a genome analysis engine,
consistent with the present disclosure (e.g., genome analysis
engine 255 and modules 260). In some embodiments, a method
consistent with the present disclosure may include at least one of
the steps in method 2400 performed in any order, simultaneously
with one another, quasi-simultaneously, or overlapping in time.
[0291] Step 2402 includes receiving a nucleotide string including a
plurality of nucleotides from at least a portion of one or more
individual patients genome, wherein the portion of the genome
includes at least one of the following elements: a 5'-UTR, a
promoter, an enhancer, a silencer, an exon, an intron, a coding
sequence, a non-protein coding RNA, a splice acceptor, a splice
donor, a branch point site, a 3'-UTR, a poly-A addition site or
signal, or a cryptic version thereof, from a known protein coding
gene and a non-protein coding RNA gene, and within the genes not
yet identified in a dark matter genome.
[0292] Step 2404 includes identifying a plurality of variants in
said nucleotide string by comparing a sequence of said nucleotide
string with at least one reference genome, wherein at least one of
the variants is in at least one of the 5'-UTR, the promoter, the
enhancer, the silencer, the exon, the intron, the non-protein
coding RNA, the splice acceptor, the splice donor, the branch point
site, the 3'-UTR, the poly-A addition site or signal, or the
cryptic version thereof.
[0293] Step 2406 includes assigning each identified variant a score
based on a location of a variant and a predicted functional
consequence.
[0294] Step 2408 includes determining a strength of a variation
responsible for a trait or phenotypic manifestation of the variants
based on a similarity score by executing instructions from an
algorithm such as Shapiro-Senapathy algorithm, a MaxEntScan
algorithm, and NNSplice algorithm, stored in a memory.
[0295] Step 2410 includes identifying at least one phenotype such
as a disease, a drug response, a therapeutic indication, and a
harmful side effect of a medication or substance, based on the
strength of the variation identified in one or more patients.
[0296] Step 2412 includes displaying, in a graphic unit interface
of a client device, said nucleotide string, the identified
variants, and the at least one phenotype, in one or more genetic
elements for the one or more individual patients.
[0297] FIG. 25 illustrates steps in a method 2500 for applying a
gene explorer platform to identify structural variants and
phenotypic traits in a population cohort, according to some
embodiments. Each one or more of the steps in method 2500 may be
performed at least partially by a processor executing instructions
stored in a memory of a client device or a server communicatively
coupled with each other via communications modules accessing a
network, as disclosed herein (e.g., processors 212, memories 220,
communications modules 218, client device 110, and server 130). In
some embodiments, at least one or more of the steps in method 2500
may be performed by an application hosted by the server and
installed in the client device, the application including a graphic
display for illustrating the results of at least one or more of the
steps in method 2500 (e.g., application 222 and graphic display
225). In some embodiments, method 2500 may be at least partially
performed by a genome sequence analysis engine in the server, the
genome sequence analysis engine including a sequence scoring tool,
a mutation tool, a statistics tool, and an algorithm tool (e.g.,
genome sequence analysis engine 242, sequence scoring tool 244,
mutation tool 246, statistics tool 248, and algorithm 250).
Further, in some embodiments, one or more of the steps in method
2500 may be performed by a gene discovery module, an EHR module, a
launch module, a report module, a pharmacogenomics module, a cohort
module, a clinical trial module, a TMB/MSI module, a rearrangement
module, a pathogenome module, a pathogene module, and a dark matter
module interacting with a genome analysis engine, consistent with
the present disclosure (e.g., genome analysis engine 255 and
modules 260). In some embodiments, a method consistent with the
present disclosure may include at least one of the steps in method
2500 performed in any order, simultaneously with one another,
quasi-simultaneously, or overlapping in time.
[0298] Step 2502 includes receiving a nucleotide string from a
subject including multiple nucleotides forming at least one of an
exon, at least two introns, at least two splice sites, a promoter
site, a silencer site, an untranslated region, a poly-A site, a
non-coding RNA site, a branching point site and optionally a
cryptic version thereof.
[0299] Step 2504 includes identifying, in said nucleotide string, a
structural variant based on one or more genes shared by a
population of subjects.
[0300] Step 2506 includes identifying a phenotype trait based on
the structural variant.
[0301] Step 2508 includes providing, to a graphic user interface in
a client device, a display of the phenotype trait, the one or more
genes, and the structural variant. In some embodiments, step 2508
includes identifying the structural variant as a gene fusion
between two or more of the genes shared by the population of
subjects.
[0302] FIG. 26 illustrates steps in a method for scoring the
variants of a gene based on a subject response to a disease,
according to some embodiments. Each one or more of the steps in
method 2600 may be performed at least partially by a processor
executing instructions stored in a memory of a client device or a
server communicatively coupled with each other via communications
modules accessing a network, as disclosed herein (e.g., processors
212, memories 220, communications modules 218, client device 110,
and server 130). In some embodiments, at least one or more of the
steps in method 2600 may be performed by an application hosted by
the server and installed in the client device, the application
including a graphic display for illustrating the results of at
least one or more of the steps in method 2600 (e.g., application
222 and graphic payload 225). In some embodiments, method 2600 may
be at least partially performed by a genome sequence analysis
engine in the server, the genome sequence analysis engine including
a sequence scoring tool, a mutation tool, a statistics tool, and an
algorithm tool (e.g., genome sequence analysis engine 242, sequence
scoring tool 244, mutation tool 246, statistics tool 248, and
algorithm 250). Further, in some embodiments, one or more of the
steps in method 2600 may be performed by a gene discovery module,
an EHR module, a launch module, a report module, a pharmacogenomics
module, a cohort module, a clinical trial module, a TMB/MSI module,
a rearrangement module, a pathogenome module, a pathogene module,
and a dark matter module interacting with the genome sequence
analysis engine, consistent with the present disclosure (e.g.,
modules 260). In some embodiments, a method consistent with the
present disclosure may include at least one of the steps in method
2600 performed in any order, simultaneously with one another,
quasi-simultaneously, or overlapping in time.
[0303] Step 2602 includes receiving a nucleotide string from a
subject, the nucleotide string including a plurality of
nucleotides.
[0304] Step 2604 includes identifying, in said nucleotide string, a
gene shared by a cohort of subjects sharing a phenotype trait
associated with a disease.
[0305] Step 2606 includes identifying one or more variants of the
gene within the cohort of subjects.
[0306] Step 2608 includes associating a score for each of the
variants based on a response of the subject to the disease. In some
embodiments, step 2608 includes determining a multiplicity of
infection of the disease in the subject.
[0307] Step 2610 includes providing, to a graphic unit interface in
a client device, a display of the gene, the one or more variants,
and the scores for each of the variants.
Hardware Overview
[0308] FIG. 27 is a block diagram illustrating an example computer
system with which the client and server of FIGS. 1 and 2 and the
methods of FIGS. 24, 25, and 26 can be implemented. In certain
aspects, the computer system 2700 may be implemented using hardware
or a combination of software and hardware, either in a dedicated
server, or integrated into another entity, or distributed across
multiple entities.
[0309] Computer system 2700 (e.g., client device 110 and server
130) includes a bus 2708 or other communication mechanism for
communicating information, and a processor 2702 (e.g., processors
212) coupled with bus 2708 for processing information. By way of
example, the computer system 2700 may be implemented with one or
more processors 2702. Processor 2702 may be a general-purpose
microprocessor, a microcontroller, a Digital Signal Processor
(DSP), an Application Specific Integrated Circuit (ASIC), a Field
Programmable Gate Array (FPGA), a Programmable Logic Device (PLD),
a controller, a state machine, gated logic, discrete hardware
components, or any other suitable entity that can perform
calculations or other manipulations of information.
[0310] Computer system 2700 can include, in addition to hardware,
code that creates an execution environment for the computer program
in question, e.g., code that constitutes processor firmware, a
protocol stack, a database management system, an operating system,
or a combination of one or more of them stored in an included
memory 2704 (e.g., memories 220), such as a Random Access Memory
(RAM), a flash memory, a Read-Only Memory (ROM), a Programmable
Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a
hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable
storage device, coupled with bus 2708 for storing information and
instructions to be executed by processor 2702. The processor 2702
and the memory 2704 can be supplemented by, or incorporated in,
special purpose logic circuitry.
