MutPred Splice: Machine learning-based prediction of exonic variants that disrupt splicing

Genome Biology

Volumn 15, Issue 1, 2014, Pages

  Mort, Matthew ^a   Sterne Weiler, Timothy ^d,e   Li, Biao ^b   Ball, Edward V ^a   Cooper, David N ^a   Radivojac, Predrag ^c   Sanford, Jeremy R ^d   Mooney, Sean D ^b  

^a CARDIFF UNIVERSITY   (United Kingdom)

^b BUCK INSTITUTE FOR RESEARCH ON AGING   (United States)

^c INDIANA UNIVERSITY   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA; RNA PRECURSOR; RNA SPLICING;

ARTICLE; CLASSIFICATION ALGORITHM; COMPUTER ANALYSIS; COMPUTER MODEL; COMPUTER PREDICTION; COMPUTER PROGRAM; CONTROLLED STUDY; EXON SKIPPING; GENE MUTATION; GENETIC ALGORITHM; GENETIC ASSOCIATION; GENETIC VARIABILITY; HUMAN; INTERMETHOD COMPARISON; MACHINE LEARNING; PROCESS DEVELOPMENT; RNA SPLICING; SOMATIC MUTATION; ALTERNATIVE RNA SPLICING; EXON; GENETIC VARIATION; GENETICS; INTRON; MISSENSE MUTATION; MUTATION; NEOPLASM; SILENCER ELEMENT; SINGLE NUCLEOTIDE POLYMORPHISM; TUMOR SUPPRESSOR GENE;

ALTERNATIVE SPLICING; EXONS; GENES, TUMOR SUPPRESSOR; GENETIC VARIATION; HUMANS; INTRONS; MACHINE LEARNING; MUTATION; MUTATION, MISSENSE; NEOPLASMS; POLYMORPHISM, SINGLE NUCLEOTIDE; RNA PRECURSORS; RNA SPLICE SITES; SILENCER ELEMENTS, TRANSCRIPTIONAL;

EID: 84892666434     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/gb-2014-15-1-r19     Document Type: Article

References (78)

