SYSTEMS AND METHODS FOR CLASSIFYING, PRIORITIZING AND INTERPRETING GENETIC VARIANTS AND THERAPIES USING A DEEP NEURAL NETWORK

• US 20160364522A1
• Filed: 06/15/2015
• Published: 12/15/2016
• Est. Priority Date: 06/15/2015
• Status: Active Grant

• Current Assignee: Deep Genomics, Inc.
• Original Assignee: Deep Genomics, Inc.
• Inventors: FREY, Brendan, LEUNG, Michael K.K., DELONG, Andrew Thomas, XIONG, Hui Yuan, ALIPANAHI, Babak, LEE, Leo J., BRETSCHNEIDER, Hannes
• Granted Patent: US 10,185,803 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis

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.

Claims

1. A method for computing variant-induced changes in one or more condition-specific cell variables for one or more variants, comprising:

a. computing a set of variant features from a DNA or RNA variant sequence;

b. 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;

c. computing a set of reference features from a DNA or RNA reference sequence;

d. applying the deep neural network to the reference features to compute one or more condition-specific reference cell variables;

e. 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.

2. The method of claim 1, wherein the variant features are computed from one of:

a. a sequence obtained by analyzing the DNA or RNA of a patient;

b. a sequence that would result when a DNA or RNA editing system is applied;

c. a sequence where nucleotides targeted by a therapy are set to fixed values;

d. a sequence where nucleotides targeted by a therapy are set to values other than the existing values;

e. a sequence and features that overlap, fully or partially, with nucleotides that are targeted by a therapy are deactivated.

3. The method of claim 1, wherein the set of variant features and reference features include one or more of the following:

a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA sequence and where each column contains a bit indicating the nucleotide at the corresponding position in the DNA or RNA sequence;

b. a set of features computed using one or more layers of an autoencoder other than the input and output layer;

c. a set of features that includes one ore more of;

RNA secondary structures, nucleosome positions, retroviral repeat elements.

4. The method of claim 1, wherein at least part of the deep neural network is configured to form one or more of:

a. a convolutional neural network;

b. a recurrent neural network;

c. a long-term short-term memory recurrent neural network.

5. The method of claim 1, wherein the output of the neural network comprises computed probabilities for discrete levels of the cell variables, and the variant-induced change is computed by one of:

a. summing an absolute difference between the computed probabilities for the reference cell variable and the variant cell variable;

b. summing a Kullback-Leibler divergence between the computed probabilities of the reference cell variable and the variant cell variable for each condition;

c. first computing an 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.

6. The method of claim 1, wherein the variant-induced changes in the one or more condition-specific cell variables are combined to output a single numerical variant score for each of the one or more variants, the variant score computed by one of:

a. outputting the score for a fixed condition;

b. summing the variant-induced changes across conditions;

c. computing the maximum of the absolute variant-induced changes across conditions.

7. The method of claim 6, wherein thresholds that are fixed or selected using labeled data are applied to each variant 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 another discrete set of labels.

8. The method of claim 1, wherein for one or more pairs of variants, a distance between the two variants in each pair is 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.

9. The method of claim 8, wherein deleteriousness labels or scores of one or more variants are determined by computing the distance of each such variant to one or more variants of known deleteriousness and performing one of:

a) outputting a label or a score of the closest known variant;

b) computing a weighted average of the labels or scores of the known variants, where the weights are nonlinear functions of the distances of the variant to the known variants.

10. The method of claim 1, wherein the variant contains one of:

a) two or more distinct single nucleotide variants;

b) a combination of substitutions, insertions and deletions where the combination is not a single nucleotide variant.

11. The method of claim 1, wherein an explanatory feature is determined by:

a. examining every feature in turn;

b. computing a feature-specific variant feature vector by copying the feature derived from the variant sequence onto the corresponding feature derived from the reference sequence;

c. using the deep neural network to compute the variant-induced changes in the one or more condition-specific cell variables for the feature-specific variant feature vector;

d. 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.

