U.S. patent application number 17/369499 was filed with the patent office on 2021-12-09 for systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network.
The applicant listed for this patent is Deep Genomics Incorporated. Invention is credited to Babak ALIPANAHI, Hannes BRETSCHNEIDER, Andrew Thomas DELONG, Brendan FREY, Leo J. LEE, Michael K. K. LEUNG, Hui Yuan XIONG.
Application Number | 20210383890 17/369499 |
Document ID | / |
Family ID | 1000005795396 |
Filed Date | 2021-12-09 |
United States Patent
Application |
20210383890 |
Kind Code |
A1 |
FREY; Brendan ; et
al. |
December 9, 2021 |
SYSTEMS AND METHODS FOR CLASSIFYING, PRIORITIZING AND INTERPRETING
GENETIC VARIANTS AND THERAPIES USING A DEEP NEURAL NETWORK
Abstract
Described herein are systems and methods that receive as input a
DNA or RNA sequence, extract features, and apply layers of
processing units to compute one ore more condition-specific cell
variables, corresponding to cellular quantities measured under
different conditions. The system may be applied to a sequence
containing a genetic variant, and also to a corresponding reference
sequence to determine how much the condition-specific cell
variables change because of the variant. The change in the
condition-specific cell variables are used to compute a score for
how deleterious a variant is, to classify a variant's level of
deleteriousness, to prioritize variants for subsequent processing,
and to compare a test variant to variants of known deleteriousness.
By modifying the variant or the extracted features so as to
incorporate the effects of DNA editing, oligonucleotide therapy,
DNA- or RNA-binding protein therapy or other therapies, the system
may be used to determine if the deleterious effects of the original
variant can be reduced.
Inventors: |
FREY; Brendan; (Toronto,
CA) ; LEUNG; Michael K. K.; (Toronto, CA) ;
DELONG; Andrew Thomas; (Toronto, CA) ; XIONG; Hui
Yuan; (Toronto, CA) ; ALIPANAHI; Babak;
(Toronto, CA) ; LEE; Leo J.; (Toronto, CA)
; BRETSCHNEIDER; Hannes; (Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Deep Genomics Incorporated |
Toronto |
|
CA |
|
|
Family ID: |
1000005795396 |
Appl. No.: |
17/369499 |
Filed: |
July 7, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16197146 |
Nov 20, 2018 |
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17369499 |
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14739432 |
Jun 15, 2015 |
10185803 |
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16197146 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 20/00 20190201;
G16B 20/20 20190201; G16B 40/00 20190201; G16B 30/00 20190201; G16B
40/20 20190201; G06N 3/04 20130101 |
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. A computer-implemented method for computing a set of
variant-induced changes in a condition-specific cell variable for a
genetic variant, comprising processing a set of variant features
using a cell variable predictor to quantify a condition-specific
variant cell variable without obtaining a reference measurement of
the genetic variant across a plurality of conditions.
2. The method of claim 1, wherein the genetic variant comprises a
variant in a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA)
variant sequence relative to a DNA or RNA reference sequence, and
wherein the method further comprises extracting the set of variant
features from the DNA or RNA variant sequence.
3. The method of claim 1, further comprising extracting a set of
reference features from the DNA or RNA reference sequence, and
processing the set of reference features using the cell variable
predictor to quantify a condition-specific reference cell
variable.
4. The method of claim 3, wherein the set of variant features is
extracted from the DNA or RNA variant sequence by generating: a. a
binary matrix with 4 rows and a number of columns equal to a length
of the DNA or RNA variant sequence or the DNA or RNA reference
sequence, wherein each column contains a bit indicating the
nucleotide value at the corresponding position in the DNA or RNA
variant sequence or the DNA or RNA reference sequence; b. a set of
features computed using one or more layers of an autoencoder other
than the input and output layers of the cell variable predictor; or
c. a set of features that correspond to one or more of: RNA
secondary structures, nucleosome positions, and retroviral repeat
elements.
5. The method of claim 3, further comprising computing, using the
cell variable predictor, probabilities for discrete levels of the
condition-specific cell variable, wherein each of the set of
variant-induced changes is computed by: a. summing an absolute
difference between the computed probabilities for the
condition-specific reference cell variable and the
condition-specific variant cell variable; b. summing a
Kullback-Leibler divergence between the computed probabilities of
the condition-specific reference cell variable and the
condition-specific variant cell variable for each condition; or c.
computing an expected value of the condition-specific reference
cell variable and the condition-specific variant cell variable, and
subtracting the expected value of the condition-specific reference
cell variable from the expected value of the condition-specific
variant cell variable.
6. The method of claim 1, wherein the cell variable predictor
comprises a deep neural network.
7. The method of claim 6, wherein the deep neural network comprises
a convolutional neural network, a recurrent neural network, or a
long-term short-term memory recurrent neural network.
8. The method of claim 1, further comprising combining the set of
variant-induced changes in the condition-specific cell variable to
compute a single numerical variant score for the genetic variant,
the single numerical variant score computed by: a. outputting the
score for a fixed condition; b. summing the variant-induced changes
across a plurality of conditions; or c. computing the maximum of
the absolute variant-induced changes across a plurality of
conditions.
9. The method of claim 1, further comprising computing, for a pair
of genetic variants, a distance between the two genetic variants in
the pair by summing the output of a nonlinear function applied to a
difference between the change in the condition-specific cell
variable for the first of the two genetic variants and the change
in the condition-specific cell variable for the second of the two
genetic variants.
10. The method of claim 1, wherein the genetic variant comprises a)
two or more distinct single nucleotide variants (SNVs); or b) a
combination of substitutions, insertions, and deletions, wherein
the combination is not a single nucleotide variant (SNV).
11. A computer-implemented method for computing a set of
variant-induced changes in a condition-specific cell variable for a
genetic variant, comprising processing a set of variant features
using a cell variable predictor to quantify a condition-specific
variant cell variable, wherein the cell variable predictor
comprises a deep neural network comprising at least two connected
layers of processing units.
12. The method of claim 11, wherein the genetic variant comprises a
variant in a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA)
variant sequence relative to a DNA or RNA reference sequence, and
wherein the method further comprises extracting the set of variant
features from the DNA or RNA variant sequence.
13. The method of claim 11, further comprising extracting a set of
reference features from the DNA or RNA reference sequence, and
processing the set of reference features using the cell variable
predictor to quantify a condition-specific reference cell
variable.
14. The method of claim 13, wherein the set of variant features is
extracted from the DNA or RNA variant sequence by generating: a. a
binary matrix with 4 rows and a number of columns equal to a length
of the DNA or RNA variant sequence or the DNA or RNA reference
sequence, wherein each column contains a bit indicating the
nucleotide value at the corresponding position in the DNA or RNA
variant sequence or the DNA or RNA reference sequence; b. a set of
features computed using one or more layers of an autoencoder other
than the input and output layers of the cell variable predictor; or
c. a set of features that correspond to one or more of: RNA
secondary structures, nucleosome positions, and retroviral repeat
elements.
15. The method of claim 13, further comprising computing, using the
cell variable predictor, probabilities for discrete levels of the
condition-specific cell variable, wherein each of the set of
variant-induced changes is computed by: a. summing an absolute
difference between the computed probabilities for the
condition-specific reference cell variable and the
condition-specific variant cell variable; b. summing a
Kullback-Leibler divergence between the computed probabilities of
the condition-specific reference cell variable and the
condition-specific variant cell variable for each condition; or c.
computing an expected value of the condition-specific reference
cell variable and the condition-specific variant cell variable, and
subtracting the expected value of the condition-specific reference
cell variable from the expected value of the condition-specific
variant cell variable.
16. The method of claim 11, wherein the deep neural network
comprises a convolutional neural network, a recurrent neural
network, or a long-term short-term memory recurrent neural
network.
17. The method of claim 11, further comprising combining the set of
variant-induced changes in the condition-specific cell variable to
compute a single numerical variant score for the genetic variant,
the single numerical variant score computed by: a. outputting the
score for a fixed condition; b. summing the variant-induced changes
across a plurality of conditions; or c. computing the maximum of
the absolute variant-induced changes across a plurality of
conditions.
18. The method of claim 11, further comprising applying thresholds
that are fixed or selected using labeled data to the single
numerical variant score for the genetic variant to classify the
genetic variant (i) as one of deleterious or non-deleterious, (ii)
as one of pathogenic, likely pathogenic, unknown significance,
likely benign, or benign, or (iii) using another discrete set of
labels.