1. Stenson PD, Mort M, Ball EV, Shaw K, Phillips AD, Cooper DN: The human gene mutation database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet 2014, 133:1-9.
2. Ng PC, Henikoff S: SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003, 31:3812-3814.
3. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods 2010, 7:248-249.
4. Ferrer-Costa C, Gelpi JL, Zamakola L, Parraga I, de la Cruz X, Orozco M: PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics 2005, 21:3176-3178.
5. Ryan M, Diekhans M, Lien S, Liu Y, Karchin R: LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics 2009, 25:1431-1432.
6. Bromberg Y, Rost B: SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 2007, 35:3823-3835.
7. Yue P, Melamud E, Moult J: SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 2006, 7:166.
8. Li B, Krishnan VG, Mort ME, Xin F, Kamati KK, Cooper DN, Mooney SD, Radivojac P: Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 2009, 25:2744-2750.
9. Gonzalez-Perez A, Lopez-Bigas N: Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am J Hum Genet 2011, 88:440-449.
10. Wang GS, Cooper TA: Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet 2007, 8:749-761.
11. Warf MB, Berglund JA: Role of RNA structure in regulating pre-mRNA splicing. Trends Biochem Sci 2010, 35:169-178.
12. Watakabe A, Tanaka K, Shimura Y: The role of exon sequences in splice site selection. Genes Dev 1993, 7:407-418.
13. Keren H, Lev-Maor G, Ast G: Alternative splicing and evolution: diversification, exon definition and function. Nat Rev Genet 2010, 11:345-355.
14. Cartegni L, Wang J, Zhu Z, Zhang MQ, Krainer AR: ESEfinder: a web resource to identify exonic splicing enhancers. Nucleic Acids Res 2003, 31:3568-3571.
15. Fairbrother WG, Yeh RF, Sharp PA, Burge CB: Predictive identification of exonic splicing enhancers in human genes. Science 2002, 297:1007-1013.
16. Goren A, Ram O, Amit M, Keren H, Lev-Maor G, Vig I, Pupko T, Ast G: Comparative analysis identifies exonic splicing regulatory sequences-The complex definition of enhancers and silencers. Mol Cell 2006, 22:769-781.
17. Stadler MB, Shomron N, Yeo GW, Schneider A, Xiao X, Burge CB: Inference of splicing regulatory activities by sequence neighborhood analysis. PLoS Genet 2006, 2:e191.
18. Wang Z, Rolish ME, Yeo G, Tung V, Mawson M, Burge CB: Systematic identification and analysis of exonic splicing silencers. Cell 2004, 119:831-845.
19. Zhang XH, Chasin LA: Computational definition of sequence motifs governing constitutive exon splicing. Genes Dev 2004, 18:1241-1250.
20. Kanopka A, Muhlemann O, Akusjarvi G: Inhibition by SR proteins of splicing of a regulated adenovirus pre-mRNA. Nature 1996, 381:535-538.
21. Wang Z, Burge CB: Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA 2008, 14:802-813.
22. Krawczak M, Thomas NS, Hundrieser B, Mort M, Wittig M, Hampe J, Cooper DN: Single base-pair substitutions in exon-intron junctions of human genes: nature, distribution, and consequences for mRNA splicing. Hum Mutat 2007, 28:150-158.
23. Lim KH, Ferraris L, Filloux ME, Raphael BJ, Fairbrother WG: Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes. Proc Natl Acad Sci USA 2011, 108:11093-11098.
24. Lopez-Bigas N, Audit B, Ouzounis C, Parra G, Guigo R: Are splicing mutations the most frequent cause of hereditary disease? FEBS Lett 2005, 579:1900-1903.
25. Sterne-Weiler T, Howard J, Mort M, Cooper DN, Sanford JR: Loss of exon identity is a common mechanism of human inherited disease. Genome Res 2011, 21:1563-1571.
26. Baralle M, Baralle D, De Conti L, Mattocks C, Whittaker J, Knezevich A, Ffrench-Constant C, Baralle FE: Identification of a mutation that perturbs NF1 a gene splicing using genomic DNA samples and a minigene assay. J Med Genet 2003, 40:220-222.
27. Nissim-Rafinia M, Kerem B: Splicing regulation as a potential genetic modifier. Trends Genet 2002, 18:123-127.
28. Cooper DN, Krawczak M, Polychronakos C, Tyler-Smith C, Kehrer-Sawatzki H: Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Hum Genet 2013, 132:1077-1130.
