U.S. patent application number 17/378404 was filed with the patent office on 2021-12-30 for neural network architectures for linking biological sequence variants based on molecular phenotype, and systems and methods therefor.
The applicant listed for this patent is Deep Genomics Incorporated. Invention is credited to Andrew DeLong, Brendan Frey.
Application Number | 20210407622 17/378404 |
Document ID | / |
Family ID | 1000005840010 |
Filed Date | 2021-12-30 |
United States Patent
Application |
20210407622 |
Kind Code |
A1 |
Frey; Brendan ; et
al. |
December 30, 2021 |
NEURAL NETWORK ARCHITECTURES FOR LINKING BIOLOGICAL SEQUENCE
VARIANTS BASED ON MOLECULAR PHENOTYPE, AND SYSTEMS AND METHODS
THEREFOR
Abstract
We describe a system and a method that ascertains the strengths
of links between pairs of biological sequence variants, by
determining numerical link distances that measure the similarity of
the molecular phenotypes of the variants. The link distances may be
used to associate knowledge about labeled variants to other
variants and to prioritize the other variants for subsequent
analysis or interpretation. The molecular phenotypes are determined
using a neural network, called a molecular phenotype neural
network, and may include numerical or descriptive attributes, such
as those describing protein-DNA interactions, protein-RNA
interactions, protein-protein interactions, splicing patterns,
polyadenylation patterns, and microRNA-RNA interactions. Linked
genetic variants may be used to ascertain pathogenicity in genetic
testing, to identify drug targets, to identify patients that
respond similarly to a drug, to ascertain health risks, or to
connect patients that have similar molecular phenotypes.
Inventors: |
Frey; Brendan; (Toronto,
CA) ; DeLong; Andrew; (Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Deep Genomics Incorporated |
Toronto |
|
CA |
|
|
Family ID: |
1000005840010 |
Appl. No.: |
17/378404 |
Filed: |
July 16, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15841106 |
Dec 13, 2017 |
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17378404 |
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PCT/CA2016/050689 |
Jun 15, 2016 |
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15841106 |
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14739432 |
Jun 15, 2015 |
10185803 |
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PCT/CA2016/050689 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 20/20 20190201;
G06N 3/04 20130101; G16B 20/00 20190201; G16B 40/20 20190201; G16B
40/00 20190201; G16B 30/00 20190201 |
International
Class: |
G16B 20/20 20060101
G16B020/20; G16B 30/00 20060101 G16B030/00; G16B 40/00 20060101
G16B040/00; G16B 40/20 20060101 G16B040/20; G06N 3/04 20060101
G06N003/04 |
Claims
1.-36. (canceled)
37. A non-transitory computer-readable medium comprising executable
instructions stored thereon that, when executed by a processor, are
operable to implement a method for determining numerical link
distances between two or more biologically related variants, the
method comprising: a. using an encoder to generate a set of input
values digitally representing a variant of the two or more
biologically related variants, wherein each of the two or more
biologically related variants is derived from a biological sequence
through a combination of substitutions, insertions, or deletions to
the biological sequence; b. obtaining at an input layer of a
trained molecular phenotype neural network (MPNN), the set of input
values generated by the encoder; c. processing the set of input
values by the trained MPNN to generate a set of numerical output
values representing a molecular phenotype for the variant, wherein
the molecular phenotype comprises numerical elements which quantify
biological molecules of cells; and d. determining, by a comparator,
a numerical link distance for pairs of variants of the two or more
biologically related variants at least in part by comparing the
numerical elements of the molecular phenotypes for the pairs of
variants.
38. The non-transitory computer-readable medium of claim 37,
wherein the biological sequence is a deoxyribonucleic acid (DNA)
sequence, a ribonucleic acid (RNA) sequence, or a protein
sequence.
39. The non-transitory computer-readable medium of claim 37,
wherein the set of input values corresponds to an encoded
representation of a set of contexts.
40. The non-transitory computer-readable medium of claim 37,
wherein the method further comprises using the input layer to
obtain an additional a set of values digitally representing a set
of contexts, wherein the molecular phenotype further comprises
numerical elements for at least one of the set of contexts.
41. The non-transitory computer-readable medium of claim 37,
wherein the method further comprises using the comparator to
determine the numerical link distance for a pair of variants at
least in part by applying a function to a difference between the
numerical elements of the molecular phenotypes for the pair of
variants.
42. The non-transitory computer-readable medium of claim 41,
wherein the function is selected from the group consisting of an
identity function, a square function, and an absolute value
function.
43. The non-transitory computer-readable medium of claim 37,
wherein at least one of the two or more biologically related
variants comprises: a. a DNA sequence, an RNA sequence, or a
protein sequence from an individual; b. a DNA sequence, an RNA
sequence, or a protein sequence which is modified by applying a DNA
editing system, an RNA editing system, or a protein modification
system; c. a DNA sequence, an RNA sequence, or a protein sequence
which is modified by setting one or more nucleotides which are
targeted by a therapy to fixed nucleotide values; d. a DNA
sequence, an RNA sequence, or a protein sequence which is modified
by setting one or more nucleotides which are targeted by a therapy
to values different from existing nucleotide values; or e. a DNA
sequence, an RNA sequence, or a protein sequence which is modified
by deleting one or more nucleotides which overlap with nucleotides
that are targeted by a therapy.
44. The non-transitory computer-readable medium of claim 37,
wherein the molecular phenotype comprises a numerical element
selected from the group consisting of: a percentage of transcripts
that include an exon; a percentage of transcripts that use an
alternative splice site; a percentage of transcripts that use an
alternative polyadenylation site; an affinity of an RNA-protein
interaction; an affinity of a DNA-protein interaction; a
specificity of a microRNA-RNA interaction; and a level of protein
phosphorylation.
45. The non-transitory computer-readable medium of claim 37,
wherein one or more variants of the two or more biologically
related variants are labeled variants, wherein the labeled variants
have associated labels, and wherein the method further comprises
using a labeling unit to obtain the numerical link distances for
the pairs of variants of the two or more biologically related
variants from the comparator, and associate labels with unlabeled
variants of the two or more biologically related variants based at
least in part on the numerical link distances.
46. The non-transitory computer-readable medium of claim 45,
further comprising associating each of the unlabeled variants with
the associated label of the labeled variant of the labeled variants
having a smallest numerical link distance to the unlabeled
variant.
47. The non-transitory computer-readable medium of claim 46,
wherein the unlabeled variants comprise at least two unlabeled
variants, wherein the labels comprise numerical values, and wherein
the method further comprises at least partially sorting the
unlabeled variants using at least one of the numerical values of
the labels.
48. The non-transitory computer-readable medium of claim 45,
wherein the method further comprises, for each of the unlabeled
variants and for each of the labeled variants, determining a
numerical weight for the unlabeled variant and the labeled variant
by applying a weighting module to the numerical link distance
between the unlabeled variant and the labeled variant; and
determining an associated label for the unlabeled variant by
summing terms corresponding to the labeled variants, wherein each
of the terms is obtained by multiplying the numerical weight for
the unlabeled variant and the corresponding labeled variant into
the associated label for the corresponding labeled variant.
49. The non-transitory computer-readable medium of claim 48,
wherein the method further comprises, for each of the unlabeled
variants and for each of the labeled variants, dividing the
numerical weight for the unlabeled variant and the labeled variant
by a sum of the weights for the unlabeled variant and the labeled
variant.
50. The non-transitory computer-readable medium of claim 37,
wherein the method further comprises using the comparator to
determine, for each of one or more pairs of variants in the two or
more biologically related variants, a measure of proximity of the
pair of variants within the biological sequence, wherein the
numerical link distance is determined at least in part by
processing the measure of proximity of the pair of variants with
the numerical elements of the molecular phenotypes for the pair of
variants.
51. The non-transitory computer-readable medium of claim 37,
wherein the weighting unit determines weights differently for
different values of the labels.
52. The non-transitory computer-readable medium of claim 37,
wherein the method further comprises using the comparator to
determine the numerical link distance at least in part by: using a
trained link neural network to process the numerical elements of
the molecular phenotypes for a pair of variants to determine the
numerical link distance for the pair of variants.
53. The non-transitory computer-readable medium of claim 52,
wherein the method further comprises using the trained link neural
network to process additional information pertaining to a
similarity of function of the pair of variants.
54. The non-transitory computer-readable medium of claim 52,
wherein a set of parameters of the trained link neural network are
determined at least in part by applying a training procedure to a
dataset of examples, wherein each of the examples comprises a pair
of variants and a target value for a link distance of the pair of
variants.
Description
CROSS-REFERENCE
[0001] This application is a continuation application of U.S.
application Ser. No. 15/841,106, filed Dec. 13, 2017, which is a
continuation application of PCT International Application No.
PCT/CA2016/050689, filed Jun. 15, 2016, which is a
continuation-in-part application of U.S. application Ser. No.
14/739,432, filed Jun. 15, 2015 (now U.S. Pat. No. 10,185,803,
issued Jan. 22, 2019), each of which is incorporated by reference
herein in its entirety.
TECHNICAL FIELD
[0002] The following relates generally to the field of genetic
variant analysis and the field of neural network architectures, and
more particularly to interpreting genetic variants to provide
molecular phenotype information in support of precision medicine,
genetic testing, therapeutic development, drug target
identification, patient stratification, health risk assessment and
connecting patients with rare disorders.
BACKGROUND
[0003] Precision medicine, genetic testing, therapeutic
development, drug target identification, patient stratification,
health risk assessment and connecting patients with rare disorders
can benefit from accurate information about how biological sequence
variants are different or are similar in their molecular
phenotypes.
