Predicting Splicing from Primary Sequence with Deep Learning

Cell

Volumn 176, Issue 3, 2019, Pages 535-548.e24

  Jaganathan, Kishore ^a   Kyriazopoulou Panagiotopoulou, Sofia ^a   McRae, Jeremy F ^a   Darbandi, Siavash Fazel ^b   Knowles, David ^c   Li, Yang I ^c   Kosmicki, Jack A ^a,d   Arbelaez, Juan ^b   Cui, Wenwu ^a   Schwartz, Grace B ^b   Chow, Eric D ^e   Kanterakis, Efstathios ^a   Gao, Hong ^a   Kia, Amirali ^a   Batzoglou, Serafim ^a   Sanders, Stephan J ^b   Farh, Kyle Kai How ^a  

^a ILLUMINA INC   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c STANFORD UNIVERSITY   (United States)

^d BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

artificial intelligence; deep learning; genetics; splicing

Indexed keywords

MESSENGER RNA PRECURSOR; RNA PRECURSOR;

ALTERNATIVE RNA SPLICING; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTISM; CLINICAL ARTICLE; COHORT ANALYSIS; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; CRYPTIC SPLICING; DE NOVO MUTATION; DEEP NEURAL NETWORK; GENE MUTATION; GENE STRUCTURE; GENETIC ASSOCIATION; GENETIC VARIABILITY; HUMAN; HUMAN CELL; INTELLECTUAL IMPAIRMENT; INTRONIC MUTATION; MEASUREMENT ACCURACY; MOLECULAR PATHOLOGY; MUTATIONAL ANALYSIS; PREDICTION; PRIORITY JOURNAL; RNA SEQUENCE; RNA SPLICING; SPLICE JUNCTION; SYNONYMOUS MUTATION; ALGORITHM; EXON; FORECASTING; GENETICS; INTRON; METABOLISM; PHYSIOLOGY; PROCEDURES; RNA SPLICE SITE;

ALGORITHMS; ALTERNATIVE SPLICING; AUTISTIC DISORDER; DEEP LEARNING; EXONS; FORECASTING; HUMANS; INTELLECTUAL DISABILITY; INTRONS; NEURAL NETWORKS (COMPUTER); RNA PRECURSORS; RNA SPLICE SITES; RNA SPLICING;

EID: 85060117879     PISSN: 00928674     EISSN: 10974172     Source Type: Journal    
DOI: 10.1016/j.cell.2018.12.015     Document Type: Article

References (52)