[0311] The instructions may be stored in the memory 2704 and
implemented in one or more computer program products, e.g., one or
more modules of computer program instructions encoded on a
computer-readable medium for execution by, or to control the
operation of, the computer system 2700, and according to any method
well known to those of skill in the art, including, but not limited
to, computer languages such as data-oriented languages (e.g., SQL,
dBase), system languages (e.g., C, Objective-C, C++, Assembly),
architectural languages (e.g., Java, .NET), and application
languages (e.g., PHP, Ruby, Perl, Python). Instructions may also be
implemented in computer languages such as array languages,
aspect-oriented languages, assembly languages, authoring languages,
command line interface languages, compiled languages, concurrent
languages, curly-bracket languages, dataflow languages,
data-structured languages, declarative languages, esoteric
languages, extension languages, fourth-generation languages,
functional languages, interactive mode languages, interpreted
languages, iterative languages, list-based languages, little
languages, logic-based languages, machine languages, macro
languages, metaprogramming languages, multi paradigm languages,
numerical analysis, non-English-based languages, object-oriented
class-based languages, object-oriented prototype-based languages,
off-side rule languages, procedural languages, reflective
languages, rule-based languages, scripting languages, stack-based
languages, synchronous languages, syntax handling languages, visual
languages, with languages, and xml-based languages. Memory 2704 may
also be used for storing temporary
[0312] or other intermediate information during execution of
instructions to be executed by processor 2702.
[0313] A computer program as discussed herein does not necessarily
correspond to a file in a file system. A program can be stored in a
portion of a file that holds other programs or data (e.g., one or
more scripts stored in a markup language document), in a single
file dedicated to the program in question, or in multiple
coordinated files (e.g., files that store one or more modules,
subprograms, or portions of code). A computer program can be
deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and inter-coupled by a communication network. The processes and
logic flows described in this specification can be performed by one
or more programmable processors executing one or more computer
programs to perform functions by operating on input data and
generating output.
[0314] Computer system 2700 further includes a data storage device
2706 such as a magnetic disk or optical disk, coupled with bus 2708
for storing information and instructions. Computer system 2700 may
be coupled via input/output module 2710 to various devices.
Input/output module 2710 can be any input/output module. Exemplary
input/output modules 2710 include data ports such as USB ports. The
input/output module 2710 is configured to connect to a
communications module 2712. Exemplary communications modules 2712
(e.g., communications modules 218) include networking interface
cards, such as Ethernet cards and modems. In certain aspects,
input/output module 2710 is configured to connect to a plurality of
devices, such as an input device 2714 (e.g., input device 214)
and/or an output device 2716 (e.g., output device 216). Exemplary
input devices 2714 include a keyboard and a pointing device, e.g.,
a mouse or a trackball, by which a user can provide input to the
computer system 2700. Other kinds of input devices 2714 can be used
to provide for interaction with a user as well, such as a tactile
input device, visual input device, audio input device, or
brain-computer interface device. For example, feedback provided to
the user can be any form of sensory feedback, e.g., visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
tactile, or brain wave input. Exemplary output devices 2716 include
display devices, such as an LCD (liquid crystal display) monitor,
for displaying information to the user.
[0315] According to one aspect of the present disclosure, the
client device 110 and server 130 can be implemented using a
computer system 2700 in response to processor 2702 executing one or
more sequences of one or more instructions contained in memory
2704. Such instructions may be read into memory 2704 from another
machine-readable medium, such as data storage device 2706.
Execution of the sequences of instructions contained in main memory
2704 causes processor 2702 to perform the process steps described
herein. One or more processors in a multi-processing arrangement
may also be employed to execute the sequences of instructions
contained in memory 2704. In alternative aspects, hard-wired
circuitry may be used in place of or in combination with software
instructions to implement various aspects of the present
disclosure. Thus, aspects of the present disclosure are not limited
to any specific combination of hardware circuitry and software.
[0316] The subject technology is illustrated, for example,
according to various aspects described below. Various examples of
aspects of the subject technology are described as numbered clauses
(clause 1, 2, etc.) for convenience. These are provided as
examples, and do not limit the subject technology.
[0317] In one aspect, a method may be an operation, an instruction,
or a function and vice versa. In one aspect, a clause may be
amended to include some or all of the words (e.g., instructions,
operations, functions, or components) recited in other one or more
clauses, one or more words, one or more sentences, one or more
phrases, one or more paragraphs, and/or one or more clauses.
[0318] To illustrate the interchangeability of hardware and
software, items such as the various illustrative blocks, modules,
components, methods, operations, instructions, and algorithms have
been described generally in terms of their functionality. Whether
such functionality is implemented as hardware, software, or a
combination of hardware and software depends upon the particular
application and design constraints imposed on the overall system.
Skilled artisans may implement the described functionality in
varying ways for each particular application.
[0319] As used herein, the phrase "at least one of" preceding a
series of items, with the terms "and" or "or" to separate any of
the items, modifies the list as a whole, rather than each member of
the list (e.g., each item). The phrase "at least one of" does not
require selection of at least one item; rather, the phrase allows a
meaning that includes at least one of any one of the items, and/or
at least one of any combination of the items, and/or at least one
of each of the items. By way of example, the phrases "at least one
of A, B, and C" or "at least one of A, B, or C" each refer to only
A, only B, or only C; any combination of A, B, and C; and/or at
least one of each of A, B, and C.
[0320] The word "exemplary" is used herein to mean "serving as an
example, instance, or illustration." Any embodiment described
herein as "exemplary" is not necessarily to be construed as
preferred or advantageous over other embodiments. Phrases such as
an aspect, the aspect, another aspect, some aspects, one or more
aspects, an implementation, the implementation, another
implementation, some implementations, one or more implementations,
an embodiment, the embodiment, another embodiment, some
embodiments, one or more embodiments, a configuration, the
configuration, another configuration, some configurations, one or
more configurations, the subject technology, the disclosure, the
present disclosure, other variations thereof and alike are for
convenience and do not imply that a disclosure relating to such
phrase(s) is essential to the subject technology or that such
disclosure applies to many configurations of the subject
technology. A disclosure relating to such phrase(s) may apply to
many configurations, or one or more configurations. A disclosure
relating to such phrase(s) may provide one or more examples. A
phrase such as an aspect or some aspects may refer to one or more
aspects and vice versa, and this applies similarly to other
foregoing phrases.
[0321] A reference to an element in the singular is not intended to
mean "one and only one" unless specifically stated, but rather "one
or more." Pronouns in the masculine (e.g., his) include the
feminine and neuter gender (e.g., her and its) and vice versa. The
term "some" refers to one or more. Underlined and/or italicized
headings and subheadings are used for convenience only, do not
limit the subject technology, and are not referred to in connection
with the interpretation of the description of the subject
technology. Relational terms such as first and second and the like
may be used to distinguish one entity or action from another
without necessarily requiring or implying any actual such
relationship or order between such entities or actions. Many
structural and functional equivalents to the elements of the
various configurations described throughout this disclosure that
are known or later come to be known to those of ordinary skill in
the art are expressly incorporated herein by reference and intended
to be encompassed by the subject technology. Moreover, nothing
disclosed herein is intended to be dedicated to the public
regardless of whether such disclosure is explicitly recited in the
above description. No clause element is to be construed under the
provisions of 35 U.S.C. .sctn. 112, sixth paragraph, unless the
element is expressly recited using the phrase "means for" or, in
the case of a method clause, the element is recited using the
phrase "step for."
[0322] While this specification contains many specifics, these
should not be construed as limitations on the scope of what may be
described, but rather as descriptions of particular implementations
of the subject matter. Certain features that are described in this
specification in the context of separate embodiments can also be
implemented in combination in a single embodiment. Conversely,
various features that are described in the context of a single
embodiment can also be implemented in multiple embodiments
separately or in any suitable subcombination. Moreover, although
features may be described above as acting in certain combinations
and even initially described as such, one or more features from a
described combination can in some cases be excised from the
combination, and the described combination may be directed to a
subcombination or variation of a subcombination.
[0323] The subject matter of this specification has been described
in terms of particular aspects, but other aspects can be
implemented and are within the scope of the following clauses. For
example, while operations are depicted in the drawings in a
particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that many illustrated operations be performed,
to achieve desirable results. The actions recited in the clauses
can be performed in a different order and still achieve desirable
results. As one example, the processes depicted in the accompanying
figures do not necessarily require the particular order shown, or
sequential order, to achieve desirable results. In certain
circumstances, multitasking and parallel processing may be
advantageous. Moreover, the separation of various system components
in the aspects described above should not be understood as
requiring such separation in many aspects, and it should be
understood that the described program components and systems can
generally be integrated together in a single software product or
packaged into multiple software products.