12. A deep neural network for computing variant-induced changes in one or more condition-specific cell variables for one or more variants, comprising

a. an input layer configured to receive as input a set of variant features from a DNA or RNA variant sequence;

and

b. at least two layers of processing units operable to;

i. compute one or more condition-specific variant cell variables;

ii. compute a set of reference features from a DNA or RNA reference sequence;

iii. compute one or more condition-specific reference cell variables;

iv. 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.

13. The system of claim 12, wherein the variant features are computed from one of a) a sequence obtained by analyzing the DNA or RNA of a patient;

b) a sequence that would result when a DNA or RNA editing system is applied;

c) a sequence where nucleotides targeted by a therapy are set to fixed values;

d) a sequence where nucleotides targeted by a therapy are set to values other than the existing values;

e) a sequence and features that overlap, fully or partially, with nucleotides that are targeted by a therapy are deactivated.

14. The system of claim 12, wherein the set of variant features and reference features include one or more of the following:

a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA sequence and where each column contains a bit indicating the nucleotide at the corresponding position in the DNA or RNA sequence;

b. a set of features computed using one or more layers of an autoencoder, other than the input and output layers;

c. a set of features that includes one ore more of RNA secondary structures, nucleosome positions, retroviral repeat elements

15. The system of claim 12, wherein at least part of the deep neural network is configured to form one or more of:

a. a convolutional neural network;

b. a recurrent neural network;

c. a long-term short-term memory recurrent neural network.

16. The system of claim 12, wherein the output of the neural network comprises computed probabilities for discrete levels of the cell variables, and the variant-induced change is computed by one of:

a. summing an absolute difference between the computed probabilities for the reference cell variable and the variant cell variable;

b. summing a Kullback-Leibler divergence between the computed probabilities of the reference cell variable and the variant cell variable for each condition;

c. first computing an 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.

17. The system of claim 12, wherein the variant-induced changes in the one or more condition-specific cell variables are combined to output a single numerical variant score for each of the one or more variants, the variant score computed by one of:

a. outputting the score for a fixed condition;

b. summing the variant-induced changes across conditions;

c. computing the maximum of the absolute variant-induced changes across conditions.

18. The system of claim 12, wherein thresholds that are fixed or selected using labeled data are applied to each variant 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 another discrete set of labels.

19. The system of claim 12, wherein for one or more pairs of variants, a distance between the two variants in each pair is 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.

20. The system of claim 12, wherein deleteriousness labels or scores of one or more variants are determined by computing the distance of each such variant to one or more variants of known deleteriousness and performing one of:

a) outputting a label or a score of the closest known variant;

b) computing a weighted average of the labels or scores of the known variants, where the weights are nonlinear functions of the distances of the variant to the known variants.

21. The system of claim 12, wherein the variant contains one of:

a) two or more distinct single nucleotide variants;

b) a combination of substitutions, insertions and deletions where the combination is not a single nucleotide variant.

22. The system of claim 12, wherein a most explanatory feature is determined by:

a. examining each feature in turn;

b. computing a feature-specific variant feature vector by copying the feature derived from the variant sequence onto the features derived from the reference sequence;

c. 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;

d. 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.

23. A method for training a deep neural network to take as input a DNA or RNA sequence and compute one or more condition-specific cell variables, comprising:

a. establishing a neural network comprising at least two connected layers of processing units;

b. iteratively 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 the 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.

24. The method of claim 23 wherein the error is one or more of:

a. a sum of negative log likelihoods of observing the measured condition-specific cell variables as determined using a corresponding set of probabilities computed by the deep neural network;

b. a sum of squared differences between the measured condition-specific cell variables and the corresponding condition-specific cell variables computed by the deep neural network;

c. a sum of absolute differences between the measured condition-specific cell variables and the corresponding condition-specific cell variables computed by the neural network;

d. a sum of Kullback-Leibler divergences between measured condition-specific cell variable probabilities and corresponding condition-specific cell variable probabilities computed by the neural network.