1. Amit, M., Donyo, M., Hollander, D., Goren, A., Kim, E., Gelfman, S., Lev-Maor, G., Burstein, D., Schwartz, S., Postolsky, B., et al. Differential GC content between exons and introns establishes distinct strategies of splice-site recognition. Cell Rep. 1 (2012), 543–556.
2. Berget, S.M., Exon recognition in vertebrate splicing. J. Biol. Chem. 270 (1995), 2411–2414.
3. Boerkoel, C.F., Exelbert, R., Nicastri, C., Nichols, R.C., Miller, F.W., Plotz, P.H., Raben, N., Leaky splicing mutation in the acid maltase gene is associated with delayed onset of glycogenosis type II. Am. J. Hum. Genet. 56 (1995), 887–897.
4. Boyd, S., Cortes, C., Mohri, M., Radovanovic, A., Accuracy at the top. Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q., (eds.) Advances in Neural Processing Systems, 2012, NIPS, 953–961.
5. Close, P., East, P., Dirac-Svejstrup, A.B., Hartmann, H., Heron, M., Maslen, S., Chariot, A., Söding, J., Skehel, M., Svejstrup, J.Q., DBIRD complex integrates alternative mRNA splicing with RNA polymerase II transcript elongation. Nature 484 (2012), 386–389.
6. Cooper, T.A., Wan, L., Dreyfuss, G., RNA and disease. Cell 136 (2009), 777–793.
7. Cummings, B.B., Marshall, J.L., Tukiainen, T., Lek, M., Donkervoort, S., Foley, A.R., Bolduc, V., Waddell, L.B., Sandaradura, S.A., O'Grady, G.L., et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med., 2017, 10.1126/scitranslmed.aal5209 Published online April 19, 2017.
8. De Rubeis, S., He, X., Goldberg, A.P., Poultney, C.S., Samocha, K., Cicek, A.E., Kou, Y., Liu, L., Fromer, M., Walker, S., et al. DDD Study Homozygosity Mapping Collaborative for Autism, UK10K Consortium. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515 (2014), 209–215.
9. Fairbrother, W.G., Yeh, R.F., Sharp, P.A., Burge, C.B., Predictive identification of exonic splicing enhancers in human genes. Science 297 (2002), 1007–1013.
10. Farh, K.K.H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W.J., Beik, S., Shoresh, N., Whitton, H., Ryan, R.J.H., Shishkin, A.A., et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518 (2015), 337–343.
11. Finkel, R.S., Mercuri, E., Darras, B.T., Connolly, A.M., Kuntz, N.L., Kirschner, J., Chiriboga, C.A., Saito, K., Servais, L., Tizzano, E., et al., ENDEAR Study Group. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377 (2017), 1723–1732.
12. Fitzgerald, T.W., Gerety, S.S., Jones, W.D., van Kogelenberg, M., King, D.A., McRae, J., Morley, K.I., Parthiban, V., Al-Turki, S., Ambridge, K., et al., Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519 (2015), 223–228.
13. Gouya, L., Puy, H., Robreau, A.-M., Bourgeois, M., Lamoril, J., Da Silva, V., Grandchamp, B., Deybach, J.-C., The penetrance of dominant erythropoietic protoporphyria is modulated by expression of wildtype FECH. Nat. Genet. 30 (2002), 27–28.
14. Harrow, J., Frankish, A., Gonzalez, J.M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B.L., Barrell, D., Zadissa, A., Searle, S., et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22 (2012), 1760–1774.
15. He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), L. O'Conner, ed. (IEEE), pp. 770–778.
16. Iossifov, I., O'Roak, B.J., Sanders, S.J., Ronemus, M., Krumm, N., Levy, D., Stessman, H.A., Witherspoon, K.T., Vives, L., Patterson, K.E., et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515 (2014), 216–221.
17. Irimia, M., Weatheritt, R.J., Ellis, J.D., Parikshak, N.N., Gonatopoulos-Pournatzis, T., Babor, M., Quesnel-Vallières, M., Tapial, J., Raj, B., O'Hanlon, D., et al. A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell 159 (2014), 1511–1523.
18. Jonkers, I., Kwak, H., Lis, J.T., Genome-wide dynamics of Pol II elongation and its interplay with promoter proximal pausing, chromatin, and exons. eLife, 3, 2014, e02407.
19. Jung, H., Lee, D., Lee, J., Park, D., Kim, Y.J., Park, W.-Y., Hong, D., Park, P.J., Lee, E., Intron retention is a widespread mechanism of tumor-suppressor inactivation. Nat. Genet. 47 (2015), 1242–1248.
20. Lee, H., Deignan, J.L., Dorrani, N., Strom, S.P., Kantarci, S., Quintero-Rivera, F., Das, K., Toy, T., Harry, B., Yourshaw, M., et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA 312 (2014), 1880–1887.
21. Lek, M., Karczewski, K.J., Minikel, E.V., Samocha, K.E., Banks, E., Fennell, T., O'Donnell-Luria, A.H., Ware, J.S., Hill, A.J., Cummings, B.B., et al., Exome Aggregation Consortium. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536 (2016), 285–291.
22. Li, Y.I., Knowles, D.A., Humphrey, J., Barbeira, A.N., Dickinson, S.P., Im, H.K., Pritchard, J.K., Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50 (2018), 151–158.
23. Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al., GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45 (2013), 580–585.
24. Luco, R.F., Pan, Q., Tominaga, K., Blencowe, B.J., Pereira-Smith, O.M., Misteli, T., Regulation of alternative splicing by histone modifications. Science 327 (2010), 996–1000.
25. Maurano, M.T., Humbert, R., Rynes, E., Thurman, R.E., Haugen, E., Wang, H., Reynolds, A.P., Sandstrom, R., Qu, H., Brody, J., et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337 (2012), 1190–1195.
26. McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R.S., Thormann, A., Flicek, P., Cunningham, F., The Ensembl variant effect predictor. Genome Biol., 17, 2016, 122.
27. McRae, J.F., Clayton, S., Fitzgerald, T.W., Kaplanis, J., Prigmore, E., Rajan, D., Sifrim, A., Aitken, S., Akawi, N., Alvi, M., et al., Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542 (2017), 433–438.
28. Neale, B.M., Kou, Y., Liu, L., Ma'ayan, A., Samocha, K.E., Sabo, A., Lin, C.-F., Stevens, C., Wang, L.-S., Makarov, V., et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485 (2012), 242–245.
29. Pertea, M., Lin, X., Salzberg, S.L., GeneSplicer: A new computational method for splice site prediction. Nucleic Acids Res. 29 (2001), 1185–1190.
30. Reese, M.G., Eeckman, F.H., Kulp, D., Haussler, D., Improved splice site detection in Genie. J. Comput. Biol. 4 (1997), 311–323.
31. Samocha, K.E., Robinson, E.B., Sanders, S.J., Stevens, C., Sabo, A., McGrath, L.M., Kosmicki, J.A., Rehnström, K., Mallick, S., Kirby, A., et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46 (2014), 944–950.
32. Sanders, S.J., Murtha, M.T., Gupta, A.R., Murdoch, J.D., Raubeson, M.J., Willsey, A.J., Ercan-Sencicek, A.G., DiLullo, N.M., Parikshak, N.N., Stein, J.L., et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485 (2012), 237–241.
33. Sanders, S.J., He, X., Willsey, A.J., Ercan-Sencicek, A.G., Samocha, K.E., Cicek, A.E., Murtha, M.T., Bal, V.H., Bishop, S.L., Dong, S., et al., Autism Sequencing Consortium. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87 (2015), 1215–1233.
34. Schwartz, S., Meshorer, E., Ast, G., Chromatin organization marks exon-intron structure. Nat. Struct. Mol. Biol. 16 (2009), 990–995.
35. Shirai, C.L., Ley, J.N., White, B.S., Kim, S., Tibbitts, J., Shao, J., Ndonwi, M., Wadugu, B., Duncavage, E.J., Okeyo-Owuor, T., et al. Mutant U2AF1 expression alters hematopoiesis and Pre-mRNA splicing in vivo. Cancer Cell 27 (2015), 631–643.
36. Soemedi, R., Cygan, K.J., Rhine, C.L., Wang, J., Bulacan, C., Yang, J., Bayrak-Toydemir, P., McDonald, J., Fairbrother, W.G., Pathogenic variants that alter protein code often disrupt splicing. Nat. Genet. 49 (2017), 848–855.
37. Spies, N., Nielsen, C.B., Padgett, R.A., Burge, C.B., Biased chromatin signatures around polyadenylation sites and exons. Mol. Cell 36 (2009), 245–254.
38. Supek, F., Miñana, B., Valcárcel, J., Gabaldón, T., Lehner, B., Synonymous mutations frequently act as driver mutations in human cancers. Cell 156 (2014), 1324–1335.
39. Tan, T.Y., Dillon, O.J., Stark, Z., Schofield, D., Alam, K., Shrestha, R., Chong, B., Phelan, D., Brett, G.R., Creed, E., et al. Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr. 171 (2017), 855–862.
40. Tennessen, J.A., Bigham, A.W., O'Connor, T.D., Fu, W., Kenny, E.E., Gravel, S., McGee, S., Do, R., Liu, X., Jun, G., et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337 (2012), 64–69.
41. The GTEx Consortium, Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348 (2015), 648–660.
42. Turner, T.N., Hormozdiari, F., Duyzend, M.H., McClymont, S.A., Hook, P.W., Iossifov, I., Raja, A., Baker, C., Hoekzema, K., Stessman, H.A., et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am. J. Hum. Genet. 98 (2016), 58–74.
43. Ule, J., Jensen, K.B., Ruggiu, M., Mele, A., Ule, A., Darnell, R.B., CLIP identifies Nova-regulated RNA networks in the brain. Science 302 (2003), 1212–1215.
44. van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., Kavukcuoglu, K., WaveNet: A generative model for raw audio. ArXiv, 2016 ArXiv:1609.03499.
45. Wang, Z., Burge, C.B., Splicing regulation: From a parts list of regulatory elements to an integrated splicing code. RNA 14 (2008), 802–813.
46. Wang, Z., Rolish, M.E., Yeo, G., Tung, V., Mawson, M., Burge, C.B., Systematic identification and analysis of exonic splicing silencers. Cell 119 (2004), 831–845.
47. Wang, E.T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S.F., Schroth, G.P., Burge, C.B., Alternative isoform regulation in human tissue transcriptomes. Nature 456 (2008), 470–476.
48. Wu, J., Anczuków, O., Krainer, A.R., Zhang, M.Q., Zhang, C., OLego: Fast and sensitive mapping of spliced mRNA-seq reads using small seeds. Nucleic Acids Res. 41 (2013), 5149–5163.
49. Xiong, H.Y., Alipanahi, B., Lee, L.J., Bretschneider, H., Merico, D., Yuen, R.K.C., Hua, Y., Gueroussov, S., Najafabadi, H.S., Hughes, T.R., et al. The human splicing code reveals new insights into the genetic determinants of disease. Science, 347, 2015, 1254806.
50. Yang, Y., Muzny, D.M., Xia, F., Niu, Z., Person, R., Ding, Y., Ward, P., Braxton, A., Wang, M., Buhay, C., et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA 312 (2014), 1870–1879.
51. Yeo, G., Burge, C.B., Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11 (2004), 377–394.
52. Yoshida, K., Sanada, M., Shiraishi, Y., Nowak, D., Nagata, Y., Yamamoto, R., Sato, Y., Sato-Otsubo, A., Kon, A., Nagasaki, M., et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478 (2011), 64–69.