[0324] The title, background, brief description of the drawings,
abstract, and drawings are hereby incorporated into the disclosure
and are provided as illustrative examples of the disclosure, not as
restrictive descriptions. It is submitted with the understanding
that they will not be used to limit the scope or meaning of the
clauses. In addition, in the detailed description, it can be seen
that the description provides illustrative examples and the various
features are grouped together in various implementations for the
purpose of streamlining the disclosure. The method of disclosure is
not to be interpreted as reflecting an intention that the described
subject matter requires more features than are expressly recited in
each clause. Rather, as the clauses reflect, inventive subject
matter lies in less than all features of a single disclosed
configuration or operation. The clauses are hereby incorporated
into the detailed description, with each clause standing on its own
as a separately described subject matter.
[0325] The clauses are not intended to be limited to the aspects
described herein, but are to be accorded the full scope consistent
with the language clauses and to encompass many legal equivalents.
Notwithstanding, none of the clauses are intended to embrace
subject matter that fails to satisfy the requirements of the
applicable patent law, nor should they be interpreted in such a
way.
RECITATION OF EMBODIMENTS
[0326] In a first embodiment, a computer-implemented method
includes receiving a nucleotide string including a plurality of
nucleotides from at least a portion of one or more individual
patients genome, wherein the portion of the genome includes at
least one of the following elements: a 5'-UTR, a promoter, an
enhancer, a silencer, an exon, an intron, a coding sequence, a
non-protein coding RNA, a splice acceptor, a splice donor, a branch
point site, a 3'-UTR, a poly-A addition site or signal, or a
cryptic version thereof, from a known protein coding gene and a
non-protein coding RNA gene, and within the genes not yet
identified in a dark matter genome. The computer-implemented method
also includes identifying a plurality of variants in said
nucleotide string by comparing a sequence of said nucleotide string
with at least one reference genome, wherein at least one of the
variants is in at least one of the 5'-UTR, the promoter, the
enhancer, the silencer, the exon, the intron, the non-protein
coding RNA, the splice acceptor, the splice donor, the branch point
site, the 3'-UTR, the poly-A addition site or signal, or the
cryptic version thereof. The computer-implemented method also
includes assigning each identified variant a score based on a
location of a variant and a predicted functional consequence,
determining a strength of a variation responsible for a trait or
phenotypic manifestation of the variants based on a similarity
score by executing instructions from an algorithm such as
Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice
algorithm, stored in a memory, identifying at least one phenotype
such as a disease, a drug response, therapeutic indications, and
harmful side effects of a medication or substance, based on the
strength of the variation identified in one or more patients, and
displaying, in a graphic unit interface of a client device, said
nucleotide string, the identified variants, and the at least one
phenotype, in one or more genetic elements for the one or more
individual patients.
[0327] In a second embodiment, a computer-implemented method
includes receiving a nucleotide string from a subject including a
plurality of nucleotides wherein said nucleotide string includes at
least one exon, at least two introns, at least two splice sites, a
promoter site, a silencer site, an untranslated region, a poly-A
site, a non-coding RNA site, a branching point site, and optionally
a cryptic version thereof. The computer-implemented method may
include identifying, in said nucleotide string, a structural
variant based on one or more genes shared by a population of
subjects, identifying a phenotype trait based on the structural
variant, and providing, to a client device, a graphic display of
the phenotype trait, the one or more genes, and the structural
variant.
[0328] In a third embodiment, a computer-implemented method
includes receiving a nucleotide string from a subject including a
plurality of nucleotides, identifying, in said nucleotide string, a
gene shared by a cohort of subjects sharing a phenotype trait
associated with a disease, identifying one or more variants of the
gene within the cohort of patients, associating a score for each of
the variants based on a response of the subject to the disease, and
providing, to a client device, a graphic display of the gene, the
one or more variants, and the scores for each of the variants.
[0329] In a fourth embodiment, a system, includes one or more
processors, and a memory storing instructions. When the
instructions are executed by the one or more processors, they cause
the system to: receive a nucleotide string comprising a plurality
of nucleotides from at least a portion of one or more individual
patients genome, wherein the portion of one or more individual
patients genome includes at least one of: a 5'-UTR, a promoter, an
enhancer, a silencer, an exon, an intron, a coding sequence, a
non-protein coding RNA, a splice acceptor, a splice donor, a branch
point site, a 3'-UTR, a poly-A addition site or signal, or a
cryptic version thereof, from a known protein coding gene and a
non-protein coding RNA gene, and within the genes not yet
identified in a Dark Matter genome; identify a plurality of
variants in said nucleotide string by comparing a sequence of said
nucleotide string with at least one reference genome, wherein at
least one of the variants is in at least one of the 5'-UTR, the
promoter, the enhancer, the silencer, the exon, the intron, the
non-protein coding RNA, the splice acceptor, the splice donor, the
branch point site, the 3'-UTR, the poly-A addition site or signal,
or the cryptic version thereof; assign each identified variant a
score based on a location of a variant and a predicted functional
consequence; determine a strength of a variation responsible for a
trait or phenotypic manifestation of the variants based on a
similarity score by executing instructions from an algorithm such
as Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and
NNSplice algorithm, stored in a memory; identify at least one
phenotype such as a disease, a drug response, a therapeutic
indications, and a harmful side effects of a medication or
substance, based on the strength of the variation identified in one
or more patients; and display, in a graphic unit interface of a
client device, said nucleotide string, the identified variants, and
the at least one phenotype, in one or more genetic elements for one
or more individual patients.
[0330] In a fifth embodiment, a non-transitory, computer readable
medium storing instructions which, when executed by a processor,
cause a computer to perform a method, the method includes:
receiving a nucleotide string comprising a plurality of nucleotides
from at least a portion of one or more individual patients genome,
wherein the portion of one or more individual patients genome
includes at least one of: a 5'-UTR, a promoter, an enhancer, a
silencer, an exon, an intron, a coding sequence, a non-protein
coding RNA, a splice acceptor, a splice donor, a branch point site,
a 3'-UTR, a poly-A addition site or signal, or a cryptic version
thereof, from a known protein coding gene and a non-protein coding
RNA gene, and within the genes not yet identified in a Dark Matter
genome; identifying a plurality of variants in said nucleotide
string by comparing a sequence of said nucleotide string with at
least one reference genome, wherein at least one of the variants is
in at least one of the 5'-UTR, the promoter, the enhancer, the
silencer, the exon, the intron, the non-protein coding RNA, the
splice acceptor, the splice donor, the branch point site, the
3'-UTR, the poly-A addition site or signal, or the cryptic version
thereof; assigning each identified variant a score based on a
location of a variant and a predicted functional consequence;
determining a strength of a variation responsible for a trait or
phenotypic manifestation of the variants based on a similarity
score by executing instructions from an algorithm such as
Shapiro-Senapathy algorithm, a MaxEntScan algorithm, and NNSplice
algorithm, stored in a memory; identifying at least one phenotype
such as a disease, a drug response, a therapeutic indications, and
a harmful side effects of a medication or substance, based on the
strength of the variation identified in one or more patients; and
displaying, in a graphic unit interface of a client device, said
nucleotide string, the identified variants, and the at least one
phenotype, in one or more genetic elements for one or more
individual patients.