[0004] Biological sequence variants, also called variants, impact
function by altering molecular phenotypes, which are aspects of
biological molecules that participate in biochemical processes and
in the development and maintenance of human cells, tissues, and
organs.
[0005] In the context of medicine and the identification and
understanding of genetic variants that cause disease, exonic
variants that change amino acids or introduce stop codons have
traditionally been the primary focus. Yet, since variants may act
by altering regulatory processes and changing a variety of
molecular phenotypes, techniques that focus on relating genetic
variants to changes in molecular phenotypes are valuable. Over the
past decade, this has led to molecular phenotype-centric approaches
that go beyond traditional exon-centric approaches. This change in
approach is underscored by several observations: while evolution is
estimated to preserve at least 5.5% of the human genome, only 1%
accounts for exons; biological complexity often cannot be accounted
for by the number of genes (e.g. balsam poplar trees have twice as
many genes as humans); differences between organisms cannot be
accounted for by differences between their genes (e.g. less than 1%
of human genes are distinct from those of mice and dogs);
increasingly, disease-causing variants have been found outside of
exons.
[0006] Analyzing how variants impact molecular phenotypes is
challenging. In traditional molecular diagnostics, an example
workflow may be as follows: a blood or tissue sample is obtained
from a patient; variants (mutations) are identified, such as by
sequencing the genome, sequencing the exome; running a gene panel;
or applying a microarray; the variants are manually examined for
their potential impact on molecular phenotype (e.g. by a
technician), using literature databases and internet search
engines; and a diagnostic report is prepared. Manually examining
the variants is costly and prone to human error, which may lead to
incorrect diagnosis and potential patient morbidity. Similar issues
arise in therapeutic design, where there is uncertainty about the
potential targets and their molecular phenotype mechanisms.
Insurance increasingly relies on variant interpretation to identify
disease markers and drug efficacy. Since the number of possible
variants is extremely large, evaluating them manually is
time-consuming, highly dependent on previous literature, and
involves experimental data that has poor coverage and therefore can
lead to high false negative rates, or "variants of uncertain
significance". Automating or semi-automating the analysis of
variants and their impact on molecular phenotypes is thus
beneficial.
SUMMARY
[0007] In one aspect, a system for linking two or more biologically
related variants derived from biological sequences is provided, the
system comprising: one or more molecular phenotype neural networks
(MPNNs), each MPNN comprising: an input layer configured to obtain
one or more values digitally representing a variant in the two or
more biologically related variants; one or more feature detectors,
each configured to obtain input from at least one of: (i) one or
more of the values in the input layer and (ii) an output of a
previous feature detector; and an output layer comprising values
representing a molecular phenotype for the variant, comprising one
or more numerical elements obtained from one or more of the feature
detectors; and a comparator linked to the output layer of each of
the one or more MPNNs, the comparator configured to compare the
molecular phenotypes for pairs of variants in the biologically
related variants to determine a numerical link distance for the
pairs of variants.
[0008] In another aspect, a method for linking two or more
biologically related variants derived from biological sequences is
provided, the method comprising: obtaining at an input layer of a
molecular phenotype neural network (MPNN), two or more digital
representations of the two or more biologically related variants,
each comprising one or more input values; processing each variant
by the MPNN, the MPNN comprising one or more feature detectors
configured to obtain input from at least one of: (i) the one or
more of the input values of the respective variant and (ii) an
output of a previous feature detector, the MPNN configured to
provide output values representing a molecular phenotype for the
variant, comprising one or more numerical elements obtained from
one or more of the feature detectors; for each of one or more pairs
of variants in the two or more biologically related variants,
determining, by a comparator, a numerical link distance, the
determining comprising comparing the molecular phenotypes for the
pair of variants.
[0009] The system may further comprise an encoder configured to
generate the digital representation of the variant, the input layer
being linked to an output of the encoder.
[0010] The encoder may further be configured to generate an encoded
representation of one or more contexts, wherein the input layer is
configured to obtain one or more values from the encoded
representation of the one or more contexts.
[0011] The input layer may additionally be configured to obtain an
additional one or more values digitally representing one or more
contexts, wherein the molecular phenotype further comprises one or
more numerical elements for each of one or more of the one or more
contexts.
[0012] For a pair of variants processed by the MPNN, the comparator
may determine the numerical link distance, by, for at least one of
the one or more numerical elements in the molecular phenotype,
applying one of the following linear or nonlinear functions to the
difference between the molecular phenotype for a first variant in
the pair of variants and the molecular phenotype for a second
variant in the pair of variants: the identity operation, the square
operation, and the absolute operation.
[0013] At least one of the variants in the two or more biologically
related variants may be obtained from: a DNA, an RNA or a protein
sequence of a patient; a sequence that would result when a DNA or
an RNA editing system is applied, or a protein modification system
is applied; a sequence where nucleotides targeted by a therapy are
set to fixed values; a sequence where nucleotides targeted by a
therapy are set to values other than existing values; and a
sequence where nucleotides that overlap, fully or partially, with
nucleotides that are targeted by a therapy are deactivated.
[0014] The molecular phenotype may comprise one or more of the
following elements: percentage of transcripts that include an exon;
percentage of transcripts that use an alternative splice site;
percentage of transcripts that use an alternative polyadenylation
site; the affinity of an RNA-protein interaction; the affinity of a
DNA-protein interaction; the specificity of a microRNA-RNA
interaction; the level of protein phosphorylation.
[0015] One or more variants in the two or more biologically related
variants may be labeled variants, wherein labeled variants have
associated labels, and the system may further comprise a labeling
unit configured to associate labels with other variants comprising
at least one variant in the two or more biologically related
variants that are not labeled variants.
[0016] The labeling unit may further be configured to associate
each other variant with the label of the variant in the labeled
variants that has the lowest link distance to the respective other
variant.
[0017] The number of other variants may be at least two, the labels
may be comprised of one or more numerical values, and the two or
more other variants may be sorted or partially sorted using one of
the one or more numerical values in the labels.
[0018] For each other variant in the other variants, the MPNN may
be configured to, for each labeled variant in the labeled variants,
determine a numerical weight for the other variant and the labeled
variant by applying a linear or a nonlinear weighting module to the
link distance for a pair of variants consisting of the other
variant and the labeled variant, and the labeling unit may be
configured to, for each other variant of the other variants,
determine an associated label by summing terms corresponding to the
labeled variants, wherein each term is obtained by multiplying the
numerical weight for the other variant and the corresponding
labeled variant into the label associated with the corresponding
labeled variant.
[0019] The MPNN may further be configured to, for each other
variant in the other variants and for each labeled variant in the
labeled variants, divide the numerical weight for the other variant
and the labeled variant by the sum of the weights for the other
variant and the labeled variants.
[0020] The number of other variants may be at least two and the
labels may be comprised of one or more numerical values, and the
system may be configured to sort or partially sort the two or more
other variants using one of the one or more numerical values in the
labels associated with the two or more other variants.
[0021] The system may further be configured to, for each of one or
more pairs of variants in the two or more biologically related
variants, obtain a measure of proximity of the pair of variants
within the biological sequence, wherein the determining a numerical
link distance further comprises combining the measure of proximity
of the pair of variants with the comparing of the molecular
phenotypes for the pair of variants.
[0022] The linear or the nonlinear weighting module may determine
weights differently for different values of the labels.
[0023] Comparing the molecular phenotypes for the pairs of variants
may comprise obtaining a link neural network, wherein the input of
the link neural network comprises the molecular phenotypes for each
pair of variants and wherein the output of the link neural network
is the link distance for the pair of variants; and applying the
link neural network to the molecular phenotypes for the pairs of
variants.
[0024] The system may further be configured to obtain additional
information pertaining to the similarity of function of the pair of
variants, wherein the input of the link neural network further
comprises the additional information.
[0025] The parameters of the link neural network may be determined
using a training procedure applied to a dataset of examples,
wherein each example comprises a pair of variants and a target
value for the link distance.
[0026] These and other aspects are contemplated and described
herein. It will be appreciated that the foregoing summary sets out
representative aspects of methods and systems for producing an
expanded training set for machine learning using biological
sequences to assist skilled readers in understanding the following
detailed description.
DESCRIPTION OF THE DRAWINGS
[0027] The features of the invention will become more apparent in
the following detailed description in which reference is made to
the appended drawings wherein:
[0028] FIG. 1A is a block diagram illustrating a first embodiment
of a system for linking biological sequence variants;
[0029] FIG. 1B is a block diagram illustrating a second embodiment
of a system for linking biological sequence variants;
[0030] FIG. 1C is a block diagram illustrating a third embodiment
of a system for linking biological sequence variants;
[0031] FIG. 1D is a block diagram illustrating a fourth embodiment
of a system for linking biological sequence variants;
[0032] FIG. 1E is a block diagram illustrating a fifth embodiment
of a system for linking biological sequence variants;
[0033] FIG. 2 is a block diagram illustrating a first example
architecture of a molecular phenotype neural network;
[0034] FIG. 3 is a block diagram illustrating a second example
architecture of a molecular phenotype neural network;
[0035] FIG. 4 is a block diagram illustrating a third example
architecture of a molecular phenotype neural network;
[0036] FIG. 5 is a block diagram illustrating a fourth example
architecture of a molecular phenotype neural network;
[0037] FIG. 6 is a block diagram illustrating a fifth example
architecture of a molecular phenotype neural network;
[0038] FIG. 7 is a block diagram illustrating labeling of
variants;
[0039] FIG. 8 is a block diagram illustrating weighting for
labeling of variants;
[0040] FIG. 9 is a block diagram illustrating the determination of
weights used for weighting for labeling of variants;
[0041] FIG. 10 is a second block diagram illustrating the
determination of weights used for weighting for labeling of
variants;
[0042] FIG. 11 is a block diagram showing a labeling unit; and
[0043] FIG. 12 is a flowchart showing a method for linking
biological sequence variants.