19. The method of claim 11, further comprising computing, for a
pair of genetic variants, a distance between the two genetic
variants in the pair by summing the output of a nonlinear function
applied to a difference between the change in the
condition-specific cell variable for the first of the two genetic
variants and the change in the condition-specific cell variable for
the second of the two genetic variants.
20. The method of claim 11, wherein the genetic variant comprises
a) two or more distinct single nucleotide variants (SNVs); or b) a
combination of substitutions, insertions, and deletions, wherein
the combination is not a single nucleotide variant (SNV).
Description
CROSS-REFERENCE
[0001] This application is a continuation of U.S. application Ser.
No. 16/197,146, filed Nov. 20, 2018, which is a divisional 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 systems and methods for
classifying, prioritizing and interpreting genetic variants and
therapies using a deep neural network.
BACKGROUND
[0003] Precision medicine, genetic testing, therapeutic development
and whole genome, exome, gene panel and mini-gene reporter analysis
require the ability to accurately interpret how diverse features
encoded in the genome, such as protein binding sites, RNA secondary
structures, and nucleosome positions, impact processes within
cells. Most existing approaches to identifying disease variants
ignore their impact on these genomic features. Many genome studies
are restricted to mutations in exons that either change an amino
acid in a protein or prevent the production of the protein.
[0004] Over the past decade, the importance of understanding
regulatory genomic instructions and not just the protein-coding
exons and genes that they control has been underscored by several
observations: While evolution is estimated to preserve at least
5.5% of the human genome, only 1% accounts for exons within genes;
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,
indicating that crucial information is encoded outside of those
sequences.
[0005] 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, by either sequencing
the genome, the exome or a gene panel; the variants are
individually examined manually (e.g. by a technician), using
literature databases and internet search engines; 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. Automating or semi-automating this
step is thus beneficial. Since the number of possible genetic
variants is 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 unknown significance". The same
issues arise in therapeutic design, where the number of possible
therapies (molecules) to be evaluated is extremely large.
[0006] Techniques have been proposed for which predicting
phenotypes (e.g., traits and disease risks) from the genome can be
characterized as a problem suitable for solution by machine
learning, and more specifically by supervised machine learning
where inputs are features extracted from a DNA sequence (genotype),
and the outputs are the phenotypes. Such an approach is shown in
FIG. 2(a). A DNA sequence 204 is fed to a predictor 202 to generate
outputs 208, such as disease risks. This approach is unsatisfactory
for most complex phenotypes and diseases for two reasons. First is
the sheer complexity of the relationship between genotype
(represented by 204) and phenotype (represented by 208). Even
within a single cell, the genome directs the state of the cell
through many layers of intricate biophysical processes and control
mechanisms that have been shaped by evolution. It is extremely
challenging to infer these regulatory processes by observing only
the genome and phenotypes, for example due to `butterfly effects`.
For many diseases, the amount of data necessary would be
cost-prohibitive to acquire with currently available technologies,
due to the size of the genome and the exponential number of
possible ways a disease can be traced to it. Second, even if one
could infer such models (those that are predictive of disease
risks), it is likely that the hidden variables of these models
would not correspond to biological mechanisms that can be acted
upon, unless strong priors, such as cause-effect relationships,
have been built in. This is important for the purpose of developing
therapies. Insisting on how a model ought to work by using these
priors can hurt model performance if the priors are inaccurate,
which they usually are.
[0007] Some other machine learning approaches to genetic analysis
have been proposed. One such approach predicts a cell variable that
combines information across conditions, or tissues. Another
describes a shallow, single-layer Bayesian neural network (BNN),
which often relies on methods like Markov Chain Monte Carlo (MCMC)
to sample models from a posterior distribution, which can be
difficult to speed up and scale up to a large number of hidden
variables and a large volume of training data. Furthermore,
computation-wise, it is relatively expensive to get predictions
from a BNN, which require computing the average predictions of many
models.
SUMMARY
[0008] In one aspect, a method for computing variant-induced
changes in one or more condition-specific cell variables for one or
more variants is provided, the method comprising: computing a set
of variant features from a DNA or RNA variant sequence; applying a
deep neural network of at least two layers of processing units to
the variant features to compute one or more condition-specific
variant cell variables; computing a set of reference features from
a DNA or RNA reference sequence; applying the deep neural network
to the reference features to compute one or more condition-specific
reference cell variables; computing a set of variant-induced
changes in the one or more condition-specific cell variables by
comparing the one or more condition-specific reference cell
variables to the one or more condition-specific variant cell
variables.
[0009] In another aspect, a deep neural network for computing
variant-induced changes in one or more condition-specific cell
variables for one or more variants is provided, the deep neural
network comprising: an input layer configured to receive as input a
set of variant features from a DNA or RNA variant sequence; and at
least two layers of processing units operable to: compute one or
more condition-specific variant cell variables; compute a set of
reference features from a DNA or RNA reference sequence; compute
one or more condition-specific reference cell variables; compute a
set of variant-induced changes in the one or more
condition-specific cell variables by comparing the one or more
condition-specific reference cell variables to the one or more
condition-specific variant cell variables.
[0010] In another aspect, a method for training a deep neural
network to compute one or more condition-specific cell variables is
provided, the method comprising: establishing a neural network
comprising at least two connected layers of processing units;
repeatedly updating one or more parameters of the neural network so
as to decrease the error for a set of training cases chosen
randomly or using a predefined pattern, where each training case
comprises features extracted from a DNA or RNA sequence and
corresponding targets derived from measurements of one or more
condition-specific cell variables, until a condition for
convergence is met at which point the parameters are no longer
updated.
DESCRIPTION OF THE DRAWINGS
[0011] The features of the invention will become more apparent in
the following detailed description in which reference is made to
the appended drawings wherein:
[0012] FIG. 1 shows a system for cell variable prediction;
[0013] FIG. 2 shows a comparison of approaches to predict
phenotypes, such as disease risks, from an input;
[0014] FIG. 3 shows a method of generating target cell variables
for training;
[0015] FIG. 4 shows an example deep neural network architecture for
a cell variable predictor that predicts splicing levels;
[0016] FIG. 5 shows a further example deep neural network
architecture for a cell variable predictor that predicts splicing
levels;
[0017] FIG. 6 shows yet a further example deep neural network
architecture for a cell variable predictor that predicts splicing
levels;
[0018] FIG. 7 shows yet a further example deep neural network
architecture for a cell variable predictor that predicts splicing
levels;
[0019] FIG. 8 shows yet a further example deep neural network
architecture for a cell variable predictor that predicts splicing
levels;
[0020] FIG. 9 shows yet a further example deep neural network
architecture for a cell variable predictor that predicts splicing
levels;
[0021] FIG. 10 shows a method for training cell variable
predictors;
[0022] FIG. 11 shows a system to perform non-uniform sampling of
training cases for determining a mini-batch for training a deep
neural network;
[0023] FIG. 12 shows a method for training cell variable predictors
for ensuring a consistent backpropagation signal that updates the
weights connected to tissue inputs and biases learning towards the
event with large tissue variability early on before overfitting
occurs;
[0024] FIG. 13 shows a method for using the outputs of the CVP for
scoring, classifying and prioritizing genetic variants;
[0025] FIG. 14 shows a method for scoring variants by associating
cell variable changes with those of other variants;
[0026] FIG. 15 shows a method for interpreting which genetic
features account for variant-induced cell variable changes;
[0027] FIG. 16 shows a further method for interpreting which
genetic features account for variant-induced cell variable
changes;
[0028] FIG. 17 shows a further method for interpreting which
genetic features account for variant-induced cell variable
changes;
[0029] FIG. 18 shows a method to generate a visualization for
tissue-specific feature importance; and
[0030] FIG. 19 shows a detailed illustration of the method to
generate a visualization for tissue-specific feature
importance.
DETAILED DESCRIPTION
[0031] 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.
[0032] 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.
[0033] 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.
[0034] Systems and methods described herein relate, in part, to the
problem of assessing genetic variants with respect to phenotypes,
such as deleteriousness for human diseases. This problem has
implications in several industrial categories under the broad
umbrella of `personalized medicine`, including molecular
diagnostics, whole genome sequencing, and pharmaceutical
development.
[0035] It has been found that the effect of a variant depends on
genetic context, which includes which other variants are present
and, more generally, on the genomic sequence within the individual,
or patient, being tested. So, whereas a particular variant may be
benign in one genetic context, it may cause a disease in another
genetic context. This impacts prioritization and interpretation.