Element 1, further including enabling the visualization of many of
the patients' meta information in tabular and graphical
illustrations, and providing the ability to launch projects in the
present disclosure. Element 2, further including providing the
ability to automatically download any and many data from the IEHR
into the GE EHR system that are relevant to genomics, and providing
various kinds of analytical capabilities and metrics for the
administration personnel and clinical researchers. Element 3,
further including determining the frequency of mutations, and genes
or patients with mutations in each of the coding sequence,
non-coding sequence, regulatory and splicing elements in different
protein coding genes, non-coding RNA genes, dark matter genes,
individually or in combination, and depicting the frequencies and
the details of their genetic and genomic locations for various
genes for the different genetic elements in various tabular and
graphical illustrations. Element 4, further including determining
the frequency of patients including a defective domain due to
mutations in at least one of the genetic elements in the genes
coding for that domain, and depicting the frequencies and the
details in various tabular and graphical illustrations. Element 5,
further including determining the frequency of patients including
defective domains and the genes containing them, due to mutations
in at least one of the genetic elements in the genes coding for
that domain, and depicting the frequencies and the details in
various tabular and graphical illustrations. Element 6, further
including determining the frequency of patients including defective
genes within a gene family due to mutations in at least one of the
genetic elements in the genes in the gene family, and depicting the
frequencies and the details in various tabular and graphical
illustrations. Element 7, further including determining the
frequency of patients including of one or more defective pathways
due to mutations in at least one of the genetic elements in the
genes constituting the pathway, and depicting the frequencies and
the details in various tabular and graphical illustrations. Element
8, further including graphically and tabularly depicting the
statistics of frequencies of mutations, genes, or patients with
mutations, and optionally mutated genes in gene families, in
different protein coding genes, non-coding RNA genes, and dark
matter genes affecting the domains and pathways, in various
graphics and plots. Element 9, further including, determining the
genes that exhibit high number of mutations across different
genetic elements, individually or in combination, in one or more
patients and graphically and tabularly depicting the statistics of
frequencies of mutations, genes, or patients with mutations.
Element 10, including, graphically plotting the mutation
distribution within introns and exons within one or more genes from
one or more patients represented by one exon for many exonic
mutations, and one intron for many intronic mutations, and
optionally whose lengths are normalized to a unit length (e.g.,
100) and different possible variations in representing the
distribution in one exon and two introns or one intron and two
exons. Element 11, further including, detecting the genes including
mutations in at least one of the genetic elements in two or more
patients, based on the frequency of mutated genes, that are causal
of a disease, therapeutic indications, or drug response phenotype.
Element 12, further including, detecting the biochemical function
whose defect or alteration caused a disease, based on highly
mutated genes in at least one of the genetic elements in two or
more patients, with the underlying disease. Element 13, further
including, detecting the biochemical function whose defect or
alteration caused a therapeutic indication or drug response
phenotype, based on highly mutated genes in at least one of the
genetic elements in two or more patients, with the underlying
therapeutic indication or drug response phenotype. Element 14,
further including, detecting a disease in an individual based on
the presence of one or more genes from a set of highly mutated
genes in at least one of the genetic elements in a cohort of
patients with the same disease. Element 15, further including,
detecting a therapeutic or drug response phenotype in a patient
based on the presence of one or more genes from a set of highly
mutated genes in at least one of the genetic elements in a cohort
of patients with the same therapeutic or drug response phenotype.
Element 16, further including, grouping the cohort of patients into
various categories of drug response phenotypes, such as high, low,
and medium levels of Adverse Drug Reactions (harmful side effects),
or high, low, and medium levels of drug efficacy, and correlating
the phenotypes with genes highly mutated in different genetic
elements. Element 17, determining the likelihood of a splice site
mutation to skip the exon based on the inherent score of a splice
site and its neighboring cryptic splice sites, and the inherent
score of an exon and the neighboring exons. Element 18, determining
the likelihood of a protein becoming defective, based on the
frequency of particular splice site mutations causing splicing
aberrations in a large population of patients with an underlying
disease, and the application of this information to predict a
defective protein and causation of disease in a new patient.
Element 19, including, determining the top ranking exons based on
the scores of the likelihood of an exon to cause a defective
protein, depending on the frequency of particular splice site
mutations causing splicing aberrations in a large population of
patients with an underlying disease, and the application of this
information to predict a defective protein and causation of disease
in a new patient. Element 20, further including, ascribing scores
for deleteriousness of a mutation based on the invariability of the
amino acid position at which a mutation occurs to determine the
degree of deleteriousness of a patient mutation. Element 21,
determining that mutations in different genes that fall into a
minimal category of cellular roles, such as cell structure, cell
function, and transcriptional regulation, are required to cause a
disease or cancer in a patient, and developing a gene panel for
each of these categories, and applying this information to identify
the disease in a new patient. Element 22, including tabulating and
graphically representing the genes from categories such as cell
structure, cell function, and transcriptional regulation, including
mutations across many of the genetic elements in one or more
patients. Element 23, including determining a gene signature as a
combination of genes from categories such as cell structure, cell
function, and transcriptional regulation, including mutations in at
least one of the genetic elements in one or more patients, and
tabulating and graphically displaying them. Element 24, including
determining that at least one gene from each of the categories such
as cell structure, cell function, and transcriptional regulation,
should be mutated to initiate and establish a cancer or disease in
a patient, indicating a gene signature, and applying this signature
to identify a cancer or a disease in a new patient. Element 25,
including detecting cancer in a patient based on the mutations in
at least one of the genes from each of the gene panels of
categories such as cell structure, cell function, and
transcriptional regulation, and tabulating and graphically
displaying them. Element 26, wherein the initiation and
establishment of a cancer or non-cancer disease, is dependent upon
more than one gene being mutated. It indicates that minimal cell
structure and functions has to be disruptive to achieve the
disease. Based on the number of genes mutated from these
categories, a score can be assigned for the likelihood of a disease
in a patient, and using this score to determine and assign a
likelihood for the occurrence of a disease in a new patient.
Element 27, wherein the scores for different genes may vary
depending on its importance in the cellular structure, function,
and regulation. In addition, they can vary based on the frequency
of these genes occurring in a large population exhibiting a
disease. Element 28, including detecting a probability score of a
cancer or disease in a patient based on the score for the
likelihood of a cancer or disease determined from the number of
mutated genes in a minimal category of cellular structure,
function, and transcriptional regulation. Element 29, including
detecting and predicting the possibility of a cancer or disease in
a patient, based on the score for the likelihood of a cancer or
disease determined from the number of mutated genes with affected
status in a disease-gene-panel or within a gene signature. Element
30, including determining the frequency of combination of genes
with mutations in at least one of the genes from the gene panel of
a particular cancer type, in a large population of patients, and
identifying the singlet, doublet, triplet, or higher number of
combinations of genes that occur at a frequency greater than a
threshold from the population of patients. Element 31, including
determining the frequency of combination of genes with mutations in
at least one of the genes from the gene panels of multiple cancer
types, in a large population of patients, and identifying the
singlet, doublet, triplet, or higher number of combinations of
genes that occur at a frequency greater than a threshold from the
population of patients. Element 32, including, determining the
frequency of combination of genes with mutations in at least one of
the genes from the gene panel of a particular disease (hypertrophic
cardiomyopathy) or disease type (many cardiac diseases), in a large
population of patients, and identifying the singlet, doublet,
triplet, or higher number of combinations of genes that occur at a
frequency greater than a threshold from the population of patients.
Element 33, including developing a repository of combinations of
genes with mutations from one or more gene panels for a disease,
detected in a large population of patients, and using this
repository to predict that disease in a new patient. Element 34,
further including displaying by tabular and graphical illustrations
the highly mutated genes in at least one of the genetic elements,
in two or more patients exhibiting a particular disease. Element
35, further including displaying by tabular and graphical
illustrations the highly mutated genes containing a defective
domain due to mutations in at least one of the genetic elements, in
two or more patients exhibiting a particular disease. Element 36,
further including displaying by tabular and graphical illustrations
the highly mutated genes involved in defective pathways due to
mutations in at least one of the genetic elements, in two or more
patients exhibiting a particular disease. Element 37, further
including displaying by tabular and graphical illustrations the
metrics, statistics, and patients' IDs containing mutated genes
from the signatures of minimal sets of one to multiple genes, that
are indicative of a disease. Element 38, further including
displaying by tabular and graphical illustrations the metrics,
statistics, and patients' IDs containing the mutations in at least
one of the genetic elements causing aberrations such as exon
skipping, intron retention, and cryptic exon creation, that are
indicative of a disease. Element 39, further including displaying a
concise description of a summary of many of the findings from
cohort analysis. Element 40, including displaying the diagnostic,
therapeutic, PGx genes in a cohort of patients with a particular
disease, and the mutation and patient frequencies in different
blocks in a single view. Element 41, further including displaying
the findings from a cohort analysis in blocks visualizable within a
single view, consisting of genes that are indicative of a disease,
therapeutic drugs, and harmful side effects (pharmacogenomics),
based on the highly mutated genes in at least one of the genetic
elements from a cohort of two or more patients. Element 42, further
including determining the genes that are highly mutated in the
group of patients with high side effects and the genes that are
highly mutated in the group of patients with least side effects,
and determining the mutually exclusive genes from the two lists as
the indicator of adverse effects or least side effects for that
drug. Element 43, further including determining the genes that are
highly mutated in the group of patients with high efficacy and the
genes that are highly mutated in the group of patients with least
efficacy, and determining the mutually exclusive genes from the two
lists as the indicator of high or low efficacy for that drug.