DETAILED DESCRIPTION
[0044] For simplicity and clarity of illustration, where considered
appropriate, reference numerals may be repeated among the Figures
to indicate corresponding or analogous elements. In addition,
numerous specific details are set forth in order to provide a
thorough understanding of the embodiments described herein.
However, it will be understood by those of ordinary skill in the
art that the embodiments described herein may be practised without
these specific details. In other instances, well-known methods,
procedures and components have not been described in detail so as
not to obscure the embodiments described herein. Also, the
description is not to be considered as limiting the scope of the
embodiments described herein.
[0045] Various terms used throughout the present description may be
read and understood as follows, unless the context indicates
otherwise: "or" as used throughout is inclusive, as though written
"and/or"; singular articles and pronouns as used throughout include
their plural forms, and vice versa; similarly, gendered pronouns
include their counterpart pronouns so that pronouns should not be
understood as limiting anything described herein to use,
implementation, performance, etc. by a single gender; "exemplary"
should be understood as "illustrative" or "exemplifying" and not
necessarily as "preferred" over other embodiments. Further
definitions for terms may be set out herein; these may apply to
prior and subsequent instances of those terms, as will be
understood from a reading of the present description.
[0046] Any module, unit, component, server, computer, terminal,
engine or device exemplified herein that executes instructions may
include or otherwise have access to computer readable media such as
storage media, computer storage media, or data storage devices
(removable and/or non-removable) such as, for example, magnetic
disks, optical disks, or tape. Computer storage media may include
volatile and non-volatile, removable and non-removable media
implemented in any method or technology for storage of information,
such as computer readable instructions, data structures, program
modules, or other data. Examples of computer storage media include
RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices, or any other medium which can be used to store the
desired information and which can be accessed by an application,
module, or both. Any such computer storage media may be part of the
device or accessible or connectable thereto. Further, unless the
context clearly indicates otherwise, any processor or controller
set out herein may be implemented as a singular processor or as a
plurality of processors. The plurality of processors may be arrayed
or distributed, and any processing function referred to herein may
be carried out by one or by a plurality of processors, even though
a single processor may be exemplified. Any method, application or
module herein described may be implemented using computer
readable/executable instructions that may be stored or otherwise
held by such computer readable media and executed by the one or
more processors.
[0047] A key unmet need in precision medicine is the ability to
automatically or semi-automatically analyze biological sequence
variants by examining their impact on molecular phenotypes.
[0048] The following provides systems and methods for determining
links between biological sequence variants, also called variants,
to other variants and generating scores for the strengths of the
link between two variants according to the similarity in their
molecular phenotypes. The systems generally comprise neural network
architectures that are referred to herein as "molecular phenotype
neural networks". The biological sequence may be a DNA sequence, an
RNA sequence, or a protein sequence. Linked variants may be used in
precision medicine to ascertain pathogenicity in genetic testing,
to identify drug targets, to identify patients that respond
similarly to a drug, to ascertain health risks, and to connect
patients that have similar molecular phenotypes.
[0049] A biological sequence variant, also called a variant, is a
biological sequence, such as a DNA sequence, an RNA sequence or a
protein sequence, that may be derived from an existing biological
sequence through a combination of substitutions, insertions and
deletions. For example, the gene BRCA1 is represented as a specific
DNA sequence of length 81,189 in the reference genome. If the
samples from multiple patients are sequenced, then multiple
different versions of the DNA sequence for BRCA1 may be obtained.
These sequences, together with the sequence from the reference
genome, form a set of variants.
[0050] To distinguish variants that are derived from the same
biological sequence from those that are derived from different
biological sequences, the following will refer to variants that are
derived from the same biological sequence as "biologically related
variants" and the term "biologically related" is used as an
adjective to imply that a variant is among a set of biologically
related variants. For example, the variants derived from the gene
BRCA1 are biologically related variants. The variants derived from
another gene, SMN1, are also biologically related variants.
However, the variants derived from BRCA1 are not biologically
related to the variants derived from SMN1. The term "biologically
related variants" is used to organize variants according to their
function, but it will be appreciated that this organization may be
different according to different functions. For example, when they
are transcribed, two different but homologous genes may generate
the same RNA sequence. Variants in the RNA sequence may impact
function in the same way, such as by impacting RNA stability. This
is the case even though they originated from two different, albeit
homologous, DNA sequences. The RNA sequence variants, regardless of
from which gene they came, may be considered to be biologically
related.
[0051] Biologically related variants may be derived naturally by
DNA replication error; by spontaneous mutagenesis; by sexual
reproduction; by evolution; by DNA, RNA and protein
editing/modification processes; by retroviral activity, and by
other means. Biologically related variants may be derived
experimentally by plasmid construction, by gene editing systems
such as CRISPR/Cas9, by sequencing samples from patients and
aligning them to a reference sequence, and by other means.
Biologically related variants may be derived computationally by
applying a series of random or preselected substitutions,
insertions and deletions to a reference sequence, by using a model
of mutation to generate variants, and by other means. Biologically
related variants may be derived from a DNA or RNA sequence of a
patient, a sequence that would result when a DNA or RNA editing
system is applied, a sequence where nucleotides targeted by a
therapy are set to fixed values, a sequence where nucleotides
targeted by a therapy are set to values other than existing values,
or a sequence where nucleotides that overlap, fully or partially,
with nucleotides that are targeted by a therapy are deactivated. It
will be appreciated that there are other ways in which biologically
related variants may be produced.
[0052] Depending on the function being studied, different sets of
biologically related variants may be obtained from the same
biological sequences. In the above example, DNA sequences for the
BRCA1 gene of length 81,189 may be obtained from the reference
genome and a group of patients and form a set of biologically
related variants. As an example, if we are interested in how
variants impact splicing of exon 6 in BRCA1, for each patient and
the reference genome, we may extract a subsequence of length 600
nucleotides centered at the 3 prime end of exon 6. These splice
site region sequences would form a different set of biologically
related variants than the set of whole-gene biologically related
variants.
[0053] The above discussion underscores that the functional meaning
of a variant is context dependent, that is, dependent on the
conditions. Consider the reference genome and an intronic single
nucleotide substitution located 100 nucleotides from the 3 prime
splice site of exon 6 in the BRCA1 gene. We can view this as two
BRCA1 variants of length 81,189 nucleotides, or as two exon 6
splice site region variants of length 600 nucleotides, or, in the
extreme, as two chromosome 17 variants of length 83 million
nucleotides (BRCA1 is located on chromosome 17). Viewing the single
nucleotide substitution in these three different situations would
be important for understanding its impact on BRCA1 gene expression,
BRCA1 exon 6 splicing, and chromatin interactions in chromosome 17.
Furthermore, consider the same single nucleotide substitution in
two different patients. Because the neighbouring sequence may be
different in the two patients, the variants may be different.
[0054] A variant impacts function by altering one or more molecular
phenotypes, which quantify aspects of biological molecules that
participate in the biochemical processes that are responsible for
the development and maintenance of human cells, tissues, and
organs. A molecular phenotype may be a quantity, level, potential,
process outcome, or qualitative description. The term "molecular
phenotype" may be used interchangeably with the term "cell
variable". Examples of molecular phenotypes include the
concentration of BRCA1 transcripts in a population of cells; the
percentage of BRCA1 transcripts that include exon 6; chromatin
contact points in chromosome 17; the strength of binding between a
DNA sequence and a protein; the strength of interaction between two
proteins; DNA methylation patterns; RNA folding interactions; and
inter-cell signalling. A molecular phenotype can be quantified in a
variety of ways, such as by using a categorical variable, a single
numerical value, a vector of real-valued numbers, or a probability
distribution.
[0055] A variant that alters a molecular phenotype is more likely
to alter a gross phenotype, such as disease or aging, than a
variant that does not alter any molecular phenotype. This is
because variants generally impact gross phenotypes by altering the
biochemical processes that rely on DNA, RNA and protein
sequences.
[0056] Since variants impact function by altering molecular
phenotypes, a set of biologically related variants can be
associated with a set of molecular phenotypes. BRCA1 whole-gene
variants may be associated with the molecular phenotype measuring
BRCA1 transcript concentration. BRCA1 exon 6 splice site region
variants may be associated with the molecular phenotype measuring
the percentage of BRCA1 transcripts that include exon 6. Chromosome
17 variants may be associated with the molecular phenotype
measuring chromatin contact points in chromosome 17. This
association may be one to one, one to many, many to one, or many to
many. For instance, BRCA1 whole-gene variants, BRCA1 exon 6 splice
region variants and chromosome 17 variants may be associated with
the molecular phenotype measuring BRCA1 transcript
concentration.
[0057] The association of a variant with a molecular phenotype does
not imply for certain that the variant alters the molecular
phenotype, it only implies that it may alter the molecular
phenotype. An intronic single nucleotide substitution located 100
nucleotides from the 3 prime splice site of exon 6 in the BRCA1
gene may alter the percentage of BRCA1 transcripts that include
exon 6, whereas a single nucleotide substitution located 99
nucleotides from the 3 prime splice site of exon 6 in the BRCA1
gene may not. Also, for the former case, whereas a G to T
substitution may alter the molecular phenotype, a G to A
substitution may not. Furthermore, the molecular phenotype may be
altered in one cell type, but not in another, even if the variant
is exactly the same. This is another example of context
dependence.