The following describes a process for context-dependent genetic
variant assessment and wherein variants may be ranked and presented
as a priority list. Variant prioritization can be used to increase
efficiency and accuracy of manual interpretation, since it enables
the technician to focus on a small subset of candidates
[0036] Computational procedures for prioritizing and/or
interpreting variants must generalize well. Generalization refers
to the ability of the computational procedure to assess variants
that have not been seen before and that may be involved in a
disease that has not been previously analyzed. A method that
generalizes well should even be able to assess variants within
genes that have not been previously analyzed for variants. Finally,
a crucial aspect of enabling computational procedures to operate
effectively is computational efficiency since these procedures may
involve aggregating, organizing and sifting through large amounts
of data.
[0037] The systems and methods described herein apply deep learning
to genetic variant analysis. Deep learning generally refers to
methods that map data through multiple levels of abstraction, where
higher levels represent more abstract entities. The goal of deep
learning is to provide a fully automatic system for learning
complex functions that map inputs to outputs, without using hand
crafted features or rules. One implementation of deep learning
comes in the form of feedforward neural networks, where levels of
abstraction are modeled by multiple non-linear hidden layers.
[0038] In brief, embodiments described herein provide systems and
methods that receive as input a DNA or RNA sequence, extracts
features, and apply multiple layers of nonlinear processing units
of a cell variable predictor ("CVP") to compute a cell variable,
which corresponds to a measurable quantity within a cell, for
different conditions, such as tissue types. To distinguish a cell
variable that corresponds to a measureable quantity for a specific
condition, such as a tissue type, from a cell variable that is a
combination of measureable quantities from multiple conditions, we
refer to the former as a "condition-specific cell variable" and the
latter as a "non-specific cell variable". In embodiments, the CVP
is applied to a DNA or RNA sequence and/or features extracted from
the sequences, containing a genetic variant, and also to a
corresponding reference (e.g., wild type) sequence to determine how
much the cell variable changes because of the variant. The systems
and methods can be applied to naturally occurring genomic
sequences, mini-gene reporters, edited genomic sequences, such as
those edited using CRISPR-Cas9, genomic sequences targeted by
therapies, and other genomic sequences. The change in the cell
variable in different conditions may be used to classify
disease-causing variants, compute a score for how deleterious a
variant is, prioritize variants for subsequent processing,
interpret the mechanism by which a variant operates, and determine
the effect of a therapy. Further, an unknown variant can be given a
high score for deleteriousness if it induces a change in a
particular cell variable that is similar to changes in the same
cell variable that are induced by one or more variants that are
known to be deleterious.
[0039] In embodiments, the CVP comprises a deep neural network
having multiple layers of processing units and possibly millions of
parameters. The CVP may be trained using a dataset of DNA or RNA
sequences and corresponding measurements of cell variables, using a
deep learning training method that adjusts the strengths of the
connections between processing units in adjacent layers.
Specialized training methods are described, including a multi-task
training method that improves accuracy. The mechanism by which a
mutation causes a deleterious change in a cell variable may in some
instances be determined by identifying features or groups of
features that are changed by the mutation and that cause the cell
variable to change, which can be computed by substituting features
derived from the variant sequence one by one into the reference
sequence or by backpropagating the cell variable change back to the
input features.
[0040] If a change related to a variant of any cell variable is
large enough compared to a reference, the variant warrants
investigation for deleteriousness. The systems described herein can
thus be used to prioritize genetic variants for further `wet-lab`
investigations, significantly aiding and reducing the costs of
variant discovery. Furthermore, because of the presence of cell
variables in the predictor, the invention can assign `blame` to
variants that are disease causing, and generate appropriate user
visualizations. For example, a variant that changes the splicing
`cell variable` may be targeted by a therapy that targets the
splicing pathway to remediate the disease
[0041] As used herein, the term "reference sequence" means: in the
context of evaluating a variant (as described below), whereupon the
systems described herein compare the variant to a `reference
sequence`, the reference sequence is a DNA or RNA sequence obtained
using genome sequencing, exome sequencing or gene sequencing of an
unrelated individual or a closely related individual (e.g., parent,
sibling, child). Alternatively, the reference sequence may be
derived from the reference human genome, or it may be an
artificially designed sequence.
[0042] As used herein, the term "variant" means: a DNA or RNA
sequence that differs from a reference sequence in one or more
nucleotides, by substitutions, insertions, deletions or any other
changes. The variant sequence may be obtained using genome
sequencing, exome sequencing or gene sequencing of an individual.
Alternatively, the variant sequence may be derived from the
reference human genome, or it may be an artificially designed
sequence. For the purpose of this invention, when a variant is
being evaluated by the system, the sequence containing the variant
as well as surrounding DNA or RNA sequence is included in the
`variant`.
[0043] As used herein, the term "single nucleotide variant" ("SNV")
means: a variant that consists of a substitution to a single
nucleotide.
[0044] As used herein, the term "variant analysis" means: the
procedure (computational or otherwise) of processing a variant,
possibly in addition to surrounding DNA or RNA sequence that
establishes context, for the purpose of variant scoring,
categorization, prioritization, and interpretation.
[0045] As used herein, the term "score" means: a numeric value that
indicates how deleterious a variant is expected to be.
[0046] As used herein, the term "classification" refers to the
classification of a variant. A variant may be classified in
different ways, such as by applying a threshold to the score to
determine if the variant is deleterious or not. The American
College of Medical Genetics recommends a five-way classification:
pathogenic (very likely to contribute to the development of
disease); likely pathogenic (there is strong evidence that the
variant is pathogenic, but the evidence is inconclusive); unknown
significance or VUS (there is not enough evidence to support
classification one way or another); likely benign (there is strong
evidence that the variant is benign, but the evidence is
inconclusive); benign (very likely to be benign).
[0047] As used herein, the terms "rank"/"prioritization" mean: the
process of sorting the scores of a set of variants to determine
which variant should be further investigated. The pathogenic
variants will be at the top, with the benign variants at the
bottom.
[0048] As used herein, the term "cell variable" means: a quantity,
level, potential, or process outcome in the cell that is
potentially relevant to the function of a living cell, and that is
computed by a CVP (see below). There are two types of cell
variables: a "condition-specific cell variable" is a cell variable
that is measured or predicted under a specific condition, such as a
tissue type; a "non-specific cell variable" is a cell variable that
is derived by combining information from across multiple
conditions, for example by subtracting the average cell variable
values across conditions from the cell variable for each condition.
A cell variable can often be quantified by a vector of one or more
real-valued numbers, or by a probability distribution over such a
vector. Examples include the strength of binding between two
molecules (e.g. protein-protein or protein-DNA binding), exon
splicing levels (the fraction of mRNA transcripts in a particular
tissue that contain a particular exon, i.e. percent spliced in),
DNA curvature, DNA methylation, RNA folding interactions.
[0049] As used herein, the term "event" means: in the context of a
splicing-related cell variable (e.g. the fraction of transcripts
with an exon spliced in), an observed (measured) alternative
splicing event in the cell where both the genomic features and the
corresponding splicing levels are known for that particular event.
Each event can be used as either a training case or a testing case
for a machine learning system.
[0050] Referring now to FIG. 1, shown therein is a system 100 for
cell variable prediction, comprising a machine learning unit. The
machine learning unit is preferably implemented by a deep neural
network, which is alternatively referred to herein as a "cell
variable predictor" ("CVP") 101. The CVP takes as input a set of
features, including genomic features, and produces an output
intended to mimic a specific cell variable. The quantification of a
cell variable can be represented in such a system by one or more
real-valued numbers on an absolute or relative scale, with or
without meaningful units. In embodiments, the CVP may provide other
outputs in addition to outputs intended to mimic a specific cell
variable.
[0051] The system 100 further comprises a memory 106
communicatively linked to the CVP 101.
[0052] An illustrated embodiment of the CVP 101 comprising a
feedforward neural network having a plurality of layers 102 (i.e.
deep) is shown. Each layer comprises one or more processing units
104, each of which implements a feature detector and/or a
computation that maps an input to an output. The processing units
104 accept a plurality of parameter inputs from other layers and
apply activation functions with associated weights for each such
parameter input to the respective processing unit 104. Generally,
the output of a processing unit of layer l may be provided as input
to one or more processing units of layer l+1.