Element 44, further including determining the genes that are highly
mutated in the group of patients with high side effects, as the
indicator of adverse side effects for a drug. Element 45, further
including determining the genes that are highly mutated in the
group of patients with high side effects, and the presence of no
mutations in these genes as the indicator of least side effects for
a drug. Element 46, further including predicting or diagnosing the
level of side effects for a drug in a patient, based on the
presence of mutations in at least one of the genes from the highly
mutated genes in the group of patients with high side effects.
Element 47, further including, predicting or diagnosing the
presence of no or least side effects in a patient for a drug, based
on the presence of mutations in at least one of the genes from the
highly mutated genes in the group of patients with least side
effects, or the absence of mutations in at least one of the genes
from the highly mutated genes in the group of patients with most
side effects. Element 48, further including, predicting or
diagnosing the presence of high efficacy in a patient for a drug,
based on the presence of mutations in at least one of the genes
from the highly mutated genes in the group of patients with high
efficacy, or the absence of mutations in at least one of the genes
from the highly mutated genes in the group of patients with least
or no efficacy. Element 49, further including predicting or
diagnosing the presence of least side effects for a drug in a
patient, based on the presence of mutations in at least one of the
genes from the least mutated genes in the group of patients with
high side effects. Element 50, including graphically marking the
mutations on the genome of the family members and tracing the
inheritance pattern of disease-causing mutations in the
patient/child. Element 51, wherein the multiple individuals include
the father, mother, child, and other extended family members.
Element 52, wherein the mutations, zygosity, mode of inheritance,
and the traits are traced through the family members to detect a
pattern of inheritance of the trait occurring in a child. Element
53, further including determining the causal gene(s) for a rare
disease or a disease with unknown genes or known genes with unknown
mutation, based on tracing the inheritance of the genes through
each generation towards the direction of affected individual(s),
whereby the number of causal genes is reduced by 50% by each
parental or sibling relationship, or 25% by each relationship one
step removed. Element 54, further including graphically displaying
the pedigree chart for many of the members in the family and
marking the affected members to demonstrate the tracing of disease
through the relatives based on the different genetic elements of
the genes, wherein tracing of each of the genetic elements can be
shown by clicking a gene on the child's genome plot or tabular
display. Element 55, including enabling the user to navigate
through the application using voice commands. Element 56, including
enabling the user to go directly to a desired result swiftly based
on voice commands by the user based on built-in data connections.
Element 57, including enabling the user to use voice commands to
know the details of any gene such as gene name, length, number of
exons, number of domains, protein length, and gene function, and
further enabling the user to stop or know more details of the same
or different genes, mutations, disease, drugs, therapeutics, and
harmful side effects. Element 58, including enabling a special
clinical genomics intelligence to detect the appropriate commands
with details of variant, mutation, disease, many different genetic
elements, zygosity, MOI, cohort, clinical trials, Family genomics,
drugs, therapeutics, PGx, side effects, dose, etc. Element 59,
including enabling a special clinical genomics intelligence to
detect the appropriate command from various types of commands used
by various users, wherein they use different accents, use of words,
and unnecessary words, and respond appropriately based on the
command. Element 60, including enabling the user to provide a voice
command and receive an appropriate report that is read to the user
by a voice through voice to text and text to voice functionalities.
Element 61, including designing, implementing, and enabling
clinic-centric, patient-centric, clinician-centric, and
genome-centric questions and answers through voice navigation.
Element 62, including detecting the occurrence of cancer in an
individual, by giving a score for each of a set of genes based on
the number of pathogenic mutations, in CDS and regulatory and
splicing
elements, within that gene in a set of patients (or samples)
reported by a cancer gene database such as COSMIC. Element 63,
further including, selecting a list of genes associated with cancer
as the [0331]"master cancer gene set" (lookup). Element 64,
including determining the number of patients (or samples) with
pathogenic mutations in each gene from the cancer-gene lookup,
calculated from a set of healthy individuals (say 1000) and a set
of patients affected by cancer or any disease (say 1000). Element
65, including determining the total number of patients from many of
the cancer genes occurring in the population of individuals from
the healthy and cancer individuals. Element 66, including
determining the cancer-gene score (%) for normal individuals=(total
# of patients in many of the genes in the healthy population/# of
genes).times.100. Element 67, including determining the cancer-gene
score (%) for cancer individuals=(total # of patients in many of
the genes in the cancer population/# of genes).times.100. Element
68, including determining the threshold indicator for a cancer to
be a value above the value for normal individuals (or Normal
value+base) (e.g., if normal=40 & cancer=80, then any value
>50 is cancer). Element 69, including normalizing the score
between the normal population (say 40%) and the cancer population
(say 80%) into 0% and 100%. Element 70, including determining the
probability for an individual to have cancer from 0 to 100% based
on where the individual's value falls in this spectrum of
normalized score. Element 71, including determining the probability
in another way by using the list of genes in a cancer type as a
signature with their weights (i.e., # of patients for each gene) to
compare the cancer genes from a patient and determine a cancer
probability score for the patient by adding the weights for the
patient genes. Element 72, including determining the threshold that
will be the indicator of presence of a cancer, based on the scores
for the genes from the gene lookup, calculated from healthy
individuals and the patients affected by cancer. Element 73,
including identifying the most frequently mutated genes from the
cohort of patients with a disease by subtracting the genes from the
gene panels of common diseases. Element 74, including determining
the zygosity of these mutated genes in a population of patients
with a disease. Element 75, including, when the zygosity of a
highly frequent gene is mostly R/V, then the mode of inheritance of
that gene will be autosomal dominant (AD). Element 76, including,
when the zygosity of a highly frequent gene is mostly V/V, then the
mode of inheritance of that gene will be autosomal recessive (AR).
Element 77, further including identifying the most frequently
mutated genes from the cohort of patients with a disease by
subtracting the genes from the gene panels of common diseases, and
determining the zygosity of each of these genes. Element 78,
further including determining the inheritance pattern (MOI) of a
gene based on the frequency of its zygosity (R/V or V/V), wherein a
high frequency of R/V (e.g., in 80% of patients) would indicate the
gene to be Autosomal Dominant (AD) and a high frequency of V/V
(e.g., in 80% of patients) would indicate the gene to be Autosomal
Recessive (AR). Element 79, further including, if the zygosity of a
highly frequent gene is mostly V/V, then the mode of inheritance of
that gene will be autosomal recessive (AR).