[0058] The systems and methods described herein can be used to
compare biologically related variants to one another by examining
how they alter one or more associated molecular phenotypes. For
example, the variants consisting of 600 nucleotides centered at the
3 prime end of exon 6 of BRCA1 obtained from a set of patients can
be compared by examining how they alter the percentage of BRCA1
transcripts that include exon 6. If two variants cause the
percentage of BRCA1 transcripts that include exon 6 to change in a
similar way, the variants are more likely to be functionally
related than if they cause the percentage of BRCA1 transcripts that
include exon 6 to change in a different way.
[0059] There are different approaches to determining how variants
alter the same molecular phenotype, ranging from experimental, to
computational, to hybrid approaches.
[0060] The present systems comprise structured computational
architectures referred to herein as molecular phenotype neural
networks (MPNNs). MPNNs are artificial neural networks, also called
neural networks, which are a powerful class of architectures for
applying a series of computations to an input so as to determine an
output. The input to the MPNN is used to determine the outputs of a
set of feature detectors, which are then used to determine the
outputs of other feature detectors, and so on, layer by layer,
until the molecular phenotype output is determined. An MPNN
architecture can be thought of as a configurable set of processors
configured to perform a complex computation. The configuration is
normally done in a phase called training, wherein the parameters of
the MPNN are configured so as to maximize the computation's
performance on determining molecular phenotypes or, equivalently,
to minimize the errors made on that task. Because the MPNN gets
better at a given task throughout training, the MPNN is said to be
learning the task as training proceeds. MPNNs can be trained using
machine learning methods. Once configured, an MPNN can be deployed
for use in the task for which it was trained and herein for linking
variants as described below.
[0061] Referring now to FIG. 1A, a system (100) comprises an MPNN
(101) that is a neural network comprising a layer of input values
that represents the variant (103) (which may be referred to as an
"input layer"), one or more layers of feature detectors (102) and a
layer of output values that represents the molecular phenotype
(105) (which may be referred to as an "output layer"). Each layer
of feature detectors (102, 102', 102'') comprises one or more
feature detectors (104), wherein each feature detector comprises or
is implemented by a processor. Weights may be applied in each
feature detector (104) in accordance with learned weighting, which
is generally learned in a training stage of the neural network. The
input values, the learned weights, the feature detector outputs and
the output values may be stored in a memory (106) linked to the
MPNN (101).
[0062] The particular MPNN (101) shown in FIG. 1A is an example
architecture; the particular links between the feature detectors
(104) may differ in various embodiments, which are not all depicted
in the figures. A person of skill in the art would appreciate that
such embodiments are contemplated herein. As an example, FIG. 1C
and FIG. 1D show example MPNNs having one layer (102) of feature
detectors (104).
[0063] Each layer (102, 102', 102'') of feature detectors comprises
the structured determination of the output of the feature detectors
(104), and each feature detector (104) implements a computation
that maps an input to an output. The feature detectors (104) in a
layer accept a plurality of inputs from previous layers, combine
them with a subset of weights, or parameters, W, and apply
activation functions. Generally, the output of a feature detector
in layer 1 may be provided as input to one or more feature
detectors in layers l+1, l+2, . . . , L, where L is the number of
layers of feature detectors. For example, in FIG. 1A, outputs of
feature detectors (104) of layer (102) may be provided as input to
one or more feature detectors (104) of a plurality of subsequent
layers (102' and 102'').
[0064] One or more feature detectors (104) may be implemented by
processing hardware, such as a single or multi-core processor
and/or graphics processing unit(s) (GPU(s)). Further, it will be
understood that each feature detector (104) may be considered to be
associated with an intermediate computation or an input of the
neural network for an intermediate layer or an input layer,
respectively. The use of large (many intermediate computations) and
deep (multiple layers of computations) neural networks may improve
the predictive performances of the MPNN compared to other
systems.
[0065] As will be explained further, the systems and methods
described herein use the MPNN to determine the molecular phenotypes
of one or more pairs of biologically related variants, wherein the
two variants in each pair will be referred to as variant t and
variant r. The two corresponding molecular phenotypes are denoted
m.sup.t and m.sup.r respectively. It may be advantageous for the
system 100 to comprise a further MPNN (101'), wherein the further
MPNN is identically trained and configured as the first MPNN (101).
This may be the case, for example, where the cost of obtaining
processors is low, the desire for increased speed is high and/or it
is advantageous to perform variant analysis on the test variant and
reference variant simultaneously. Alternatively, a single MPNN may
be provided and the variants analysed one after the other, with the
output of the first analysis being buffered at buffer (109) until
the output of the second analysis is available.
[0066] The two molecular phenotypes m.sup.t and m.sup.r are
analyzed using a comparator (108), which determines the link
distance for the two variants, d.sup.tr. It will be appreciated
that when processing links between one variant and multiple other
biologically related variants, the molecular phenotype of the one
variant may be determined by one application of the MPNN, stored,
and then fed into the comparator along with the molecular phenotype
for every one of the multiple other biologically related variants.
It will also be appreciated that when processing links between
variants in a first set of variants and variants in a second set of
variants, all of the molecular phenotypes of the variants in the
first and second set of variants may be determined by applying the
MPNN and then stored at buffer (109), and then the comparator may
be applied to every pair of variants consisting of one variant from
the first set of variants and one variant from the second set of
variants.
[0067] Returning now to the MPNN (101 and 101'), MPNN can operate
in two modes: the forward-propagation mode and the back-propagation
mode. In the forward-propagation mode, the MPNN takes as input X,
applies a series of computations resulting in intermediate values
Z, and then applies computations to ascertain the output Y. The
quantities X, Y and Z may each be a scalar value, a vector of
values, or a set of values. The MPNN is configurable and its
configuration is represented by parameters W=(w.sub.1, . . . ,
w.sub.p), where p is the number of parameters. For any choice of
configuration W, we denote the output Y ascertained by the MPNN by
Y=F(X; W), where F defines the architecture of the MPNN.
[0068] As shown in the system depicted in FIG. 1A, an MPNN takes as
input a biological sequence and may also take as input a
specification of the context. It then applies a structured series
of computations, and outputs a numerical description of the
molecular phenotype, which may comprise one or more numerical
values or other information. The specification of the context may
encode cell types, pairs of cell types, tissue types, age, sex,
known biomarkers, patterns of behaviour, blood chemistry, and other
environmental factors. It may also encode sequence context, such as
the chromosome, gene or exon from which the input biological
sequence was obtained. As shown in the system depicted in FIG. 1E,
on the other hand, the MPNN may not take as input a context. The
MPNN is configurable and its configuration is determined by a set
of parameters using machine learning training. The MPNN can be
applied to a set of biologically related variants to determine the
corresponding variant molecular phenotypes.
[0069] MPNNs can be used to evaluate a variety of molecular
phenotypes. In one example, an MPNN could take as input a sequence
of 600 nucleotides centered at the 3 prime splice site of exon 6 in
the BRCA1 gene and a specification of tissue type, and output the
percentage of BRCA1 transcripts in that tissue type that include
exon 6.
[0070] Examples of molecular phenotypes that may be predicted using
MPNNs include exon inclusion levels/percentages, alternative splice
site selection probabilities/percentages, alternative
polyadenylation site selection probabilities/percentages for a
transcript, affinity of an RNA-protein or DNA-protein interaction,
RNA- or DNA-binding protein specificities, microRNA specificities,
specificity of microRNA-RNA interaction, the level of protein
phosphorylation, phosphorylation patterns, the distribution of
proteins along a strand of DNA containing a gene, the number of
copies of a gene (transcripts) in a cell, the distribution of
proteins along the transcript, and the number of proteins.
[0071] The system (100) may further comprise an encoder (107)
functionally coupled to the input layer of the MPNN so that
biological sequences, which are discrete-symbol sequences, can be
encoded numerically and used as inputs to the MPNN. The encoder may
further encode the context to be input to the MPNN. It may be
advantageous for the system 100 to comprise a further encoder
(107'), wherein the further encoder is identical to the first
encoder (107). This may be the case, for example, where the cost of
obtaining processors is low, the desire for increased speed is high
and/or it is advantageous to perform variant analysis on the test
variant and reference variant simultaneously. Alternatively, a
single encoder may be provided and the biological sequence and the
context may be encoded one after the other, with the output of the
first analysis being buffered at buffer (110) until the output of
the second analysis is available. It will be appreciated that the
encoder may be applied in different ways and that an encoder may
not be used at all, as depicted in FIG. 1B and FIG. 1D.
[0072] The encoder may, for example, encode the sequence of symbols
in a sequence of numerical vectors (a vector sequence) using
one-hot encoding. Suppose the symbols in the sequence come from an
alphabet =(.alpha..sub.1, . . . , .alpha..sub.k) where there are k
symbols. The symbol s.sub.i at position i in the sequence is
encoded into a numerical vector x.sub.i of length k:
x.sub.i=(x.sub.i,1, . . . , x.sub.i,k) where
x.sub.i,j=[s.sub.i=.alpha..sub.j] and [ ] is defined such that
[True]=1 and [False]=0 (so called Iverson's notation). One-hot
encoding of all of the biological sequence elements produces an
m.times.r matrix X. For example, a DNA sequence CAAGTTT of length
n=7 and with an alphabet =(A, C, G, T), such that k=4, would
produce the following vector sequence:
X = ( 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 ) .
##EQU00001##
Such an encoding is useful for representing biological sequences as
numeric inputs to the neural network. It will be appreciated that
other encodings of X may be computed from linear or non-linear
transformations of a one-hot encoding, so long as the transformed
values are still distinct, or that other encodings may be used.
[0073] The MPNN also takes as input a specification of context,
which may be numerical, categorical or an additional sequence. The
specification of context may also in part be encoded by the encoder
using, for example, a one-hot encoding scheme.