[0053] Each processing unit may be considered as a processing
"node" of the network and one or more nodes 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 processing unit may be considered to be
associated with a hidden unit or an input unit of the neural
network for a hidden layer or an input layer, respectively. The use
of large (many hidden variables) and deep (multiple hidden layers)
neural networks may improve the predictive performances of the CVP
compared to other systems.
[0054] In embodiments, inputs to the input layer of the CVP can
include genetic information, such as sequences representing DNA,
RNA, features derived from DNA and RNA, and features providing
extra information (e.g. tissue type, age, sex), while outputs at
the output layer of the CVP can include cell variables.
[0055] It will be appreciated that though an illustrative
feedforward network is described herein, the type of neural network
implemented is not limited merely to feedforward neural networks
but can also be applied to any neural networks, including
convolutional neural networks, recurrent neural networks,
auto-encoders and Boltzmann machines.
[0056] In embodiments the system 100 comprises a secondary analysis
unit 114 for receiving the cell variables from the output layer and
providing further analysis, as described below.
[0057] The memory 106 may comprise a database for storing
activations and learned weights for each feature detector, as well
as for storing datasets of genetic information and extra
information and optionally for storing outputs from the CVP 101.
The genetic information may provide a training set comprising
training data. The training data may, for example, be used for
training the CVP 101 to predict cell variables, in which case DNA
and RNA sequences with known cell variables and/or phenotypes may
be provided. The memory 106 may further store a validation set
comprising validation data.
[0058] Generally, during the training stage, the neural network
learns optimized weights for each processing unit. After learning,
the optimized weight configuration can then be applied to test
data. Stochastic gradient descent can be used to train feedforward
neural networks. A learning process (backpropagation), involves for
the most part matrix multiplications, which makes them suitable for
speed up using GPUs. Furthermore, the dropout technique may be
utilized to prevent overfitting.
[0059] The system may further comprise a computing device 110
communicatively linked to the CVP 101 for controlling operations
carried out in the CVP. The computing device may comprise further
input and output devices, such as input peripherals (such as a
computer mouse or keyboard), and/or a display. The computing device
110 may further be linked to a remote device 112 over a wired or
wireless network 108 for transmitting and receiving data. In
embodiments, genetic information is received over the network 108
from the remote device 112 for storage in memory 106. Cell variable
predictions and lists of variants priorities may be displayed to a
user via the display.
[0060] Referring now to FIG. 2, shown therein is a comparison of a
prior (FIG. 2(a)) and currently described (FIG. 2(b)) machine
learning process to predict phenotypes, such as disease risks or
deleteriousness from a genotype. Contrary to the prior approach,
which was described above, the currently described process predicts
a cell variable as an intermediate to the phenotype. As described
above, the inputs 204 to a CVP can include sequences representing
DNA, RNA, features derived from DNA and RNA, and features providing
extra information (e.g. tissue type, age, sex). The cell variables
206 could be, for example, 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. Once determined, the cell
variables can be used by the system to determine how much a variant
causes the cell variable to change. By examining how much a
mutation causes the cell variable to change, the CVP can be used to
score, categorize, and prioritize variants. Specifically, once
determined, the cell variable predictions can act as high-level
features to facilitate more accurate phenotypic predictions,
optionally performed at the secondary analysis unit 114. By
training predictors that predict how genotype influences cell
variables, such as concentrations of proteins, the resultant
machine learning problem is modularized. Moreover, it allows
variants to be related to particular cell variables, thereby
providing a mechanism to explain variants.
[0061] In one embodiment, the variant and a reference sequence are
fed into the input layer of the CVP 101 and the amount of change in
the cell variable is quantified and used to score, categorize and
prioritize the variant by the secondary analysis unit 114.
[0062] In another embodiment, the secondary analysis unit 114
comprises a second system (of similar architecture to the CVP)
trained to predict a phenotype based on the outputs of the cell
variable prediction systems (as illustrated in FIG. 2b). For
example, in the case of spinal muscular atrophy, the cell variable
could be the frequency with which the exon is included when the
gene is being copied to make a protein. Other examples of cell
variables include 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.
[0063] The CVP comprises multiple layers of nonlinear processing
units to compute the cell variable using the raw DNA or RNA
sequence, or features derived from the sequence. In embodiments, in
order to quantify the effect of a variant, the system may first
construct a pair of feature vectors corresponding to the reference
sequence and the variant sequence. Due to the variant, these
genomic feature vectors will be different, but without a further
cell variable predictor it may not be possible to predict whether
those differences would result in any change in phenotype.
Embodiments of the predictive system may therefore infer both the
reference cell variable value and the variant cell variable value
using these two distinct feature vectors. After that, a distance
function that combines the reference and the variant predictions
may be used to produce a single score which summarizes the
magnitude of predicted effect induced by the mutations. Example
distance functions include the absolute difference in expectation,
Kullback-Leibler divergence, and variation distance. Detailed
mathematical formulas of these will be described in a later
paragraph.
[0064] It will be appreciated that process 250 can rely on input
features derived from other types of data besides DNA sequences
(e.g. age, sex, known biomarkers)--the above described inputs are
merely illustrative.
[0065] An aspect of the embodiments described herein is the use of
machine learning to infer predictors that are capable of
generalizing to new genetic contexts and to new cell states. For
example, a predictor may be inferred using reference genome and
data profiling transcripts in healthy tissues, but then applied to
the genome of a cancer cell to ascertain how the distribution of
transcripts changes in the cancer cell. This notion of
generalization is a crucial aspect of the predictors that need to
be inferred. If a predictor is good at generalization, it can
analyze variant sequences that lead to changes in cell variables
that may be indicative of disease state, without needing
experimental measurements from diseased cells.
[0066] Process 250 may address the two problems discussed with
respect to approach 200. Since the cell variables are more closely
related to and more easily determined from genomic sequences than
are phenotypes, learning predictors that map from DNA to cell
variables is usually more straightforward. High-throughput
sequencing technologies are currently generating massive amounts of
data profiling these cell variables under diverse conditions; these
datasets can be used to train larger and more accurate predictors.
Also, since the cell variables correspond to intermediate
biochemically active quantities, such as the concentration of a
gene transcript, they may be good targets for therapies. If high
disease risk is associated with a change in a cell variable
compared to a healthy individual, an effective therapy may consist
of restoring that cell variable to its normal state. Embodiments
may include such cell variables as `exon inclusion or exclusion`,
`alternative splice site selection`, `alternative polyadenylation
site selection`, `RNA- or DNA-binding protein or microRNA
specificity`, and `phosphorylation`.
[0067] Various aspects of the current system and method include:
the method can be applied to raw DNA or RNA sequence or features
extracted from the sequence, such as RNA secondary structures and
nucleosome positions; the method can compute one or more
condition-specific cell variables, without the need for a baseline
average across conditions; the method can detect variants that
affect all condition-specific cell variables in the same way; the
method can compare a variant sequence to a reference sequence,
enabling it to make different predictions for the same variant,
depending on genetic context; the method can compute the
condition-specific cell variables using a deep neural network,
which has at least two layers of processing units; the method does
not require disease labels (e.g., a case population and a control
population); the method can score a variant that has never been
seen before; the method can be used to compute a `distance` between
a variant sequence and a reference sequence, which can be used to
rank the variant; the method can be used to compute a `distance`
between variants, which is useful for classifying unknown variants
based on how similar they are to known variants.
[0068] In the following sections, systems and methods for creating
a condition-specific cell variable predictor for cassette splicing
are described in further detail. First, production of training
targets, and generation of outputs using the systems and methods
will be described. Subsequently, the procedure for training and
optimizing a deep neural network (DNN), such as the CVPs, on a
sparse and unbalanced biological dataset will be described.
Subsequently, example methods to analyze the outputs of the systems
will be described. Further, techniques to analyze the behaviour of
such a DNN in terms of its inputs and gradients will be
described.
[0069] Referring now to FIG. 3, shown therein is a method of
generating target cell variables for training. During training of a
neural network, a family of gradient-following procedures are
performed where weights (".theta.") of a neural network are changed
according to the gradient of a cost function evaluated using the
prediction and the target in a training dataset. To construct the
training procedure, the measured cell variable to be modeled is
represented in a mathematical form, also referred to as the
`target` in a dataset. For example, in predicting the
percent-spliced-in values ("PSI"), two distinct forms could be
provided, the expected PSI and a discretized version of PSI.
