Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies

PLoS ONE

Volumn 13, Issue 3, 2018, Pages

  Korvigo, Ilia ^a,b,e   Afanasyev, Andrey ^a,c   Romashchenko, Nikolay ^b   Skoblov, Mikhail ^a,d  

^a MOSCOW INSTITUTE OF PHYSICS AND TECHNOLOGY   (Russian Federation)

^b ALL RUSSIA RESEARCH INSTITUTE FOR AGRICULTURAL MICROBIOLOGY   (Russian Federation)

^c IBinom Inc   (United States)

^d RESEARCH CENTRE FOR MEDICAL GENETICS   (Russian Federation)

^e ITMO UNIVERSITY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; COMPUTER PREDICTION; DEEP LEARNING; GENE DELETION; GENE ONTOLOGY; GENETIC ALGORITHM; RECEIVER OPERATING CHARACTERISTIC; SINGLE NUCLEOTIDE POLYMORPHISM; WHOLE EXOME SEQUENCING; BIOLOGICAL MODEL; DNA SEQUENCE; EXOME; INTERNET; MACHINE LEARNING; PROCEDURES;

EXOME; INTERNET; MACHINE LEARNING; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE; SEQUENCE ANALYSIS, DNA;

EID: 85043772279     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0192829     Document Type: Article

References (43)