[0074] It is useful to compare the output of a MPNN Y to a desired
output or target, Y'. The molecular phenotype target may be
ascertained using experimental techniques, such as RNA-Seq,
ChIP-Seq, microarrays, RT-PCR, SELEX, and massively parallel
reporter assays. This is useful for training, in which the MPNN is
configured using the parameters W such that, for input-target pairs
(X, Y') in the training set of many such input-target pairs, the
MPNN's output Y=F(X; W) is a good approximation of the training
target Y', across the input-output pairs in the training set. The
error or cost between the MPNN output Y and a target Y' can be
quantified by, for example, the squared error (Y-Y').sup.2. It will
be appreciated that different error or cost functions may be used.
The error term is incorporated into a loss function L(X, Y'; W),
which measures the discrepancy between the output of the MPNN and
the desired output. In the example, L(X, Y'; W)=(F(X; W)-Y').sup.2.
The process of training involves configuring W so as to minimize
the total loss in a training set, such as the sum over the training
examples of the loss for each example. Training may consist of
determining a configuration W that minimizes or approximately
minimizes the expected value or sum of L for pairs (X, Y') sampled
from either training set or from a held-out validation set.
[0075] Alternatively or additionally, the MPNN may be operated in
the back-propagation mode, which is used to determine how changes
in the intermediate computations, the inputs, and the parameters
will impact the output of the MPNN. These three types of changes
are called gradients or derivatives and are denoted
.differential.Y/.differential.Z, .differential.Y/.differential.X
and .differential.Y/.differential.W respectively. Note that while Z
is not explicit in the input-output relationship Y=F(X; W), the
output depends on the intermediate computations and so the gradient
of the output with respect to the values produced by the
intermediate computations can be determined. These gradients are
useful for training.
[0076] An MPNN operating in back-propagation mode is a structured
architecture that comprises a series of computations in a
structured framework. First, the MPNN is operated in the
forward-propagation mode to compute Y=F(X;W). Then, the MPNN is
operated in the back-propagation mode, which comprises a series of
computations that starts with the output of the MPNN and works its
way back to the input, so as to determine the gradients
.differential.Y/.differential.Z, .differential.Y/.differential.X
and .differential.Y/.differential.W for values produced by all of
the intermediate computations Z, for all inputs X, and for all
parameters W.
[0077] MPNNs are configured by training their parameters using one
or more neural network training procedures applied to a training
dataset. The dataset consists of examples of biological sequences,
specifications of context, and corresponding molecular phenotypes.
An important aspect of MPNNs is their ability to generalize to new
conditions, that is, to biological sequences and contexts that are
not in the training dataset. This aspect enables MPNNs to determine
the molecular phenotypes of variants that are not in the training
dataset or to variant-context combinations that are not in the
training dataset.
[0078] In one example, an MPNN takes as input a subsequence of
length 600 nucleotides centered at the 3 prime end of exon 6 in
BRCA1 (a splice site region variant), and a one-hot encoding of the
cell type, and through a structured series of computations
determines the percentage of BRCA1 transcripts that include exon 6.
This MPNN may have been trained using BRCA1 exon 6 splice region
variants and corresponding measurements of splicing percentages,
obtained by DNA and RNA sequencing of patients. This MPNN can be
used to analyze BRCA1 exon 6 splice site region variants. It can
also be used to analyze splice site region variants from other
exons in BRCA1 and even for other exons in other genes, but it may
not be accurate in these cases because it was trained using only
data for exon 6 in BRCA1.
[0079] In another example, an MPNN takes as input a subsequence of
length 600 nucleotides centered at the 3 prime end of any exon in
the human genome, and a one-hot encoding of the cell type, and
through a structured series of computations determines the
percentage of transcripts that include the exon, out of all those
transcripts generated from the gene containing the exon. This MPNN
may have been trained using splice region variants from chromosomes
1 to 10 and corresponding measurements of splicing percentages,
obtained by DNA and RNA sequencing of a single healthy individual.
This MPNN can be used to analyze BRCA1 exon 6 splice site region
variants, but it can also be used to analyze splice site region
variants from other exons in BRCA1 and for other exons in other
genes. Even though it was trained using data for chromosomes 1 to
10, it may generalize well to the other chromosomes.
[0080] In another example, an MPNN takes as input a subsequence of
length 600 nucleotides centered at the 3 prime end of any exon in
the human genome, and a one-hot encoding of the cell type, and a
one-hot encoding of the gene in which the exon is located, and
through a structured series of computations determines the
percentage of transcripts that include the exon, out of all those
transcripts generated from the gene containing the exon. By
providing the gene identity as input to the MPNN, the MPNN may
account for gene-specific effects on the molecular phenotype, as
well as for gene-independent effects.
[0081] The MPNN examples described above may all be implemented by
the same or possibly different MPNN structures; that is, the
number, composition and parameters of the nodes and layers may or
may not differ. It will be appreciated that the biological
sequences need not be of the same length and that an MPNN may be
trained to account for other molecular phenotypes, for other
biologically related variants and for other specifications of
context.
[0082] The MPNN may be configured in different ways such as to use
a discriminative neural network, a convolutional neural network, an
autoencoder, a multi-task neural network, a recurrent neural
network, a long short-term memory neural network, or a combination
thereof. It will also be appreciated that many different machine
learning architectures can be represented as neural networks,
including linear regression, logistic regression, softmax
regression, decision trees, random forests, support vector machines
and ensemble models. Differences between techniques and
architectures often pertain to differences in the cost functions
and optimization procedures used to configure the architecture
using a training set.
[0083] It will also be appreciated that the MPNN may also take as
input a vector of features that are derived from the variant
sequence. Examples of features include locations of protein binding
sites, RNA secondary structures, chromatin interactions, and
protein structure information.
[0084] It will be appreciated that the MPNN may be applied to a set
of variants to determine the molecular phenotypes of the variants
in the set of variants.
[0085] Since biologically related variants may be derivable from a
reference sequence, in another embodiment, the MPNN is used to
determine the molecular phenotype of a variant as it relates to the
molecular phenotype of the reference sequence. For example,
consider an MPNN that is configured to determine the percentage of
transcripts that include exon 6 of BRCA1 using the 600 nucleotide
sequence centered at the 3 prime end of the exon. The MPNN may be
applied to the reference sequence extracted from the reference
genome, and also to the variants from the patient. The percentage
value for the reference genome may be subtracted from the
percentage values for the patients, resulting in variant molecular
phenotypes that measure the change in the percentage. It will be
appreciated that the comparison of the variant and the reference
sequence may be performed in different ways, including using the
difference, the absolute difference and the squared difference. For
multi-valued molecular phenotypes, the sum of the differences, the
sum of the absolute differences and the sum of the squared
differences may be used. For probability distributions,
Kullback-Leibler divergence may be used. For example, if the output
of the MPNN is a probability distribution over a discrete variable,
the variant molecular phenotype may be computed using the
Kullback-Leibler divergence between the probability distribution
ascertained from the variant and the reference sequence. It will be
appreciated that more than one reference sequence may be used and
the comparison may be adjusted accordingly, such as by determining
the maximum or the average of the differences between the outputs
for the variant and the references. It will be appreciated that the
one or more reference sequences may be obtained in different ways,
such as by sequencing the DNA from one or more close relatives of
the patient; by examining the reference genome, the reference
transcriptome or the reference proteome; by sequencing a gene using
a sample from a patient's tumour; or by sequencing the gene using a
sample from an unaffected tissue in the same patient.
[0086] Unlike many existing systems, the methods and systems
described herein can be used to analyze variants in different
contexts. For instance, when a child's variant is compared to a
reference sequence obtained from the reference human genome, the
MPNN may produce a large variant-induced molecular phenotype,
indicating that the variant may be disease causing. But, when the
same variant is compared to the reference sequences obtained from
his or her unaffected parents, the MPNN may produce a low
variant-induced molecular phenotype, indicating that the variant
may not be disease causing. In contrast, if the MPNN produces a
large variant-induced molecular phenotype when the parents'
sequences are used as the reference, then the variant is more
likely to be the cause of the disease.
[0087] Another circumstance in which different reference sequences
arise is when the variant may be present in more than one
transcript, requiring that the impact of the variant be ascertained
in a transcript-dependent fashion. Also, since the MPNN takes as
input a description of the environment, such as a one-hot encoding
of the cell type, the variant-induced molecular phenotype can
depend on the context as established by the environment. A variant
may not induce a molecular phenotype in a liver cell, but induce a
large molecular phenotype in a brain cell.
[0088] FIG. 12 illustrates a flowchart that summarizes the above
steps performed by system 100. A method (1200) for linking two or
more biologically related variants derived from biological
sequences comprises: at block 1202, each of two or more digital
representations of the two or more biologically related variants
may be generated by the encoder; at block 1204, digital
representations of the one or more contexts may be generated by the
encoder; at block 1206, obtaining at an input layer of a molecular
phenotype neural network (MPNN), each of the two or more digital
representations of the two or more biologically related variants,
each comprising one or more input values digitally representing a
variant and, possibly, the one or more contexts; at block 1208,
processing each variant by the MPNN, the MPNN comprising one or
more feature detectors configured to obtain input from at least one
of: (i) the one or more of the input values of the respective
variant and (ii) an output of a previous feature detector, the MPNN
configured to provide output values representing a molecular
phenotype for the variant, comprising one or more numerical
elements of one or more of the feature detectors; at block 1210,
for each of one or more pairs of variants in the two or more
biologically related variants, determining, by a comparator, a
numerical link distance comprising comparing the molecular
phenotypes for the pair of variants.