Sequence CWU 1
1
45197DNAArtificial SequenceSynthetic 1aggtggggct gggcctgggt
tctgtgcagg tggggctggc tgacacccgg cttccgtgtc 60tccacctgga aaagtgactc
tgtgtctctg cttagga 972810DNAArtificial SequenceSynthetic
2gatgggattg gggttttccc ctcccatgtg ctcaagactg gcgctaaaag ttttgagctt
60ctcaaaagtc tagagccacc gtccagggag caggtagctg ctgggctccg gggacacttt
120gcgttcgggc tgggagcgtg ctttccacga cggtgacacg cttccctgga
ttggcagcca 180gactgccttc cgggtcactg ccatggagga gccgcagtca
gatcctagcg tcgagccccc 240tctgagtcag gaaacatttt cagacctatg
acttcctgaa aacaacgttc tggaaacttc 300ccccttgccg tcccaagcaa
tggatgattt gatgctgtcc ccggacgata ttgaacaatg 360gttcactgaa
gacccaggtc cagatgaagc tcccagaatg ccagaggctg ctccccccgt
420ggcccctgca ccagcagctc ctacaccggc ggcccctgca ccagccccct
cctggcccct 480gtcatcttct gtcccttccc agaaaaccta ccagggcagc
tacggtttcc gtctgggctt 540cttgcattct gggacagcca agtctgtgac
ttgcacgtac tcccctgccc tcaacaagat 600gttttgccaa ctggccaaga
cctgccctgt gcagctgtgg gttgattcca cacccccgcc 660cggcacccgc
gtccgcgcca tggccatcta caagcagtca cagcacatga cggaggttgt
720gaggcgctgc ccccaccatc agcgctgctc agatagcgat ggtctggccc
ctcctcagca 780tcttatccga gtggaaggaa atttgcgtgt 8103156DNAArtificial
SequenceSynthetic 3gcgcagctct tcgcgcagag ctacaactcc agcgccgaac
aggtcctgtt ccagagcgtg 60gccgccagct gggcgcacga caccaacatc accgcggaga
atgcaagccg ccaggaggaa 120gcagccctgc tcagccagga gtttgcggag gcctgg
156453PRTArtificial SequenceSynthetic 4Ala Gln Leu Phe Ala Gln Ser
Tyr Asn Ser Ser Ala Glu Gln Val Leu1 5 10 15Phe Gln Ser Val Ala Ala
Ser Trp Ala His Asp Thr Asn Ile Thr Ala 20 25 30Glu Asn Ala Arg Arg
Gln Glu Glu Ala Ala Leu Leu Ser Gln Glu Phe 35 40 45Ala Glu Ala Glu
Trp 50542PRTArtificial SequenceSynthetic 5Tyr Tyr Tyr Thr Tyr Tyr
Val Trp Tyr Trp Tyr Trp Tyr Tyr Trp Pro1 5 10 15Trp Trp Trp Trp Tyr
Tyr Trp Val Tyr Tyr Tyr Tyr Tyr Tyr Tyr Trp 20 25 30Tyr Tyr Gln Tyr
Trp Ser Trp Tyr Trp Arg 35 40639PRTArtificial SequenceSynthetic
6Trp Pro Trp Ser Trp Thr Arg Trp Arg Trp Trp Ser His Tyr Ser Arg1 5
10 15Arg Trp Trp Pro Thr Trp Trp Trp Trp Trp Trp Trp Val Trp Trp
Pro 20 25 30Trp Thr Arg Pro Trp Thr Pro 35739PRTArtificial
SequenceSynthetic 7Thr His Pro Arg Thr Pro Met Val Gln Pro Asn Pro
Glu Gln Met Pro1 5 10 15Pro Pro Pro Leu Ser Pro Val Ser Thr Ser Pro
Arg Pro Pro Ser His 20 25 30Arg Pro Pro Lys Thr Pro Asn
35842PRTArtificial SequenceSynthetic 8Ser Gly Gly Gln Ser His Leu
Pro Arg Cys Pro Pro His Met Asn Asp1 5 10 15Met Cys Met Leu Met Gln
Lys Arg Leu Thr Pro Pro Arg Met Pro Met 20 25 30His Pro Asp Pro Leu
Asn Gly Pro Met Gly 35 40942PRTArtificial SequenceSynthetic 9Arg
Phe Cys Pro Arg Phe Ile Asn Gln Tyr Asn Asn Cys His His Tyr1 5 10
15Leu Tyr His Lys Gly Pro Ile Gln His Pro Met Ile Gln Gly Asn Leu
20 25 30Asp Gly Cys Met Ile Lys Cys Asn Gly Asp 35
401042PRTArtificial SequenceSynthetic 10Gln Cys Val Asn Gln Cys Cys
Met Pro Val His Lys Val Cys Asp Trp1 5 10 15Gly Val Cys His Cys Asn
His Pro Gly Asn His His Pro Phe His Ile 20 25 30Cys Cys Tyr Lys His
Gly Tyr Met Phe Cys 35 401142PRTArtificial SequenceSynthetic 11Pro
Trp Thr Met Pro Trp Tyr Lys Met Thr Gly His Thr Val Cys Val1 5 10
15Glu Thr Tyr Gly Val Met Phe Asn Phe His Gly Gly Met Cys Gly His
20 25 30Val Val Trp Ile Phe Asp Val Phe Cys Tyr 35
401242PRTArtificial SequenceSynthetic 12Asn Val Ser Leu Asn Val Trp
Gly Leu Ser Phe Glu Ser Thr Tyr Thr1 5 10 15Asp Arg Val Phe Thr Leu
Cys Met Val Gly Phe Asp Lys Val Glu Cys 20 25 30Thr Thr Val His Asp
Cys Thr Asp Tyr Trp 35 401342PRTArtificial SequenceSynthetic 13Met
Thr Arg Lys Lys Ser Thr Asp Lys Arg Cys Asp Arg Ser Val Ser1 5 10
15Tyr Gln Thr Glu Ser His Tyr Leu Thr Phe Asp Cys Phe Thr Asp Tyr
20 25 30Ser Arg Thr Glu Cys Tyr Ser Cys Val Val 35
401442PRTArtificial SequenceSynthetic 14Lys Ser Gln His His Arg Ser
Tyr Ile Gln Ala Cys Gln Arg Thr Arg1 5 10 15Val Pro Ser Asp Arg Gly
Val Lys Ser Cys Cys Val Cys Ser Cys Thr 20 25 30Arg Gln Ser Cys Tyr
Trp Arg Val Ser Thr 35 401542PRTArtificial SequenceSynthetic 15His
Arg Asn Gly Gly Gln Arg Val His Pro Thr Tyr Asn Gln Arg Gln1 5 10
15Ser Asn Gln Cys Gln Phe Thr Ile Arg Ser Val Ser Val Arg Val Ser
20 25 30Gln Asn Arg Val Val Val Gln Ser Arg Ser 35
401652PRTArtificial SequenceSynthetic 16Phe Gln Met Glu Glu Asn Gln
Ser Gly Asn Ser Val Met Pro Gln Asn1 5 10 15Arg Leu Asn Tyr Asn Glu
Ser His Gln Cys Ser Phe Glu Phe Gly Gln 20 25 30Pro Glu Gln Arg Thr
Arg Thr Gln Thr Arg Asn Met Asn Thr Ser Thr 35 40 45Asn Arg Gln Gln
501752PRTArtificial SequenceSynthetic 17Glu Asn Leu Asp Asp Met Asn
Gln Phe Leu Arg Thr Leu Leu Met Met1 5 10 15Pro Lys Leu Val Leu Asp
Arg Gly Asn Tyr Asn Glu Cys Glu Phe Pro 20 25 30Asn Asp Asn Gln Arg
Gln Asn Asn Ser Gln Met Leu Met Ser Arg Gln 35 40 45Met Gln Asn Met
501852PRTArtificial SequenceSynthetic 18Asp Met Lys Cys Cys Leu Lys
Leu Cys Lys Gln Ser Lys Lys Leu Leu1 5 10 15Asn Ile Lys Thr Lys Cys
Gln Glu Met Ser Leu Asp Ala Asp Glu Asn 20 25 30Leu Cys Met Met Gln
Asn Leu Leu Gln Asn Leu Lys Leu Gln Gln Met 35 40 45Leu Leu Leu Leu
501949PRTArtificial SequenceSynthetic 19Cys Leu Ile Tyr Val Lys His
Ile Thr Ile Met Met Ile Ile Lys Lys1 5 10 15Ser Ile Val Asn Asp Lys
Gln Ile Cys Tyr Cys Asp Leu Lys Ala Leu 20 25 30Leu Asn Met Ile Lys
Met Lys Lys Ile Lys Asn Asn Leu Ile Lys Lys 35 40
45Lys2049PRTArtificial SequenceSynthetic 20Val Lys His Trp Met Ile
Gly His Ser His Leu Leu Gly Gly Ile Ile1 5 10 15Gln His Thr Met Cys
Ile Asn Gly Ala Ser Val Cys Lys His Thr Lys 20 25 30Lys Leu Leu His
Ile Leu Gly Ile His Ile Leu Met Ile His Ile Ile 35 40