[0070] To compute these targets, at block 302, the biological
measurements such as RNA-Seq datasets are processed to produce a
posterior probability distribution p of PSI, using methods such as
cufflinks and the bootstrap binomial model. With posterior
probability of PSI, at block 304, the expected PSI can be computed
by an exact evaluation or an approximation to the following
integral: E(.psi.)=.intg..sub..psi..psi.p(.psi.)d.psi.. The result
is a scalar value between 0 and 1. A regression model to predict
the expected PSI can be trained, with the cost function being
squared loss function or the cross-entropy based on a binomial
distribution with E(.psi.) as the probability of success. In
addition to the expected PSI, a discretized version of PSI may also
be determined at block 306, which is defined by the probability
mass of PSI in k predefined bins with boundaries ranging between 0
and 1. For example, using k=3 bins with a uniform bin width, we
arrive at the `low, mid, high` (LMH) formulation of PSI, which we
also call a `splicing pattern`. With this formulation, p(.psi.) is
discretized to three probabilities{p.sub.low, p.sub.mid,
p.sub.high} for use during training. In particular, p.sub.low is
equal to the probability that PSI is between 0 and 1/3:
p.sub.low=.intg..sub.0.sup.1/3p(.psi.)d.sub..psi.. For the
discretized splicing patterns, the cross entropy cost function can
be used for a classification model.
[0071] Though the preparation of training targets according to
method 300 may be different for different cell variables, the
system architecture applied may be the same or similar.
[0072] Referring now to FIGS. 4 to 9, shown therein are example DNN
architectures for CVPs that predicts splicing levels (.PSI.).
[0073] Though the figures depict possible architecture embodiments,
the number of hidden layers and the number of processing units in
each layer can range widely and may be determined by hand, using
data or using other information;
[0074] In an embodiment, the nodes of the DNN are fully connected,
where each connection is parameterized by a real-valued weight
.theta.. The DNN has multiple layers of non-linearity consisting of
hidden units. The output activation a of each hidden unit v in
layer l processes a sum of weighted outputs from the previous
layer, using a non-linear function f:
a.sub.v.sup.l=f(.SIGMA..sub.m.sup.M.sup.l-1.theta..sub.v,m.sup.la.sub.m.-
sup.l-1)
where M.sup.l represents the number of hidden units in layer l, and
a.sub.0 and M.sub.0 are the input into the model and its
dimensionality, respectively. Different activation functions for
the hidden units can be used, such as the TANH function, SIGMOID,
and the rectified linear unit (RELU).
[0075] Referring now to FIG. 4, shown therein is an example
architecture 400 of a deep neural network that predicts alternative
splicing inclusion levels in a single tissue type i, where the
inclusion level is represented by a real-valued number
.PSI..sub.i.
[0076] Inputs into the first hidden layer 406 consist of genomic
features 402 describing a genomic region; these features 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. To
improve learning, the features can be normalized by the maximum of
the absolute value across all training examples. The purpose of the
first hidden layer is to reduce the dimensionality of the input and
learn a better representation of the feature space.
[0077] The identity of conditions (e.g., tissues) 404, which
consists of a 1-of-T binary variables where T represent the number
of conditions, are then appended to the vector of outputs of the
first hidden layer, together forming the input into the second
hidden layer 408. A third hidden layer 410, or additional hidden
layers may be included if found to be necessary to improve
generalization performance.
[0078] In an embodiment, the final output 412 may be a regression
model that predicts the expected PSI.
[0079] Referring now to FIG. 5, in another embodiment, the
discretized PSI may be predicted by a classification model 512.
FIG. 5 shows an example architecture 500 of a deep neural network
that predicts alternative splicing inclusion levels in a single
tissue type i, where the probability mass function over inclusion
levels is represented by a k-valued vector, depicted here with k=3
values labeled (Low, Medium, High).
[0080] Referring now to FIG. 6, alternatively, the DNN can predict
the difference in PSI (.DELTA.PSI) between two conditions for a
particular exon. FIG. 6 shows an example architecture 600 of a deep
neural network that predicts the difference between the alternative
splicing inclusion levels of two tissue types (conditions) i 602
and j 604. Here, instead of one tissue as input, two different
tissues can be supplied to the inputs.
[0081] Further, three classes can be generated, called decreased
inclusion 606, no change 608, and increased inclusion 610, which
can be similarly generated, but from the .DELTA.PSI distributions.
An interval can be chosen that more finely differentiates
tissue-specific alternative splicing for this task, where a
difference of greater than 0.15 could be labeled as a change in PSI
levels. The probability mass could be summed over the intervals of
-1 to -0.15 for decreased inclusion, -0.15 to 0.15 for no change,
and 0.15 to 1 for increased inclusion.
[0082] Referring now to FIG. 7, shown therein is an example
architecture 700 of a deep neural network that predicts the
alternative splicing inclusion levels of two tissue types i and j,
where the inclusion levels are represented by real-valued numbers
.PSI..sub.i 702 and .PSI..sub.j 704 and the difference in
alternative splicing inclusion levels between the two tissue types
706 is also represented by a real-valued number.
[0083] In embodiments, the classification, regression, and tissue
difference codes may be trained jointly. The benefit is to reuse
the same hidden representations learned by the model, and for each
learning task to improve the performance of another.
[0084] Referring now to FIG. 8, shown therein is an example
architecture 800 of a deep neural network that predicts the
difference between the alternative splicing inclusion levels of two
tissue types i and j, where the probability mass function over
inclusion levels is represented by a k-valued vector, depicted here
with k=3 values labeled (Low, Medium, High) 802 and the probability
mass function over inclusion level differences is represented by a
d-valued vector, here depicted with d=3 values labeled (Decrease,
No Change, Increase) 804.
[0085] Referring now to FIG. 9, shown therein is an example
architecture 900 of a deep neural network that predicts alternative
splicing inclusion levels in T tissue types, where the probability
mass function over inclusion levels is represented by a k-valued
vector, depicted here with k=3 values labeled (Low, Medium, High).
Accordingly, multiple tissues may be trained as different
predictors via multitask learning. The learned representation from
features may be shared across all tissues. FIG. 9 shows an example
architecture of such system.
[0086] Training of the systems will now be described with reference
to FIGS. 10 to 12. Referring now to FIG. 10, shown therein is a
method 1000 for training the cell variable predictors of the
systems described above. At block 1002, the first hidden layer can
be trained using an autoencoder to reduce the dimensionality of the
feature space in an unsupervised manner. An autoencoder is trained
by supplying the input through a non-linear hidden layer, and
reconstructing the input, with tied weights going into and out of
the hidden layer. Alternatively, the weights can be untied. This
method of pretraining the network may initialize learning near a
good local minimum. An autoencoder may be used instead of other
dimensionality reduction techniques like principal component
analysis, because it naturally fits into the CVP's architecture,
and that a non-linear technique may discover a better and more
compact representation of the features. At block 1004, in the
second stage of training, the weights from the input layer to the
first hidden layer (learned from the autoencoder) are fixed, and
the inputs corresponding to tissues are appended. A one-hot
encoding representation may be used, such that specifying a tissue
for a particular training example can take the form [0 1 0 0 0] to
denote the second tissue out of 5 possible types. At block 1006,
the reduced feature set and tissue variables become input into the
second hidden layer. At block 1008, the weights connected to the
second hidden layer and the final hidden layer of the CVP are then
trained together in a supervised manner, with targets being the
expected value of PSI, the discretized version of PSI, the expected
value of .DELTA.PSI, and/or the discretized version of .DELTA.PSI,
depending on architecture. At block 1010, after training these
final two layers, weights from all layers of the CVP may be
fine-tuned by backpropagation.
[0087] In an alternate embodiment, the autoencoder may be omitted
altogether, and all weights of neural network may be trained at
once.
[0088] In one embodiment, the targets consist of (1) PSI for each
of the two tissues, and (2) .DELTA.PSI between the two tissues.
Given a particular exon and N possible tissue types, N.times.N
training examples can be constructed. This construction has
redundancy in that it generates examples where both tissues are the
same in the input to teach the model that it should predict no
change for .DELTA.PSI given identical tissue indices. Additionally,
if the tissues are swapped in the input, a previously increased
inclusion label should become decreased inclusion. The same
rationale extends to the LMH classifier. Generating these
additional examples is one method to incorporate this knowledge
without explicitly specifying it in the model architecture.
[0089] A threshold can be applied to exclude examples from training
if the total number RNA-Seq junction is below a number, such as 10,
to remove low signal training examples.