1. Ng SB, Nickerson DA, Bamshad MJ, Shendure J. Massively parallel sequencing and rare disease. Human Molecular Genetics. 2010; 19(R2):R119-R124. https://doi.org/10.1093/hmg/ddq390 PMID: 20846941
2. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Research. 2011; 39(17):37-43. https://doi.org/10.1093/nar/gkr407
3. Ng PC, Henikoff S. Predicting the Effects of Amino Acid Substitutions on Protein Function. Annu Rev Genom Hum Genet. 2006; 7(1):61-80. https://doi.org/10.1146/annurev.genom.7.080505.115630
4. Thusberg J, Vihinen M. Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Human Mutation. 2009; 30(5):703-714. https://doi.org/10.1002/humu.20938 PMID: 19267389
5. Cooper GM, Goode DL, Ng SB, Sidow A, Bamshad MJ, Shendure J, et al. Single-nucleotide evolutionary constraint scores highlight disease-causing mutations. Nature Methods. 2010; 7(4):250-251. https://doi.org/10.1038/nmeth0410-250 PMID: 20354513
6. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Research. 2010; 20(1):110-121. https://doi.org/10.1101/gr.097857.109 PMID: 19858363
7. Liu X, Jian X, Boerwinkle E. dbNSFP: A lightweight database of human nonsynonymous SNPs and their functional predictions. Human Mutation. 2011; 32(8):894-899. https://doi.org/10.1002/humu.21517 PMID: 21520341
8. Liu X, Jian X, Boerwinkle E. dbNSFP v2.0: A database of human non-synonymous SNVs and their functional predictions and annotations. Human Mutation. 2013; 34(9):1-14. https://doi.org/10.1002/humu.22376
9. Liu X, Wu C, Li C, Boerwinkle E. dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs. Human Mutation. 2016; 37(3):235-241. https://doi.org/10.1002/humu.22932 PMID: 26555599
10. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genetics in medicine: official journal of the American College of Medical Genetics. 2013; 15(7):565-74. https://doi.org/10.1038/gim.2013.73
11. Shihab Ha, Gough J, Cooper DN, Stenson PD, Barker GLa, Edwards KJ, et al. Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Human Mutation. 2013; 34(1):57-65. https://doi.org/10.1002/humu.22225 PMID: 23033316
12. Gonzalez-Pérez A, López-Bigas N. Improving the Assessment of the Outcome of Nonsynonymous SNVs with a Consensus Deleteriousness Score, Condel. The American Journal of Human Genetics. 2011; 88(4):440-449. https://doi.org/10.1016/j.ajhg.2011.03.004 PMID: 21457909
13. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: A library of protein families and subfamilies indexed by function. Genome Research. 2003; 13(9):2129-2141. https://doi.org/10.1101/gr.772403 PMID: 12952881
14. Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 2006; 22(22):2729-2734. https://doi.org/10.1093/bioinformatics/btl423 PMID: 16895930
15. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research. 2003; 31(13):3812-3814. https://doi.org/10.1093/nar/gkg509 PMID: 12824425
16. Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R. WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC genomics. 2013; 14 Suppl 3(Suppl 3):S6. https://doi.org/10.1186/1471-2164-14-S3-S6 PMID: 23819482
17. Schwarz JM, Rodelsperger C, Schuelke M, Seelow D. Mutation Taster evaluates disease-causing potential of sequence alterations. Nature Publishing Group. 2010; 7(8):575-576.
18. Li B, Krishnan VG, Mort ME, Xin F, Kamati KK, Cooper DN, et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics. 2009; 25(21):2744-2750. https://doi.org/10.1093/bioinformatics/btp528 PMID: 19734154
19. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nature Methods. 2010; 7(4):248-249. https://doi.org/10.1038/nmeth0410-248 PMID: 20354512
20. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE. 2012; 7(10). https://doi.org/10.1371/journal.pone.0046688
21. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nature genetics. 2014; https://doi.org/10.1038/ng.2892 PMID: 24487276
22. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++. PLoS Comput Biol. 2010; 6(12): e1001025. https://doi.org/10.1371/journal.pcbi.1001025 PMID: 21152010
23. Cooper GM. Distribution and intensity of constraint in mammalian genomic sequence. Genome Research. 2005; 15(7):901-913. https://doi.org/10.1101/gr.3577405 PMID: 15965027
24. Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Human Molecular Genetics. 2015; 24(8):2125-2137. https://doi.org/10.1093/hmg/ddu733 PMID: 25552646
25. Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Identification of deleterious mutations within three human genomes. 2009; p. 1553-1561.
26. Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Human Mutation. 2011; 32(4):358-368. https://doi.org/10.1002/humu.21445 PMID: 21412949
27. Li MX, Gui HS, Kwan JSH, Bao SY, Sham PC. A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases. Nucleic Acids Research. 2012; 40(7). https://doi.org/10.1093/nar/gkr1257
28. Wager S, Wang S, Liang P. Dropout Training as Adaptive Regularization. Nips. 2013; p. 1-11.
29. Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics (Oxford, England). 2014; 31(5):761-763. https://doi.org/10.1093/bioinformatics/btu703
30. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nature Genetics. 2016;(November2015). https://doi.org/10.1038/ng.3477 PMID: 26727659
31. Morrison AC, Voorman A, Johnson AD, Liu X, Yu J, Li A, et al. Whole-genome sequence-based analysis of high-density lipoprotein cholesterol. Nature Genetics. 2013; 45(8):899-901. https://doi.org/10.1038/ng.2671 PMID: 23770607
32. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research. 2005; 33 (DATABASE ISS.):514-517.
33. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w 1118; iso-2; iso-3. Fly. 2012; 6(2):80-92. https://doi.org/10.4161/fly.19695 PMID: 22728672
34. Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. Journal of Machine Learning Research (JMLR). 2013; 28(2010):1139-1147.
35. Duchi J, Hazan E, Singer Y. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research. 2011; 12:2121-2159.
36. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: prevent NN from overfitting. Journal of Machine Learning Research. 2014; 15:1929-1958.
37. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). 2010; 9: 249-256.
38. Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems. 2012; p. 1-9.
39. Gülçehre Ç, Bengio Y. Knowledge Matters: Importance of Prior Information for Optimization. Journal of Machine Learning Research. 2016; 17(8):1-32.
40. Bergstra J, Bardenet R, Bengio Y, Kegl B. Algorithms for Hyper-Parameter Optimization. Advances in Neural Information Processing Systems. 2011; p. 2546-2554.
41. Lessmann S, Stahlbock R, Crone SF. Optimizing Hyperparameters of Support Vector Machines by Genetic Algorithms. Proceedings of the 2005 International Conference on Artificial Intelligence (ICAI 2005). 2005; p. 74-82.
42. Chetlur S, Woolley C. cuDNN: Efficient Primitives for Deep Learning. arXiv preprint arXiv:⋯.2014; p. 1-9.
43. Blake JA, Christie KR, Dolan ME, Drabkin HJ, Hill DP, Ni L, et al. Gene ontology consortium: Going forward. Nucleic Acids Research. 2015; 43(D1):D1049-D1056. https://doi.org/10.1093/nar/gku1179