[0089] Referring now to FIG. 2, shown therein is an example
architecture (200) of an MPNN that has a layer of input values that
represent genomic features (206) that are DNA sequences, encoded
DNA sequences, or other features derived from DNA sequences,
wherein the DNA sequences containing an exon, the neighbouring
introns and the neighbouring exons as well as the annotated splice
junctions. The layer of input values also includes a specification
of the context in the form of the tissue index (218). In this
example, where are three layers of feature detectors (208, 210 and
212). In this example, using these layers of feature detectors, the
MPNN processes the inputs through three layers of feature detectors
(208, 210, 212) that apply a structured series of computations to
determine an output (214), which in this example is the percentage
of transcripts that include the exon .PSI., at the output layer.
This MPNN may be viewed as a regression model. The input values
representing genomic features comprise input to the first layer of
feature detectors (208). In this example, the input values
representing the tissue index (218) and the outputs of the feature
detector from the first layer of feature detectors (208) comprise
the inputs to the second layer of feature detectors (210). The
outputs of the second layer of feature detectors (210) comprise the
inputs to the third and final layer of feature detectors (212). The
outputs of the third and final layer of feature detectors (212) are
the molecular phenotype values (214). It will be appreciated that
different architectures may be used. For example, the input values
representing the tissue index (218) may be inputs to the first
layer of feature detectors (208) and the first layer of feature
detectors may be the final layer of feature detectors and the
outputs of the first layer of feature detectors may be the
molecular phenotype values (214). For example, there may be more
than three layers of feature detectors. The values in the input
layer may be inputs to the second and third layers of feature
detectors. It will be appreciated that values in the input layer
may be derived in different ways or encoded in different ways. For
example, the values in the input layer (206) may include binding
specificities of RNA- and DNA-binding proteins, RNA secondary
structures, nucleosome positions, position-specific frequencies of
short nucleotide sequences, and many others. The context (e.g.,
tissue index) may also be derived or encoded in different ways,
such as by using an encoder (not shown), which encodes the tissue
index i using a 1-of-TT binary vector where TT represents the
number of conditions and the values in the vector are zero
everywhere except at the position indicating the condition, where
the value is one. This is called one-hot encoding.
[0090] FIG. 3 shows another example where the input values
representing context (204) along with the input values representing
genomic features comprise inputs to the first layer of feature
detectors (208)
[0091] Referring now to FIG. 4, it will be appreciated that the
molecular phenotype may be represented in different ways. Instead
of determining a real-valued .PSI. in the form of a percentage, the
MPNN may output probabilities over discrete molecular phenotype
categories. For example, the percentage may be binned into low
(between 0 and 33%), medium (34% to 66%) and high (67% to 100%),
and the output of the MPNN may be three real numbers between zero
and one that add up to one: p.sub.low, p.sub.med, p.sub.high. The
molecular phenotype targets for training this MPNN may be one-hot
encoded vectors, (1,0,0), (0,1,0) and (0,0,1), or probabilities
distributions that take into account measurement noise. For these
discretized molecular phenotype values, the cross entropy cost
function or the log-likelihood performance measure can be used for
training.
[0092] Referring now to FIG. 5, it will be appreciated that instead
of encoding the context as an input to the MPNN, the MPNN may
output a different molecular phenotype value for each context.
Here, the MPNN determines the percentage of transcripts that
include the exon for every one of the T tissue types. These T
numerical values together comprise the molecular phenotype. It will
be appreciated that hybrid approaches are possible, where part of
the context is provided as input and the molecular phenotype is
provided for every other aspect of the context. Referring now to
FIG. 6, for example, the age of the patient may be provided as an
input to the MPNN, and the MPNN may provide a molecular phenotype
value for each of T different tissue types, such as heart, muscle,
tissue, etc.
[0093] Referring back to FIG. 1A, in the training phase, the MPNN
(101) can be configured by adjusting its parameters using a dataset
of biological sequences, specifications of context, and
corresponding molecular phenotypes. This comprises establishing an
MPNN and then repeatedly updating the one or more parameters, or
weights, of the MPNN so as to decrease the error between the
molecular phenotypes determined using the MPNN and the measured
molecular phenotypes, until a condition for convergence is met at
which point the parameters are no longer updated. It will be
appreciated that instead of decreasing the error, the objective may
be to decrease another loss function such as cross entropy, or to
maximize an objective function, such as log-likelihood. The
resulting parameters, or weights, are then stored in the memory
(106) such that the MPNN parameters can be reused during
application to analyze variants. At each step of the updating of
one or more parameters, the entire batch of data may be used, or a
subset of examples called a minibatch may be used, the examples in
the minibatch being selected randomly or by using a predetermined
pattern.
[0094] Referring again to FIG. 1A, embodiments comprising a
comparator (108) can be used to link variants by using MPNNs to
determine the variant molecular phenotypes and then, for any two
variants, determining a link distance by comparing their molecular
phenotypes. These link distances are used to identify, score,
prioritize or rank the variants. Knowledge about one variant can be
associated with another variant by examining the link distance.
Knowledge may include English language descriptions,
interpretations and mechanistic explanations; functional
annotations; and literature references.
[0095] For two variants, the comparator may determine the link
distance as a numerical value indicating the strength of the link
between the two variants, where a strong link has a low link
distance and a weak link has a high link distance. The link
distances between a test variant and multiple established variants
can further be compared to identify which established variants are
most strongly linked to the test variant.
[0096] In conjunction with link distances, the term
"prioritization" is used herein to refer to the process of
producing a sorted list of variants to identify the order in which
variants should be examined, processed, classified, or otherwise
considered for further analysis.
[0097] In one embodiment, for one or more pairs of biologically
related variants, the MPNN is used to determine the variant
molecular phenotype for every variant. The comparator determines
link distance between the variants in each pair by summing the
output of a nonlinear function applied to the difference between
the molecular phenotypes for the two variants. The nonlinear
function may be the square operation. The nonlinear function may be
the absolute operation.
[0098] In one embodiment, the link distance between a pair of
variants t and r for context c is determined by first ascertaining
their real-valued molecular phenotypes me and m.sub.cr using the
MPNN. The context-specific link distance d.sup.tr between the two
variants may be computed using one of the formulas:
d.sup.tr=m.sub.c.sup.t-m.sub.c.sup.r,d.sup.tr=(m.sub.c.sup.t-m.sub.c.sup-
.r).sup.2,d.sub.tr=|m.sub.c.sup.t-m.sub.c.sup.r|,
where | | is the absolute function. This may be repeated for all
pairs of biologically related variants or for a subset of pairs. It
will be appreciated that the MPNN need be applied only once for
each variant, and that the comparator (108) may apply various other
computations to compute the link distance.
[0099] In another embodiment, the molecular phenotype determined
using the MPNN is a vector of values, so that
m.sub.c.sup.t=(m.sub.c,1.sup.t, m.sub.c,1.sup.t, . . .
,m.sub.c,q.sup.t) and m.sub.c.sup.r=(m.sub.c,1.sup.r,
m.sub.c,1.sup.r, . . . ,m.sub.c,q.sup.r). The context-specific link
distance between the two variants may be computed using one of the
operations:
d.sup.tr.rarw..SIGMA..sub.n=1.sup.q(m.sub.c,n.sup.t-m.sub.c,n.sup.r),d.s-
up.tr.rarw..SIGMA..sub.n=1.sup.q(m.sub.c,n.sup.t-m.sub.c,n.sup.r).sup.2,d.-
sup.tr.rarw..SIGMA..sub.n=1.sup.q|m.sub.c,n.sup.t-m.sub.c,n.sup.r|,
where | | is the absolute function. This may be repeated for all
pairs of biologically related variants or for a subset of pairs. It
will be appreciated that the MPNN need be applied only once for
each variant, and that the comparator (108) may apply various other
computations to compute the link distance.
[0100] In another embodiment, the molecular phenotype is a vector
of values corresponding to probabilities over different possible
categories, the probabilities summing to one. The context-specific
link distance between the two variants may be computed using an
operation that accounts for probabilities in the fashion of the
Kullback-Leibler divergence:
d.sup.tr.rarw..SIGMA..sub.n=1.sup.qm.sub.c,n.sup.t
log(m.sub.c,n.sup.t/m.sub.c,n.sup.r),d.sup.tr.rarw..SIGMA..sub.n=1.sup.qm-
.sub.c,n.sup.r log(m.sub.c,n.sup.r/m.sub.c,n.sup.t),
[0101] In another embodiment, the molecular phenotypes for every
context c=1 . . . T is determined using the MPNN and they are
placed in a vector for each pair of variants:
m.sup.t=(m.sub.1.sup.t, . . . , m.sub.T.sup.t) and
m.sup.r=(m.sub.1.sup.r, . . . , m.sub.T.sup.r) wherein T is the
number of contexts. The link distance between the two variants may
be computed using one of the formulas:
d.sup.tr.rarw..SIGMA..sub.c=1.sup.T(m.sub.c.sup.t-m.sub.c.sup.r),d.sup.t-
r.rarw..SIGMA..sub.c=1.sup.T(m.sub.c.sup.t-m.sub.c.sup.r).sup.2,d.sup.tr.r-
arw..SIGMA..sub.c=1.sup.T|m.sub.c.sup.t-m.sub.c.sup.r|.
When summing across contexts, predetermined numerical scaling
factors may be used to give higher weight to some conditions
compared to others. Denote the set of scale factors for the
different conditions by a.sub.1, . . . , a.sub.T. One of the
following formulas may be used to compute the link distance:
d.sup.tr.rarw..SIGMA..sub.c=1.sup.Ta.sub.c(m.sub.c.sup.t-m.sub.c.sup.r),-
d.sup.tr.rarw..SIGMA..sub.c=1.sup.Ta.sub.c(m.sub.c.sup.t-m.sub.c.sup.r).su-
p.2,d.sup.tr.rarw..SIGMA..sub.c=1.sup.Ta.sub.c|m.sub.c.sup.t-m.sub.c.sup.r-
|.