[0090] In some of the embodiments, multiple tasks may be trained
together. Since each of these tasks might learn at different rates,
learning rates may be allowed to differ. This is to prevent one
task from overfitting too soon and negatively affecting the
performance of another task before the complete model is fully
trained. This may be implemented by having different learning rates
for the weights between the connections of the last hidden layer
and the functions used for classification or regression for each
task.
[0091] To train and test CVPs of the systems described herein, data
may be split into folds at random for cross validation, such as
five approximately equal folds. Each fold may contain a unique set
of genetic information, such as exons that are not found in any of
the other folds. Where five folds are provided, three of the folds
could be used for training, one used for validation, and one held
out for testing. Training can be performed for a fixed number of
epochs and hyperparameters can be selected that give optimal area
under curve ("AUC") performance or data likelihood on the
validation data. The model can then be re-trained using the
selected hyperparameters with both the training and validation
data. Multiple models can be trained this way from the different
folds of data. Predictions from the models on their corresponding
test set can then be used to evaluate the code's performance. To
estimate the confidence intervals, the data can be randomly
partitioned, and the above training procedure can be repeated.
[0092] The CVP's processing unit weights may be initialized with
small random values sampled from a zero-mean Gaussian distribution.
Alternatively it may be initialized with small random values with a
zero-mean uniform distribution. Learning may be performed with
stochastic gradient descent with momentum and dropout, where
mini-batches are constructed as described below. An L1 weight
penalty may be included in the cost function to improve the model
performance by disconnecting features deemed to be not useful by
the predictor. The model's weights may updated after each
mini-batch.
[0093] Referring now to FIG. 11, shown therein is a system to
perform non-uniform sampling of training cases for creating a
mini-batch for training a deep neural network.
[0094] To promote neural networks to better discover patterns in
the inputs that help to distinguish tissue types or genomic
features, a system is provided for biasing the distribution of
training events in the mini-batches. The system comprises training
cases separated into "high-variance" cases and "low-variance"
cases. The set of high-variance training cases is thus selected by
thresholding each case's variance across tissue types or genomic
features. In the illustrated embodiment the "high-variance" cases
are provided in a database 1106, and the "low-variance" cases are
provided in a database 1108. The system further comprises switches
1104 and multiplexers 1102. In use, each row of a mini-batch 1110
is sampled either from a list of high- or low-variance training
cases, depending on a probabilistic {0,1} switch value. The
resulting mini-batch of genomic features and corresponding cell
variable targets can be used for training, such as for training the
architectures in FIGS. 6 and 7.
[0095] Referring now to FIG. 12, shown therein is a method for
training cell variable predictors for ensuring a consistent
backpropagation signal that updates the weights connected to tissue
inputs and biases learning towards the event with large tissue
variability early on before overfitting occurs. According to a
method 1200, at block 1202, all training cases are separated into a
database of "high-variance" cases and a database of "low-variance"
cases, where the variance of each training case is measured as
"variance of the .PSI. training targets across tissue types" and
the threshold for separating high/low is any pre-determined
constant. At block 1204, all events that exhibit large tissue
variability are selected, and mini-batches are constructed based
only on these events. At each training epoch, training cases can be
further sampled (with or without replacement) from the larger pool
of events with low tissue variability, of some pre-determined or
randomized size typically smaller than equal to one fifth of the
mini-batch size. A purpose of method 1200 is to have a consistent
backpropagation signal that updates the weights connected to the
tissue inputs and bias learning towards the event with large tissue
variability early on before overfitting occurs. As training
progresses, the splicing pattern of the events with low tissue
variability is also learned. This arrangement effectively gives the
events with large tissue variability greater importance (i.e. more
weight) during optimization. This may be beneficial to improve the
models' tissue specificity.
[0096] With the above methods for training, techniques to reduce
overfitting can be applied to the system to provide an embodiment
of a CVP with dropout. Along with the use of GPUs, CVPs comprising
of deep neural networks may be a competitive technique for
conducting learning and prediction on biological datasets, with the
advantage that they can be trained quickly, have enough capacity to
model complex relationships, and scale well with the number of
hidden variables and volume of data, making them potentially highly
suitable for `omic` datasets.
[0097] Additionally, the performance of a CVP depends on a good set
of hyperparameters. Instead of conducting a grid search over the
hyperparameter space, Bayesian frameworks can be used to
automatically select a model's hyperparameters. These methods use a
Gaussian Process to search for a joint setting of hyperparameters
that optimize a process's performance on validation data. It uses
the performance measures from previous experiments to decide which
hyperparameters to try next, taking into account the trade-off
between exploration and exploitation. This method eliminates many
of the human judgments involved with hyperparameter optimization
and reduces the time required to find such hyperparameters.
Alternatively, randomized hyperparameter search can be performed,
where the hyperparameters to be optimized is sampled from a uniform
distribution. These methods require only the search range of
hyperparameter values to be specified, as well as how long to run
the optimization for.
[0098] In the following paragraphs, methods for using the outputs
of the CVP for scoring, classifying and prioritizing genetic
variants (with reference to FIG. 13); for scoring variants by
associating cell variable changes with those of other variants
(with reference to FIG. 14); and for interpreting which genetic
features account for variant-induced cell variable changes (with
reference to FIGS. 15 to 18), will be described.
[0099] The systems described above can be used to compute a set of
condition-specific scores for how deleterious a variant is. For
instance, a variant may be found to have a high deleteriousness
score in brain tissue, but not in liver tissue. In this way the
condition-specific cell variables computed as described above can
be used to compute condition-specific deleteriousness scores. To
classify variants as pathogenic, likely pathogenic, unknown
significance (VUS), likely benign or benign, and to prioritize or
rank a set of variants, these sets of scores can be combined.
[0100] According to a method 1300, to quantify the effect of a SNV
(single nucleotide variation) or a combination of mutations (called
in general a variant) using a CVP, at block 1302, a pair of feature
vectors are constructed corresponding to the reference sequence and
the variant sequence. Due to the mutation, these genomic feature
vectors will be different, but without a further CVP it may not be
possible to predict whether those differences will result in any
change in phenotype. At block 1304, the predictive system is
therefore used to compute both the reference cell variable value
and the mutant cell variable value for each condition, using these
two distinct feature vectors. After that, at block 1306, a distance
function that combines the reference and the mutant predictions can
be used to produce a single score for each condition, which
summarizes the magnitude of predicted effect induced by the
mutations. Because large change of cell variables is likely to
cause diseases, without further information about a particular
diseases and a particular cell variable, high scoring mutations are
assumed to cause diseases.
[0101] Examples of distance functions are the expected difference,
Kullback-Leibler divergence, and variation distance. In the
following, we describe each of these distance functions in detail
using a LMH splicing predictor as an example.
[0102] The expected difference represents the absolute value of the
difference induced by the mutation in the expected value of a cell
variable. For an LMH PSI predictor, the predicted reference
splicing patterns {p.sub.low.sup.wt, p.sub.mid.sup.wt,
p.sub.high.sup.wt} and the predicted mutant splicing patterns
{p.sub.low.sup.mut, p.sub.mid.sup.mut, p.sub.high.sup.mut} are
computed using the reference and mutant feature vectors as inputs.
Then, the expected value of the predicted cell variable with and
without the mutation is computed, denoted as .psi..sub.wt and
.psi..sub.mut. The expected value is a weighted average of the PSI
values corresponding to the center of the bins used to define the
splicing pattern. As described above, if three bins are used with
uniform spacing, reference PSI is computed by .psi..sub.wt=1/6
p.sub.low.sup.wt+1/2 p.sub.mid.sup.wt+ p.sub.high.sup.wt. In the
same way, mutant PSI is computed by
.psi..sub.mut=1/6p.sub.low.sup.mut+1/2p.sub.mid.sup.mut+
p.sub.high.sup.mut. The final score is the absolute difference
between the expected PSI: s=|.psi..sub.mut-.psi..sub.wt|. This can
be combined across conditions, by computing the maximum absolute
difference across conditions.
[0103] Kullback-Leibler (KL) divergence is an information theoretic
measure of difference between probability distributions P and
Q:
D KL .function. ( P , Q ) = i .times. P .function. ( i ) .times.
log .times. P ( i ) Q ( i ) . ##EQU00001##
Due to the asymmetric nature of the KL divergence, either
s=D.sub.KL (P.sub.wt, P.sub.mut) or s=D.sub.KL (P.sub.mut,
P.sub.wt) can be used as the distance measure. The KL divergence
can be computed for each condition and the sum (or average) KL
divergence can be computed across conditions, or the maximum KL
divergence can be computed across tissues.