45Ile2152PRTArtificial SequenceSynthetic 21Leu Ile Phe Val Leu Gly
Phe Phe Asn Gly Lys Ile Phe Phe Gly Gly1 5 10 15His Phe Phe Asn Phe
Ser Gly Ala Glu His Phe Val Asn Met Ala His 20 25 30Phe Asn Ile Ile
Lys Lys Gly His Lys Phe Gly Phe Gly Gly Lys His 35 40 45Phe His His
His 502251PRTArtificial SequenceSynthetic 22Ile Glu Glu Ile Glu Glu
Glu Glu Glu Ile Gly Glu Glu Phe Phe Phe1 5 10 15Glu Glu Met Glu Lys
Glu Tyr Asp Gly Glu Thr His Leu Thr Glu Glu 20 25 30Leu Glu Glu Ile
Phe Glu Glu Ile Glu Phe Glu Phe Phe Gly Phe Glu 35 40 45Gly Glu Phe
502352PRTArtificial SequenceSynthetic 23Gly Asp Asp Phe Phe Asp Asp
Cys Asp Asp Glu Phe Asp Asp Glu Cys1 5 10 15Cys Asp Asp Ile Asp Ile
Asp Trp Cys Phe Asp Ser Phe Ile Ser Asp 20 25 30Asp Lys Asp Asp Glu
Glu Asp Asp Phe Asp Glu Asp Glu Asp Glu Glu 35 40 45Asp Glu Asp Glu
502452PRTArtificial SequenceSynthetic 24Ala Ala Ala Ala Ala Ala Ala
Ala Ala Ala Asp Ala Ala Ala Ala Ala1 5 10 15Ala Ala Ala Ala Ala Ala
Ala Phe Ala Ala Ala Asn Asp Ala Asn Ala 20 25 30Ala His Ala Ala Ala
Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala Ala 35 40 45Ala Ala Ala Ala
502580DNAArtificial SequenceSynthetic 25cccagcgtca tcttgtctcc
cagcgtcatc ttgtctccca tcttgtctcc cagcgtcatc 60ttgtctccca gcgtccagca
80261200DNAArtificial SequenceSynthetic 26agttgctaga aagcaatgcg
cctattcaca tggagaatct tccctttcct ctaaaattac 60ttagtgcctc atcactaaac
acccccagct ccacaccatg ggtgttggat atcttcctca 120ccttggtgtt
tgccctgggg ctcttcttcc tattactccc ctacttctct tacctccgtt
180gtgacaaccc accctcacca tcgcctagga agagaaaggt aaggagctct
cagtccagac 240ccacagagct tgattctctt ctttcttttt attattagtt
ccacctttcc aaatccagtg 300gagacttctg cgatgggaag tctcaggaga
gaccagaaca gcatgcttcc agggagaggc 360agggcagcca ggggttggta
ggggtagatc gtgtactggg atttccatcc caagctctca 420gtccatctgt
gggggagcca ggaggcatga aggcaaaatc aaacccgtgg gctcagcggc
480caggaccggt catgagacgg gggaggtctc tggtcatgag acgggaggtc
tctgtccgag 540gccaggccct gagccctggc tcatcagtcc cttcctgggg
caggtggctc gggacccagc 600ctcttctgtg tggggtgata tggggcctgt
gctgggcccc cgaggggcct cccaccgggc 660ctggcatctc ctctggtctc
ctggcaagca gaatgctacc tgacagctca gtggtgcctg 720cgggcctgag
cctgggtgtt cctggagcag aggaacaggg actgatggcg tccatggtgg
780acctcatatt gaaaatccct gtgtgtgtgc gtgtgtgttt gtgtgttatt
tttatttatt 840ttgttttttg tgcaggctgc agtgaaatga agcgataccg
gctcagtgcc acctttgctt 900ccagagttca agctattctc ctgtcttagc
gtcctgagta gctggggatt acaggcgccc 960gccaccacgc ctggctaatt
ttcgtatttt tagtagagat ggggtttcac catggccagg 1020ctggtctcaa
actcttgacc tcaggtgatc cacctgcctc ggcctcccaa agtgcttgga
1080ttataggcgt gagccaccgc acccgacccc ctcttcctgt ttttctaaga
aaagcagttt 1140atcatccatt taaacatgag tgggagggga gaaagcacac
agagctccct gagcaagaca 120027105DNAArtificial SequenceSynthetic
27atggagaatc ttccctttcc tctaaaatta cttagtgcct catcactaaa cacccccagc
60tccacaccat gggtgttgga tatcttcctc accttggtgt ttgcc
1052835PRTArtificial SequenceSynthetic 28Met Glu Asn Leu Pro Phe
Pro Leu Lys Leu Leu Ser Ala Ser Ser Leu1 5 10 15Asn Thr Pro Ser Ser
Thr Pro Trp Val Leu Asp Ile Phe Leu Thr Leu 20 25 30Val Phe Ala
35291200DNAArtificial SequenceSynthetic 29agttgctaga aagcaatgcg
cctattcaca tggagaatct tccctttcct ctaaaattac 60ttagtgcctc atcactaaac
acccccagct ccacaccatg ggtgttggat atcttcctca 120ccttggtgtt
tgccctgggg ttcttcttcc tattactccc ctacttctct tacctccgtt
180gtgacaaccc accctcacca tcgcctaaga agagaaaggt aaggagctct
cagtccagac 240ccacagagct tgattctctt ctttcttttt attattagtt
ccacctttcc aaatccagtg 300gagacttctg cgatgggaag tctcaggaga
gaccagaaca gcatgcttcc agggagaggc 360agggcagcca ggggttggta
ggggtagatc gtgtactggg atttccatcc caagctctca 420gtccatctgt
gggggagcgc aggaggcatg aaggcaaaat caaacccgtg ggctcagcgg
480ccaggaccgg tcatgagacg ggggaggtct ctggtcatga gacgggggag
gtctctgtcc 540gaggccaggc cctgagccct ggctcatcag tcccttcctg
gggcaggtgg ctcgggaccc 600agcctcttct gtgtggggtg atatggggcc
tgtgctgggc ccccgagggc ctcccaccgg 660ggcctggcat ctcctctggt
ctcctggcaa gcagaatgct acctgacagc tcagtggtgc 720ctgcgggcct
gagcctgggt gttcctggag cagaggaaca gggactgatg gcgtccatgg
780tggacctcat attgaaaatc cctgtgtgtg tgcgtgtgtg tttgtgtgtt
atttttattt 840attttgtttt ttgtgcaggc tgcagtgaaa tggagcgata
ccggctcagt gccacctttg 900cttccagagt tcaagctatt ctcctgtctt
agcgtcctga gtagctgggg attacaggcg 960cccgccacca cgcctggcta
atttttgtat ttttagtaga gatggggttt caccatggct 1020aggctggtct
caaactcttg acctcaggtg atccacctgc ctcagcctcc caaagtgctg
1080ggattatagg cgtgagccac cgcaccgtga gccactgcac ccgaccccct
cttcctgttt 1140ttctaagaaa agcagtttat catccattta aacatgagtg
ggaggggagg aagcacacag 120030105DNAArtificial SequenceSynthetic
30atggagaatc ttccctttcc tctaaaatta cttagtgcct catcactaaa cacccccagc
60tccacaccat gggtgttgga tatcttcctc accttggtgt ttgcc
1053135PRTArtificial SequenceSynthetic 31Met Glu Asn Leu Pro Phe
Pro Leu Lys Leu Leu Ser Ala Ser Ser Leu1 5 10 15Asn Thr Pro Ser Ser
Thr Pro Trp Val Leu Asp Ile Phe Leu Thr Leu 20 25 30Val Phe Ala
35321200DNAArtificial SequenceSynthetic 32agttgctaga aagcaatgcg
cctattcaca tggagaatct tccctttcct ctaaaattac 60ttagtgcctc atcactaaac
acccccagct ccacaccatg ggtgttggat atcttcctca 120ccttggtgtt
tgccctgggg ctcttcttcc tattactccc ctacttctct tacctccgtt
180gtgacaaccc accctcacca tcgcctagga agagaaagcg tcatcttgtc
tcccagcgtc 240cagcagggcg gagggggagg cccagaggca ggatgaaaaa
ccacagtctg agagggtaag 300gctctgccag ggcacactag agttaatttg
atctcatctg tccgggaggg aactgactct 360gaagaagtca gttgaaaaaa
cctgaggtgg gggctcctag gaaggaaatc agaaccctgg 420gtccttctca
gattctgtgc cgccggaatg aagccatggt gggccaggga ctgagcgtta
480cccagcaggg ggcagtgtgt gtgtcctggg gagaccaatg cctgcctgga