This may be repeated for all pairs of biologically related variants
or for a subset of pairs. It will be appreciated that the MPNN need
be applied only once for each variant. It will be appreciated that
the comparator may apply various other computations to compute the
link distance.
[0102] It will be appreciated that this method can be applied using
MPNNs that compute several different molecular phenotypes and these
may be combined to determine link distances. It will be appreciated
that multiple MPNNs may be applied to compute multiple molecular
phenotypes and these may be combined to determine link distances.
It will be appreciated that multiple MPNNs may be applied to
compute multiple link distances and that these may be combined to
determine link distances.
[0103] In another aspect, for a set of biologically related
variants wherein some of the variants are labeled, the
MPNN-determined link distances between the other variants and the
labeled variants can be used to associate the labels with the other
variants. The label of one of the other variants may be determined
by computing the link distances of the other variant to one or more
of the labeled variants. The label of the other variant may be
determined from the label of the labelled variant that has the
lowest link distance. Alternatively, the label of the other variant
may be determined by computing the weighted average of the labels
of the labelled variants, where the weights are nonlinear functions
of the link distances. Two or more other variants may be
prioritized, by sorting them according to their label values. Two
or more other variants may be partially sorted according to their
label values, that is, the k other variants with smallest link
distance may be identified and sorted, where k is smaller than the
number of other variants. The determined label may be applied to
the variant by the labeling unit (111), as shown in FIG. 11.
[0104] To illustrate, suppose the system determines that a test
variant causes a change in a particular molecular phenotype, say
the splicing level of a specific exon. Suppose a nearby, labelled
variant whose disease function is well characterized causes a
similar change in the exact same molecular phenotype. Since
variants act by changing cellular chemistry, such as the splicing
level of the exon, it can be inferred that the test variant likely
has the same functional impact as the labelled variant. The system
can ascertain the link distance between the two variants in this
fashion using a variety of different measures. Because the MPNN can
take a specification of context, such as cell type, as input, this
information can be used to more accurately associate variants with
one another. For example, two variants that have similar molecular
phenotypes in brain tissue would be associated more strongly than
two variants that have similar molecular phenotypes, but in
different tissues.
[0105] One class of labels measure deleteriousness. A
"deleteriousness label" is a classification, category, level or
numerical value that is associated with a variant and that relates
its level of deleteriousness for one or more functions or
categories. It may be derived using evolutionary analysis, an
analysis of how severely the variant damages a biological process
or biomolecule, knowledge about the variant's disease function, or
other information pertaining to the variant. A deleteriousness
label may contain a set of numerical values that each indicates the
degree of deleteriousness in one of multiple categories of
deleteriousness. It will be appreciated that deleteriousness has a
broad definition and that the methods and systems described herein
may be applied to deleteriousness labels, but also to labels of
related or other kinds.
[0106] More generally, labels represent additional information that
should be associated between variants of similar function. Labels
may be categorical, with two values, such as "yes" and "no", or
"damaging" and "non-damaging", or may have one of more than two
values, such as "benign", "likely benign", "likely pathogenic",
"pathogenic" and "uncertain significance". Labels may real-valued,
such as a real number between zero and one where zero corresponds
to low pathogenicity and one corresponds to high pathogenicity.
Labels may be scores with numeric values that indicate how
deleterious, pathogenic, or damaging variants are expected to be.
The labels may reflect other quantitative aspects of gross
phenotype, phenotype or molecular phenotype, such as those
associated with diabetes, cardiovascular conditions and
neurological disorders. An example is the IQ coefficient. Labels
may be vector-valued; for example, three quantitative phenotypes
can be encoded as a vector of length 3, (value 1, value 2, value
3). Categorical labels may be encoded as vectors using one-hot
encoding. For example, the categories "benign", "likely benign",
"likely pathogenic" and "pathogenic" can be encoded as the vector
labels (1,0,0,0), (0,1,0,0), (0,0,1,0) and (0,0,0,1). It will be
appreciated that labels may be encoded in different ways and that
the systems and methods described herein can be applied.
[0107] Referring now to FIG. 7, labels for deleteriousness may be
associated with some variants and these labeled variants may be
used to determine labels for other variants. Denote the label for
variant r by L.sup.r. The label may be a one-hot encoding of a
classification, such as where a label of (1,0) indicates that the
variant is not deleterious and a label of (0,1) indicates that the
variant is deleterious. The label may be real-valued, such as a
real number between 0 and 1, where 0 indicates that the variant is
not deleterious and 1 indicates that it is deleterious. It is
appreciated that other categorical, numerical, or vector-numerical
labels may be used. The labels of the other variants indexed by t
may be determined using the labeled variant with lowest link
distance, with the formula:
[0108] L.sup.t.rarw.L.sup.r*, where r* is selected such that
d.sup.tr*.ltoreq.d.sup.tr for all labeled variants r.
[0109] Referring now to FIG. 8, the labels for the other variants
may be determined by a weighted average of the labels of the
labeled variants. Referring now to FIG. 9, for another variant t, a
weighting module is applied to determine the weights for all
labeled variants and then the weights are used to combine the
labels of the labeled variants:
L.sup.t.rarw..SIGMA..sub.r=1.sup.R=w.sup.trL.sup.r.
This weighted combination of labels requires that the labels be
represented numerically, such as using one-hot encoding. It will be
appreciated that other numerical encodings of labels are possible
and that the label may represent a continuous quantity, such as a
probability distribution or a real-valued pathogenicity level.
[0110] Referring now to FIG. 9, the weighting module takes as input
link distances and outputs a set of weights. Denote the weight for
the other variant t and the labeled variant r by w.sup.tr. The
weights are determined by applying a linear or a nonlinear
weighting module to the link distances:
(w.sup.t1,w.sup.t2, . . . ,w.sup.tR).rarw.f(d.sup.t1,d.sup.t2, . .
. ,d.sup.tR),
where f( ) is the result of the linear or nonlinear weighting
module and the labeled variants are indexed by 1, . . . , R.
[0111] The weighting module may determine the weights for different
labeled variants independently:
(w.sup.t1,w.sup.t2, . . .
,w.sup.tR).rarw.(f'(d.sup.t1),f(d.sup.t2), . . .
,f'(d.sup.tR)),
where f'( ) is the result of the weighting module applied to each
link distance individually. This corresponds to a weighting module
with the following form:
f(d.sup.t1,d.sup.t2,d.sup.tR)=(f'(d.sup.t1),f'(d.sup.t2), . . .
,f(d.sup.tR)).
Examples of such weighting modules f'( ) include:
f'(d.sup.tr).rarw.1/(1+.alpha.d.sup.tr),
f'(d.sup.tr).rarw.exp(-.alpha.d.sup.tr),
f'(d.sup.tr).rarw.1/(1+exp(.alpha.(d.sub.0-d.sup.tr))),
where .alpha. and d.sub.0 are predetermined numerical parameters.
.alpha. determines how quickly the magnitude of the weight drops
off when the link distance increases. The first two formulas cause
the weight to drop immediately when the link distance increases
from zero. The third formula allows for the weight to drop off only
when it starts to approach a threshold on the link distance,
d.sub.0. It will be appreciated that other nonlinear weighting
functions may be used.
[0112] Referring now to FIG. 10, the weighting module may determine
the weights for different labeled variants in a way that depends on
more than one labeled variant. For example, the weights for one
other variant and all labeled variants may be normalized so that
the sum over the labeled variants is one. The weighting module
first computes the un-normalized weights independently for
different labeled variants:
{tilde over (w)}.sup.tr.rarw.f'(d.sup.tr), for r=1, . . . ,R.
Then, the weighting module determines the normalization factor:
z.sup.t=.SIGMA..sub.r'=1.sup.R{tilde over (w)}.sup.tr'.
Lastly, the weighting module outputs the normalized weights:
(w.sup.t1,w.sup.t2, . . . ,w.sup.tR).rarw.({tilde over
(w)}.sup.t1/z.sup.t,{tilde over (w)}.sup.t2/z.sup.t, . . . ,{tilde
over (w)}.sup.tr/z.sup.t)
It will be appreciated that these computations can be performed
differently so as to achieve the same or a very similar effect.
[0113] Another example of a weighting module that determines the
weights for different labeled variants in a way that depends on
more than one labeled variant, is a weighting module that places
all weight on the labeled variant with the lowest link distance.
The weighting module first identifies the labeled variant with
lowest link distance:
r * .rarw. arg .times. .times. min r .times. .times. d tr ,
##EQU00002##
Then, it sets the corresponding weight to one and the others to
zero:
(w.sup.t1,w.sup.t2, . . . ,w.sup.tR).rarw.([r*=1],[r*=2], . . .
,[r*=R]),
where [ ] indicates Iverson's notation, as described above. It will
be appreciated that the set of weights may be determined
efficiently by setting all weights to zero and then setting the
weight for label r* to one.
[0114] After the weights are computed, the label of the other
variant t may be determined by combining the labels of the labeled
variants, using the weights:
L.sup.t.rarw..SIGMA..sub.r=1.sup.Rw.sup.trL.sup.r.
It will be appreciated that labeled variants that have a weight of
zero need not be explicitly multiplied by their weights and summed
over:
L.sup.t.rarw..SIGMA..sub.r {1, . . .
,R},w.sub.tr.sub..noteq.0w.sup.trL.sup.r.