[0104] The variation distance is another measure of difference
between probability distributions. It is the sum of absolute value
of the predicted probabilities. In the LMH splicing predictor
example, s=1/2.SIGMA..sub.s.di-elect cons.{low,mid,high}
|p.sub.s.sup.mut-p.sub.s.sup.wt|. Again, this can be computed for
each condition and then the sum or maximum can be taken across
conditions.
[0105] Once the score of a variant has been computed at block 1306,
at block 1308 the score can be thresholded and/or combined with
other information to classify the variant as pathogenic, likely
pathogenic, unknown significance (VUS), likely benign or
benign.
[0106] Further, at block 1310, given a set of variants, the score
of every variant can be computed and the set of variants can be
reordered so that the highest-scoring (most deleterious) variants
are at the top of the list and the lowest-scoring variants are at
the bottom of the list.
[0107] Referring now to FIG. 14, a method 1400 is shown for
scoring, classifying and prioritizing variants. The method 1400
comprises by, at block 1402, associating the cell variable changes
of variants with those of other variants with known function. For
instance, suppose the system 100 determines that a variant that has
never been seen before causes a change in a particular cell
variable, say the cassette splicing level of a specific exon.
Suppose a nearby variant whose disease function is
well-characterized causes a similar change in the exact same cell
variable, e.g., the splicing level of the same exon. Since
mutations act by changing cellular chemistry, such as the splicing
level of the exon, it can be inferred that the unknown variant
likely has the same functional impact as the known variant. The
system can ascertain the `distance` between two variants in this
fashion using a variety of different measures. Because the system
computes variant-induced changes in a cell variable for different
conditions, this information can be used to more accurately
associate variants with one another. For example, two variants that
induce a similar cell variable change in brain tissue would be
associated more strongly than two variants that induce similar cell
variable changes, but in different tissues.
[0108] Unlike many existing systems, the methods and systems
described here can be used to score, classify, prioritize and
interpret a variant in the context of different reference
sequences. For instance, when a child's variant is compared to a
reference sequence obtained from the reference human genome, the
variant may have a high score, but when the same variant is
compared to the reference sequences obtained from his or her
unaffected parents, the variant may have a low score, indicating
that the variant is likely not the cause of the disease. In
contrast, if the child's variant is found to have a high score when
it is compared to the reference sequences obtained from his or her
parents, then it is more likely to be the cause of the disease.
Another circumstance in which different reference sequences arise
is when the variant may be present in more than one transcript,
which can occur because transcription occurs bidirectionally in the
genome, there may be alternative transcription start sites, there
may be alternative splicing, and for other reasons.
[0109] Referring now to FIGS. 15 to 19, methods will now be
described to identify the impact of features (which may include
nucleotides) on a cell variable CVP prediction.
[0110] It can be useful to determine why a variant changes a cell
variable and leads to disease. A variant leads to a change in
DNA/RNA sequence and/or a change in the DNA/RNA features extracted
from the sequence. However, which particular changes in the
sequence or features are important. An SNV may change more than one
feature (e.g., a protein binding site and RNA secondary structure),
but because of contextual dependence only some of the affected
features play an important role.
[0111] To ascertain this, the system 100 can determine which inputs
(nucleotides or DNA/RNA features) are responsible for changes in
cell variables. In other words, it is useful to know how important
a feature is overall for making a specific prediction, and it is
also useful to know in what way the feature contributes to the
prediction (positively or negatively).
[0112] Referring now to FIG. 15, a first method 1500 to identify
the impact of features on a cell variable CVP prediction works by
computing, at block 1502, the features for the sequence containing
the variant and the features for the sequence that does not have
the variant. At block 1504, both feature vectors are fed into the
cell variable predictor to obtain the two sets of
condition-specific cell variables. At block 1506, a single feature
from the variant sequence is copied into the corresponding feature
in the non-variant sequence and the system is used to compute the
set of condition-specific cell variables. At block 1508, this is
repeated for all features and the feature that produces the set of
condition-specific cell variables that is most similar to the set
of condition-specific cell variables for the variant sequence is
identified. This approach can be extended to test a set of pairs of
features or a set of arbitrary combinations of features.
[0113] Referring now to FIG. 16, a second method 1600 to identify
the impact of features on a cell variable CVP prediction evaluates
the impact of a subset S{1, . . . , n} of input features
x=(x.sub.1, . . . , x.sub.n) on the corresponding cell variable
prediction z=f (x). The method consists of, at block 1602,
constructing a new set of input features {circumflex over
(x)}=({circumflex over (x)}.sub.1, . . . , {circumflex over
(x)}.sub.n) where for each feature index i.di-elect cons.S in the
subset the value {circumflex over (x)}.sub.i has been replaced with
the median value of x.sub.i across the training dataset. At block
1604, this new feature vector is then sent through the cell
variable prediction system in question, resulting in a new
prediction {circumflex over (z)}=f ({circumflex over (x)}). For a
splicing cell variable predictor, this entails replacing genomic
feature x.sub.i with its median value across all events (all exons)
in the training set. The impact of feature subsets of the same size
are comparable, including all cases when |S|=1. Among comparable
feature subsets, those that correspond to the largest decrease in
performance may be deemed to have high impact. At block 1606, the
overall importance of a feature (as opposed to its importance for a
specific training or test case) with regard to a particular dataset
(e.g. a training or test set) can be determined as the average or
median of all its impact scores across all cases in that
dataset.
[0114] Referring now to FIG. 17, a third method 1700 is described
to identify the impact of features on a cell variable CVP
prediction. At block 1702, an example from the dataset is given as
input to the trained model and forward propagated through a CVP
comprising of a neural network to generate an output. At block
1704, the target is modified to a different value compared to the
predicted output; for example, in classification, the class label
would be modified so that it differs from the prediction. At block
1706, the error signal is backpropagated to the inputs. The
resulting signal describes how much each input feature needs to
change in order to make the modified prediction, as well as the
direction. The computation is extremely quick, as it only requires
a single forward and backward pass through the CVP, and all
examples can be calculated in parallel. Features that need to be
changed the most are deemed to be important. At block 1708, the
overall importance of a feature (as opposed to its importance for a
specific training or test case) with regards to a particular
dataset (e.g. a training or test set) can be determined as the
average or median of amount of change across all cases in that
dataset. The benefit of this approach compared to the first is it
can model how multiple features operate simultaneously.
[0115] Referring now to FIG. 18, a complementary method 1800 based
on the method of 1700 to analyze a CVP is to see how features are
used in a tissue-specific manner. At block 1802, this extension
simply receives examples from the dataset corresponding to
particular tissues, and, at block 1804, performs the procedure as
described above [110]. In cases where the cell variable predictor
is tissue-specific (e.g. FIGS. 4-9) this procedure yields
tissue-specific feature importance information.
[0116] Referring now to FIG. 19, shown therein is a detailed
illustration of a method 1900 to generate a visualization for
tissue-specific feature importance based on the method described in
1700 and 1800. At block 1902, input comprising examples from a
dataset corresponding to a particular tissue is provided to the
CVP. At block 1904, tissue-specific cell variable predictions are
provided by the CVP. At block 1906, targets are constructed based
on the cell value predictions, such that there is a mismatch
between the prediction and the target. At block 1908, an update
signal is computed which describes how the weights of the
connection need to change to make the prediction match the target.
At block 1910, an update signal backpropagated to the input,
.DELTA.feature, is further computed. At block 1912, examples from
the dataset are sorted by tissue types. At block 1914, the overall
importance of features for each tissue is computed by taking the
mean of the magnitude of the update signal over the entire dataset.
At block 1916, a visualization is generated, where the importance
of each feature is colored accordingly for each tissue.
[0117] The systems and methods described here can also be used to
determine whether a therapy reverses the effect of a variant on a
pertinent cell variable. For example, an SNV within an intron may
cause a decrease in the cell variable that corresponds to the
inclusion level of a nearby exon, but an oligonucleotide therapy
that targets the same region as the SNV or a different one may
cause the cell variable (inclusion level) to rise to its original
level. Or, a DNA editing system such as CRISPR-Cas9 may be used to
edit the DNA, adding, remove or changing a sequence such that the
cell variable (inclusion level) of the exon rises to its original
level. If the method described here is applied to a variant and a
reference sequence obtained from the reference genome or an
unaffected family member, and the cell variable is found to change
by a certain amount, or if the cell variable has been measured to
change by a certain amount, the following technique can be used to
evaluate putative therapies to see if they correct the change. In
the case of therapies that target the variant sequence, such as by
protein-DNA or protein-RNA binding or by oligonucleotide
hybridization, the effect of the therapy on the variant can be
computed using the CVP, where the reference is taken to be the
variant sequence and the "variant sequence" is now taken to be the
variant sequence modified to account for the effect of the therapy.