29. Lee Y, Gamazon ER, Rebman E, Lee S, Dolan ME, Cox NJ, Lussier YA: Variants affecting exon skipping contribute to complex traits. PLoS Genet 2012, 8:e1002998.
30. Cartegni L, Chew SL, Krainer AR: Listening to silence and understanding nonsense: exonic mutations that affect splicing. Nat Rev Genet 2002, 3:285-298.
31. Hua Y, Sahashi K, Hung G, Rigo F, Passini MA, Bennett CF, Krainer AR: Antisense correction of SMN2 splicing in the CNS rescues necrosis in a type III SMA mouse model. Genes Dev 2010, 24:1634-1644.
32. Reese MG, Eeckman FH, Kulp D, Haussler D: Improved splice site detection in Genie. J Comput Biol 1997, 4:311-323.
33. Yeo G, Burge CB: Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 2004, 11:377-394.
34. Lim KH, Fairbrother WG: Spliceman-a computational web server that predicts sequence variations in pre-mRNA splicing. Bioinformatics 2012, 28:1031-1032.
35. Woolfe A, Mullikin JC, Elnitski L: Genomic features defining exonic variants that modulate splicing. Genome Biol 2010, 11:R20.
36. Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C: Human splicing finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009, 37:e67.
37. Spurdle AB, Couch FJ, Hogervorst FB, Radice P, Sinilnikova OM: Prediction and assessment of splicing alterations: implications for clinical testing. Hum Mutat 2008, 29:1304-1313.
38. Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA: A map of human genome variation from population-scale sequencing. Nature 2010, 467:1061-1073.
39. Human gene mutation database. [http://www.hgmd.org]
40. Chapelle O, Sch-lkopf B, Zien A: Semi-supervised Learning. Cambridge, Mass: MIT Press; 2006.
41. Sinha R, Hiller M, Pudimat R, Gausmann U, Platzer M, Backofen R: Improved identification of conserved cassette exons using Bayesian networks. BMC Bioinformatics 2008, 9:477.
42. Leslie C, Eskin E, Noble WS: The spectrum kernel: a string kernel for SVM protein classification. Pac Symp Biocomput 2002:564-575.
43. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A: Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 2010, 20:110-121.
44. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, Weinstock GM, Wilson RK, Gibbs RA, Kent WJ, Miller W, Haussler D: Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 2005, 15:1034-1050.
45. Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM, Wong M, Sloan CA, Rosenbloom KR, Roe G, Rhead B, Raney BJ, Pohl A, Malladi VS, Li CH, Lee BT, Learned K, Kirkup V, Hsu F, Heitner S, Harte RA, Haeussler M, Guruvadoo L, Goldman M, Giardine BM, Fujita PA, Dreszer TR, Diekhans M, Cline MS, Clawson H, Barber GP, Haussler D, Kent WJ: The UCSC genome browser database: extensions and updates 2013. Nucleic Acids Res 2013, 41:D64-D69.
46. Breiman L: Random Forests. Machine Learning 2001, 45:5-32.
47. Bao L, Cui Y: Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics 2005, 21:2185-2190.
48. Kaminker JS, Zhang Y, Watanabe C, Zhang Z: CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res 2007, 35:W595-W598.
49. Olatubosun A, Valiaho J, Harkonen J, Thusberg J, Vihinen M: PON-P: integrated predictor for pathogenicity of missense variants. Hum Mutat 2012, 33:1166-1174.
50. Li Y, Fang Y, Fang J: Predicting residue-residue contacts using random forest models. Bioinformatics 2011, 27:3379-3384.
51. Vapnik VN: The Nature of Statistical Learning Theory. New York: Springer; 1995.
52. Vapnik VN: Statistical Learning Theory. New York: Wiley; 1998.
53. Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ: Weka: Practical machine learning tools and techniques with Java implementations. 1999 [http://www.cs.waikato.ac.nz/~ihw/papers/99IHWETF- LT-MJ-GH-SJC-Weka.pdf]
54. Leskovec J, Shawe-Taylor J: Linear programming boosting for uneven datasets. [http://cs.stanford.edu/people/jure/pubs/textbooster-icml03.pdf]
55. Di Giacomo D, Gaildrat P, Abuli A, Abdat J, Fr-bourg T, Tosi M, Martins A: Functional analysis of a large set of BRCA2 exon 7 variants highlights the predictive value of hexamer scores in detecting alterations of exonic splicing regulatory elements. Hum Mutat 2013, 34:1547-1557.