tgcggagggg 540ggtgaggggc ctcccgctcc ctgggaacag ctgttcaact
ctgctaaggc tgattcctct 600ctgagaccac cccagtcctt tctcctcaca
ggcgagttgt gaggaccgtg ggggtggagg 660gtctgtgtgt ggaagccctt
tgtgaatgac aaagccctgt cctctatgcc ttgctattac 720cgtcggggcc
atgtggcttt ggacacagat gggtgggttc cagggtctaa ttccccatgg
780tcttccctaa agaaacatac actcagcctc ctgtgagatc ccaagcccct
ccctcactgc 840cctaacccgg tctgatttcc agcttgtaga gagtgcccga
gaggcctgga ggagacttgg 900gacctgcttt cacaactgca gaggtgaggc
acttcccctt ccctgcgtcc ttcctaccag 960ggctgggacg tgaccccagg
gccacaggca gcctggagtc gacctgggat ggagagacca 1020gggggacaga
ggatgggagt aaagccctgg ggcgaggggt agcaggagaa ttgggtggtc
1080agggtgtggc gtggtggagg ggctgtggcc caagcgccca ctctgccctg
gcaacccatc 1140tgctcctggc tgcagcttgt gcctcctgtc tcctgcagcc
tcctggggcc acaccttgaa 120033102DNAArtificial SequenceSynthetic
33atggagaatc ttccctttcc tctaaaatta cttagcgtca tcttgtctcc cagcgtccag
60ggcggagggg gaggcccaga ggcaggatga aaaaccacag tc
1023434PRTArtificial SequenceSynthetic 34Met Glu Asn Leu Pro Phe
Pro Leu Lys Leu Leu Ser Val Ile Leu Ser1 5 10 15Pro Ser Val Gln Gln
Gly Gly Gly Gly Gly Pro Glu Ala Gly Lys Thr 20 25 30Thr
Val357DNAArtificial SequenceSynthetic 35gagaaat 73612DNAArtificial
SequenceSynthetic 36cgtggccgag cc 123712DNAArtificial
SequenceSynthetic 37cccgagaaat ga 123814DNAArtificial
SequenceSynthetic 38cagggactgg ggct 143914DNAArtificial
SequenceSynthetic 39agggactggg gctc 144014DNAArtificial
SequenceSynthetic 40tttgggtccg ggcg 144114DNAArtificial
SequenceSynthetic 41ttgggtccgg gcgg 144214DNAArtificial
SequenceSynthetic 42tgggtccggg cggg 14432000DNAArtificial
SequenceSynthetic 43ctgggcggcc gggcgcgggg agagggcgcg ggagcggctc
gtgcggcagg taccatgcgg 60acgcgcgagc ccggcgaggg ccccggcagg cccggtccct
gctcgggggc gcgctgagac 120ggcgggtgag ctccacgaga gcgccgtcgc
cacttcgggc caactttgcg attcccgaca 180gttaagcaat ggggagacat
ttggctttgc tcctgcttct gctccttctc ttccaacatt 240ttggagacag
tgatggcagc caacgacttg aacagactcc tctgcagttt acacacctcg
300agtacaacgt caccgtgcag gagaactctg cagctaagac ttatgtgggg
catcctgtca 360agatgggtgt ttacattaca catccagcgt gggaagtaag
gtacaaaatt gtttccggag 420acagtgaaaa cctgttcaaa gctgaagagt
acattctcgg agacttttgc tttctaagaa 480taaggaccaa aggaggaaat
aacagctatt cttaatgaga agtgaaggat cactacacat 540tgatagtgaa
agcacttgaa aaaaatacta atgtggaggc gcgaacaaag gtcagggtgc
600aggtgctgga tacaaatgac ttgagaccgt tattctcacc cacctcatac
agcgtttctt 660tacctgaaaa cacagctata aggaccagta tcgcaagagt
cagcgccacg gatgcagaca 720taggaaccaa cggggaattt tactacagtt
ttaaagatcg aacagatatg tttgctattc 780acccaaccag tggtgtgata
gtgttaactg gtacacttga ttacctagag accaagctct 840atgagatgga
aatcctcgct gcggaccgtg gcatgaagtt gtatgggagc agggcatcat
900gcagcatggc caagctaacg gtgcacatcg aacaggccaa tgaatgtgct
ccggtgataa 960cagcagtgac attgtcacca tcagaactgg acagggaccc
ggagcatatg caattgtgac 1020agtggatgac tgcatcaggg tccaatggtg
acatagcatc tttaagcatc gtggcaggtg 1080accttctcca gcagtttaga
acagtgaggt cctttccagg gagtaaggag tataaagtca 1140aagccatcgg
tggcattgat tgggacagtc atcctttcgg ctacaatctc acactacagg
1200ctaaagataa aggaactccg ccccagttct cttctgttaa agtcattcac
gtgacttctc 1260cacagttcaa agccgggcca gtcaagtttg aaaaggatgt
ttacagagca gaaataagtg 1320aatttgctcc tcccaacaca cctgtggtca
tggtaaaggc cattcctgct tattcccatt 1380tgaggtatgt ttttaaaagt
taacacctgg aaaagcaatt
cagtttaaat tacaacactg 1440gtctcatttc tattttagaa ccagttaaaa
gacagcaggc agcccatttt gaacttgaag 1500taacaacaag tgacagaaaa
gcgtccacca aggtcttggt gaaagtctta ggtgcaaata 1560gcaatccccc
tgaatttacc cagacagcgt acaaagctgc ttttgatgag aacgtgccca
1620ttggtactac tgtcatgagc ctgagtgccg tagaccctga tgagggtgag
aacgggtacg 1680tgacatacag tatcgcaaat ttaaatcatg tgccgtttgc
gattgaccat ttcactggtg 1740ccgtgagtac gtcagaaaac ctggactacg
aactgatgcc tcgggtttat actctgagga 1800ttcgtgcatc agactggggc
ttgccgtacc gccgggaagt cgaagtcctt gctacaatta 1860ctctcaataa
cttgaatgac aacacacctt tgtttcagaa aataaattgt gaagggacaa
1920ttcccagaga tctaggcgtg ggagagcaaa taaccactgt ttctgctatt
gatgcagatg 1980aacttcagtt ggtacagtat 2000441190DNAArtificial
SequenceSynthetic 44aaggtaggtc gactgaactt gatgagtcct ctctgaatca
cgggctctcg gctccgtgta 60ttttcagctc gggaaaatcg ctggggctgg gggtggggca
gtggggactt agcgagtttg 120ggggtgagtg ggatggaagc ttggctagag
ggatcaggag ttgcattgtt gggagacctg 180ggtgtagatg atggggatgt
taggaccatc cgaactcaaa gttgaacgcc taggcagagg 240agtggagctt
taccttgagc cggcctaaag cgtacttctt tgcacatcca cccggtgctg
300ggcgtaggga atccctgaaa taaaagcaca tgacggaggt tgtgaggcgc
tgcccccacc 360atgagcccat tgagactcaa tagcgtaata ttttagagcc
catggcatcc tagtgaaaac 420tggggctcca ttccgaaatg atcatttggg
ggtgatccgg ggagcccaag ctgctaaggt 480cccacaactt ccggaccttt
gtccttcctg gagcgatctt tccaggcagc ccccggctcc 540gctagatgga
gaaaatccaa ttgaaggctg tcagtcgtgg aagtgagaag tgctaaacca
600ggggtttgcc cgccaggccg aggaggaccg tcgcaatctg agaggcccgg
cagccctgtt 660attgtttggc tccacattta catttctgcc tcttgcagca
gcatttccgg tttctttttg 720ccggagcagc tcactattca cccgatgaga
ggggaggaga gagagagaaa atgtccttta 780ggccggttcc tcttacttgg
cagagggagg ctgctattct ccgcctgcat ttctttttct 840ggattactta
gttatggcct ttgcaaaggc aggggtattt gttttgatgc aaacctcaat
900ccctcccctt ctttgaatgg tgtgccccac cccgcgggtc gcctgcaacc
taggcggacg 960ctaccatggc gtgagacagg gagggaaaga agtgtgcaga
aggcaagccc ggaggtattt 1020tcaagaatga gtatatctca tcttcccgga
ggaaaaaaaa aaagaatggg tacgtctgag 1080aatcaaattt tgaaagagtg
caatgatggg tcgtttgata atttgtacct gttatctagc 1140tttgggctag
gccattccag ttccagacgc aggctggctt ttgttgcagg 11904561DNAArtificial
SequenceSynthetic 45attcataaac ccgccctttg taggaggaag cggtgaacca
cggggcgtgg tcattcgtgt 60c 61
* * * * *