If the therapy targets a subsequence of the variant, that
subsequence may be, in silico, modified by randomly changing the
nucleotides, setting them all to a particular value, or some other
method. Alternatively or additionally, when features are extracted
from the modified sequence, features that overlap, fully or
partially, with the targeted subsequence may be set to values that
reflect absence of the feature. The reference (the original
variant) and the modified variant are then fed into the CVP and the
change in the cell variable is computed. This is repeated with a
wide range of therapies, and the efficacy of each therapy can be
determined by how much the therapy-induced change in the cell
variable corrects for the original variant-induced change. In the
case of a DNA editing system, such as CRISPR-Cas9, the procedure is
even more straightforward. The reference is taken to be the
original variant, and the variant is taken to be the edited version
of the variant. The output of the CVP then indicates by how much
the cell variable will change because of the editing.
[0118] Thus, what has been provided is, essentially, a system and
method for computing variant-induced changes in one more
condition-specific cell variables. An exemplary method comprises
computing a set of features from the DNA or RNA sequence containing
the variant, applying a network of at least two layers of
processing units (the deep neural network) to the variant features
to compute the one or more condition-specific variant cell
variables, computing a set of features from a reference DNA or RNA
sequence, applying the deep network to the reference features to
compute the one or more condition-specific reference cell
variables, and computing the variant-induced changes in the one or
more condition-specific cell variables by comparing the one or more
condition-specific reference cell variables to the one or more
condition-specific variant cell variables. In embodiments, the
number of condition-specific cell variables is at least two.
[0119] The deep neural network may be trained using a dataset of
examples, where each example is a measured DNA or RNA sequence and
a corresponding set of measured values of the condition-specific
cell variables, one for each condition, and where the
condition-specific cell variables are not normalized using a
baseline that is determined by combining the condition-specific
cell variables across two or more conditions.
[0120] The set of features may include a binary matrix with 4 rows
and a number of columns equal to the length of the DNA or RNA
sequence and where each column contains a single `1` and three `0`s
and where the row in which each `1` occurs indicates the nucleotide
at the corresponding position in the DNA or RNA sequence. The set
of features includes a set of features may be computed using the
recognition path of an autoencoder that is applied to the binary
matrix. The autoencoder may be trained using a dataset of binary
matrices computed using a set of DNA or RNA sequences of fixed
length. The set of features may also include real and binary
features derived from the DNA or RNA sequence.
[0121] At least part of the deep network may be configured to form
a convolutional network and/or recurrent network. Part of the deep
network that is a recurrent network may be configured to use of
long-term short-term memory.
[0122] The deep neural network may be trained using a dataset of
feature vectors extracted from DNA or RNA and a corresponding set
of measured values of cell variables. The training method may
adjust the parameters of the deep neural network so as to minimize
the sum of the error between the measured cell variables and the
output of the deep neural network. The error may be the squared
difference between the measured cell variable and the corresponding
output of the neural network. The error may be the absolute
difference between the measured cell variable and the corresponding
output of the neural network. The error may be the Kullback-Leibler
divergence between the measured cell variable and the corresponding
output of the neural network. Stochastic gradient descent may be
used to train the deep neural network.
[0123] Dropout may be used to train the deep neural network.
[0124] The hyperparameters of the deep neural network may be
adjusted so as to minimize the error on a separate validation
set.
[0125] The deep neural network may be trained using multitask
learning, where the outputs of the deep neural network are
comprised at least two of the following: a real-valued cell
variable, a probability distribution over a discretized cell
variable, a probability distribution over a real-valued cell
variable, a difference between two real-valued cell variables, a
probability distribution over a discretized difference between two
real-valued cell variables, a probability distribution over the
difference between two real-valued cell variables.
[0126] An input to the deep neural network may indicate the
condition for which the cell variable is computed and the deep
neural network is applied repeatedly to compute each
condition-specific cell variable.
[0127] The output of the deep neural network may comprise one real
value for each condition and the variant-induced change for each
condition may be computed by subtracting the computed reference
cell variable from the computed variant cell variable.
[0128] The output of the deep neural network may comprise a
probability distribution over a discrete variable for each
condition and the variant-induced change for each condition may be
computed by summing the absolute difference between the computed
probabilities for the reference cell variable and the variant cell
variable.
[0129] The output of the deep neural network may comprise a
probability distribution over a discrete variable for each
condition and the variant-induced change for each condition may be
computed using the Kullback-Leibler divergence between the computed
probabilities for the reference cell variable and the variant cell
variable.
[0130] The output of the deep neural network may comprise a
probability distribution over a discrete variable for each
condition and the variant-induced change for each condition may be
computed by first computing the expected value of the reference
cell variable and the variant cell variable, and then subtracting
the expected value of the reference cell variable from the expected
value of the variant cell variable.
[0131] The variant-induced changes in the one or more
condition-specific cell variables may be combined to output a
single numerical variant score. The variant score may be computed
by summing the variant-induced changes across conditions. The
variant score may be computed by summing the squares of the
variant-induced changes across conditions. The variant score may be
computed by summing the outputs of a nonlinear function that are
computed by applying the nonlinear function to the variant-induced
changes across conditions.
[0132] At least two variants and corresponding reference sequences
may be independently processed to compute the variant-induced
changes in one or more condition-specific cell variables for each
variant and corresponding reference sequence. At least two variants
and corresponding reference sequences may be independently
processed to compute the variant score for each variant and
corresponding reference sequence. The variant scores may be used to
prioritize the variants by sorting them according to their scores.
Thresholds may be applied to the score to classify the variant as
deleterious or non-deleterious, or to classify the variant as
pathogenic, likely pathogenic, unknown significance, likely benign
or benign, or to classify the variant using any other discrete set
of labels. A validation data consisting of variants, reference
sequences, and labels may be used to compute the thresholds that
minimize classification error. The scores may be combined with
additional numerical information before the variants are sorted.
The scores may be combined with additional numerical information
before the thresholds are applied. The scores may be combined with
additional numerical information before the thresholds are
applied.
[0133] For one or more pairs of variants, the distance between the
two variants in each pair may be computed by summing the output of
a nonlinear function applied to the difference between the change
in the condition-specific cell variable for the first variant and
the change in the condition-specific cell variable for the second
variant. The nonlinear function may be the square operation. The
nonlinear function may be the absolute operation.
[0134] The deleteriousness label of an unknown variant may be
determined by computing the distance of the variant to one or more
variants of known deleteriousness and outputting the label or the
score of the closest known variant. The deleteriousness value of an
unknown variant may be determined by computing the distance of the
variant to one or more variants of known deleteriousness and then
computing the weighted average of their labels or scores, where the
weights are nonlinear functions of the distances. Two or more
unknown variants may be prioritized, by sorting them according to
their deleteriousness values.
[0135] The mini-batches used during multitask training may be
balanced so that the number of cases that exhibit a large
difference is similar to the number of cases that exhibit a small
difference.
[0136] The genetic variant may be a single nucleotide variant. The
genetic variant may contain two or more distinct single nucleotide
variants. The genetic variant may be a combination of
substitutions, insertions and deletions and not be a single
nucleotide variant. The genetic variant may be obtained by
sequencing the DNA from a patient sample.
[0137] The reference sequence may be obtained by sequencing the DNA
from a close relative of the patient. The reference sequence may be
any DNA or RNA sequence and the variant sequence may be any DNA or
RNA sequence, but where the reference sequence and the variant
sequence are not identical.
[0138] The features may include position-dependent genetic features
such as conservation.
[0139] The most explanatory feature may be determined by examining
each feature in turn, and computing a feature-specific variant
feature vector by copying the feature derived from the variant
sequence onto the features derived from the reference sequence;
using the deep neural network to compute the variant-induced
changes in the one or more condition-specific cell variables for
that feature-specific variant identifying the feature whose
corresponding feature-specific variant-induced changes in the one
or more condition-specific cell variables are most similar to the
variant-induced changes in the one or more condition-specific cell
variables.
[0140] The similarity between the feature-specific variant-induced
changes in the one or more condition-specific cell variables and
the variant-induced changes in the one or more condition-specific
cell variables may be computed by summing the squares of their
differences.
[0141] 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.
* * * * *