In the case of picking the labeled variant with lowest link
distance, this summation reduces to
L.sup.t.rarw.L.sup.r*.
[0115] Another example of a weighting module that determines the
weights for different labeled variants in a way that depends on
more than one labeled variant, is a weighting module that outputs
equal weights on the .rho. labeled variants that have lowest link
distance.
[0116] The weighting module parameters, such as .alpha., .rho.,
d.sub.0 may be set by hand or by searching over appropriate values
using a dataset of variants with known labels, such as to obtain
the highest possible correct label classification rate.
[0117] The labels may be encoded as real-valued or binary-valued
vectors, in which case the weighted combination of labels will
result in a vector label of the same length. If the reference
variant labels use a one-hot encoding, such as where a label of
(1,0) indicates that the variant is not deleterious and a label of
(0,1) indicates that the variant is deleterious, the weighted
combination of the labels of the labeled variants will result in a
real-valued vector. For example, if the normalized weights for 5
labeled variants are 0.5, 0.3, 0.1, 0.1, 0.0 and the labeled
variants have labels (0,1), (0,1), (1,0), (1,0), (1,0), then the
label of the other variant will be
0.5.times.(0,1)+0.3.times.(0,1)+0.1.times.(1,0)+0.1.times.(1,0)+0.0.times-
.(1,0), which equals (0.2,0.8), indicating that the label (0,1) has
more evidence than the label (1.0), but that there is some
uncertainty. It will be appreciated that this is a small example
and that in practical applications the number of variants may be
higher, such as in the thousands, in the millions or even
higher.
[0118] Once the labels have been determined for a set of other
variants indexed from 1 to .tau., the other variants may be
prioritized by sorting their labels. If the labels use a one-hot
encoding, such as where a label of (1,0) indicates that the variant
is not deleterious and a label of (0,1) indicates that the variant
is deleterious, the second label value for each other variant may
be used for prioritization. For example, if there are 4 other
variants with labels (0.2,0.8), (0.7,0.3), (0.1,0.9), (0.9,0.1)
corresponding to other variants 1, 2, 3 and 4, and we use the
second label value, which corresponds to the deleterious label, we
will prioritize the 4 other variants using the values 0.8, 0.3, 0.9
and 0.1. Sorting this list of values gives us a prioritized list of
other variants: 3, 1, 2, 4, that is, other variant 3 is the "most
deleterious" and other variant 4 is the "least deleterious". The
other variants prioritized in this way may be subject to subsequent
analysis, which may include further computational analysis or
experimental analysis. It will be appreciated that the other
variants may be prioritized in different ways using the labels.
[0119] The weights used to combine the labels of the labelled
variants can be constructed so as to have different values for
different possible values of the labels. This can be used to
correct for different link distance densities of labeled variants,
for example, wherein the number of variants labeled benign is
significantly higher than the number of variants labeled
pathogenic. Denote the label vector length by v, so that the label
of the labeled variant L.sup.r can be represented as
L.sup.r=(L.sub.1.sup.r,L.sub.2.sup.r, . . . ,L.sub.v.sup.r).
[0120] An example is label that uses a one-hot encoding, where Lr
is a binary vector with a 1 in one position and zero everywhere
else. The weight w.sup.tr for the other variant t and the labeled
variant r can be a real-valued vector of the same length, v:
w.sup.tr=(w.sub.1.sup.tr,w.sub.2.sup.tr, . . .
,w.sub.v.sup.tr).
[0121] The weights are determined by applying a weighting module to
the link distances, in a way so that different possible values of
the labels may have different weights. Using e to index the labels
such that e ranges from 1 to v, the weighting module may
determining the weights as follows:
w e tr .rarw. 1 / ( 1 + .alpha. e .times. .times. d tr ) , .times.
w e tr .rarw. exp .times. .times. ( - .alpha. e .times. .times. d
tr ) , .times. w e tr .rarw. ( 1 1 + exp .times. .times. ( .alpha.
e .function. ( d 0 , e - d tr ) ) ) , ##EQU00003##
where .alpha..sub.e and d.sub.0,e are predetermined numerical
parameters that determine how quickly the weights drop off to zero
as link distance increases, but in a way that is label dependent.
For instance, if the labels are (1,0) for "benign" and (0,1) for
"pathogenic" and, for a particular test variant, the link distance
density of labeled benign variants is much larger than the density
of labeled pathogenic variants nearby in the genome, then we can
set .alpha..sub.1 and .alpha..sub.2 to values such that the weights
drop off more quickly with link distance for the benign variants:
.alpha..sub.1>.alpha..sub.2. The weights for each label value
e=1, . . . ,q may be separately normalized so that the sum over the
labeled variants is one. The weighting module first computes the
un-normalized weights {tilde over (w)}.sub.e.sup.tr independently
for different labeled variants, such as by using
{tilde over (w)}.sub.e.sup.tr.rarw.1/(1+.alpha..sub.ed.sup.tr).
Then, for each label value, the weighting module determines the
normalization factor:
z.sub.e.sup.t=.SIGMA..sub.r'=1.sup.R{tilde over (w)}.sub.e.sup.tr'
for e=1 . . . q.
Lastly, the weighting module outputs the normalized weights:
(w.sub.1.sup.t1,w.sub.1.sup.t2, . . . ,w.sub.1.sup.tR).rarw.({tilde
over (w)}.sub.1.sup.t1/z.sub.1.sup.t,{tilde over
(w)}.sub.1.sup.t2/z.sub.1.sup.t, . . . ,{tilde over
(w)}.sub.1.sup.tR/z.sub.1.sup.t),
(w.sub.q.sup.t1,w.sub.q.sup.t2, . . . ,w.sub.q.sup.tR).rarw.({tilde
over (w)}.sub.q.sup.t1/z.sub.q.sup.t,{tilde over
(w)}.sub.q.sup.t2/z.sub.q.sup.t, . . . ,{tilde over
(w)}.sub.q.sup.tR/z.sub.q.sup.t)
It will be appreciated that these computations can be performed
differently so as to achieve the same or a very similar effect.
[0122] For all label values e=1, . . . ,q, the e th label of the
other variant t may be determined using the weighted average:
L.sub.e.sup.t.rarw..SIGMA..sub.r=1.sup.Rw.sub.e.sup.trL.sub.e.sup.r.
[0123] The weighting module parameters may be set by hand or by
searching over appropriate values using a dataset of variants with
known labels, such as to obtain the highest possible correct label
classification rate.
[0124] The link distance provides information about how similar two
variants are in their molecular phenotype, but additional
information may be available about the variants that can be used by
the weighting module to determine the weights. Additional
information may include the proximity of the two variants within
the biological sequence, such as the difference in the coordinates
of two single-substitution variants; quantitative trait loci
information, such as expression- or splicing-quantitative trait
loci information; information about the linkage disequilibrium
between the two variants or between the two variants and other
variants of interest; information pertaining to other information
for variants that are implicated in a specific disease or class of
diseases. It will be appreciated that other types of information
may be used to adjust the weights. We denote this additional
information for other variant t and labeled variant r by
I.sup.tr.
[0125] More generally, the link distance may be determined using a
link neural network, which takes as input the molecular phenotype
of the labeled variant for contexts c=1, . . . , T,
m.sup.r=(m.sub.1.sup.r, . . . , m.sub.T.sup.r), and the molecular
phenotype of the other variant for contexts c=1, . . . , T,
m.sup.t=(m.sub.1.sup.r, . . . , m.sub.T.sup.r), and the additional
information I.sup.tr, and outputs the link distance d.sup.tr.
Denoting the operations of the link neural network by N( ), the
application of the link neural network can be represented as
d.sup.tr.rarw.N(m.sup.t,m.sup.r,I.sup.tr).
The parameters of the link neural network may be determined from a
dataset of examples, wherein each example consists of the pair of
variants, the additional information, and the target, which may be
derived from labels for the variants and a measure of similarity on
the labels. An appropriate machine learning method can be used to
configure the link neural network.
[0126] In one embodiment, the link neural network is not trained
using a dataset of examples, but is instead configured by hand. For
example, if the link neural network is configured as follows,
N(m.sup.t,m.sup.r,I.sup.tr).rarw..SIGMA..sub.c=1.sup.T(m.sub.c.sup.t-m.s-
ub.c.sup.r).sup.2,
then it acts to produce the link distance described above.
[0127] In another embodiment, the additional information pertains
to the proximity of two localized variants, such as
single-substitution variants, within the biological sequence. In
this case, for one of the other variants, the labeled variants that
are nearby in the biological sequence may be given lower link
distances, even if their molecular phenotypes are similar. Denote
the absolute difference in coordinates between the other variant t
and the labeled variant r in the biological sequence by I.sup.tr.
If this value is large, the variants are less likely to have
similar function, all else being the same, than if the value is
small. The link neural network may be configured as follows:
N(m.sup.t,m.sup.r,I.sup.tr).rarw..SIGMA..sub.c=1.sup.T(m.sub.c.sup.t-c.s-
ub.m.sup.r).sup.2+.gamma.I.sup.tr,
where .gamma. is a parameter that trades off the effect of the
molecular phenotype distance and the additional information. This
parameter may be set using training data. It will be appreciated
that other measures of proximity may be used, such as square
differences in coordinates, and that other types of additional
information may be used. It will be appreciated that multiple types
of additional information may be encoded in I.sup.tr, including
real-valued, vector-valued and categorical information, which may
be encoded, for instance, using one-hot encoding.
[0128] Although the invention has been described with reference to
certain specific embodiments, various modifications thereof will be
apparent to those skilled in the art without departing from the
spirit and scope of the invention as outlined in the claims
appended hereto.
* * * * *