56. Rice GI, Reijns MA, Coffin SR, Forte GM, Anderson BH, Szynkiewicz M, Gornall H, Gent D, Leitch A, Botella MP, Fazzi E, Gener B, Lagae L, Olivieri I, Orcesi S, Swoboda KJ, Perrino FW, Jackson AP, Crow YJ: Synonymous mutations in RNASEH2A create cryptic splice sites impairing RNase H2 enzyme function in Aicardi-Gouti-res syndrome. Hum Mutat 2013, 34:1066-1070.
57. Sharma VP, Fenwick AL, Brockop MS, McGowan SJ, Goos JA, Hoogeboom AJ, Brady AF, Jeelani NO, Lynch SA, Mulliken JB, Murray DJ, Phipps JM, Sweeney E, Tomkins SE, Wilson LC, Bennett S, Cornall RJ, Broxholme J, Kanapin A, 500 Whole-Genome Sequences (WGS500) Consortium, Johnson D, Wall SA, van der Spek PJ, Mathijssen IM, Maxson RE, Twigg SR, Wilkie AO: Mutations in TCF12, encoding a basic helix-loop-helix partner of TWIST1, are a frequent cause of coronal craniosynostosis. Nat Genet 2013, 45:304-307.
58. Thompson BA, Greenblatt MS, Vallee MP, Herkert JC, Tessereau C, Young EL, Adzhubey IA, Li B, Bell R, Feng B, Mooney SD, Radivojac P, Sunyaev SR, Frebourg T, Hofstra RM, Sijmons RH, Boucher K, Thomas A, Goldgar DE, Spurdle AB, Tavtigian SV: Calibration of multiple in silico tools for predicting pathogenicity of mismatch repair gene missense substitutions. Hum Mutat 2013, 34:255-265.
59. Whiley PJ, Pettigrew CA, Brewster BL, Walker LC, kConFab Investigators, Spurdle AB, Brown MA: Effect of BRCA2 sequence variants predicted to disrupt exonic splice enhancers on BRCA2 transcripts. BMC Med Genet 2010, 11:80.
60. Matthews BW: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975, 405:442-451.
61. Wang K, Li M, Hakonarson H: ANNOVAR: Functional annotation of genetic variants from next-generation sequencing data. Nucleic Acids Res 2010, 38:e164.
62. Vihinen M: How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genomics 2012, 13:S2.
63. Forbes SA, Tang G, Bindal N, Bamford S, Dawson E, Cole C, Kok CY, Jia M, Ewing R, Menzies A, Teague JW, Stratton MR, Futreal PA: COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res 2010, 38:D652-D657.
64. COSMIC. [http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/]
65. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW: Cancer genome landscapes. Science 2013, 339:1546-1558.
66. MutPred Splice. [http://mutdb.org/mutpredsplice]
67. MutPred Splice download. [http://mutdb.org/mutpredsplice/download.htm]
68. MutPred Splice GitHub repository. [https://github.com/wmgmm/mutpredsplice]
69. Care MA, Needham CJ, Bulpitt AJ, Westhead DR: Deleterious SNP prediction: be mindful of your training data! Bioinformatics 2007, 23:664-672.
70. Teraoka SN, Telatar M, Becker-Catania S, Liang T, Oneng-t S, Tolun A, Chessa L, Sanal O, Bernatowska E, Gatti RA, Concannon P: Splicing defects in the ataxia-telangiectasia gene, ATM: underlying mutations and consequences. Am J Hum Genet 1999, 64:1617-1631.
71. Srebrow A, Kornblihtt AR: The connection between splicing and cancer. J Cell Sci 2006, 119:2635-2641.
72. Venables JP: Aberrant and alternative splicing in cancer. Cancer Res 2004, 64:7647-7654.
73. Telatar M, Teraoka S, Wang Z, Chun HH, Liang T, Castellvi-Bel S, Udar N, Borresen-Dale AL, Chessa L, Bernatowska-Matuszkiewicz E, Porras O, Watanabe M, Junker A, Concannon P, Gatti RA: Ataxia-telangiectasia: identification and detection of founder-effect mutations in the ATM gene in ethnic populations. Am J Hum Genet 1998, 62:86-97.
74. Bogdanova N, Cybulski C, Bermisheva M, Datsyuk I, Yamini P, Hillemanns P, Antonenkova NN, Khusnutdinova E, Lubinski J, D-rk T: A nonsense mutation (E1978X) in the ATM gene is associated with breast cancer. Breast Cancer Res Treat 2009, 118:207-211.
75. Ge K, DuHadaway J, Du W, Herlyn M, Rodeck U, Prendergast GC: Mechanism for elimination of a tumor suppressor: aberrant splicing of a brain-specific exon causes loss of function of Bin1 in melanoma. Proc Natl Acad Sci USA 1999, 96:9689-9694.
76. Pereira NL, Lin D, Pelleymounter L, Moon I, Stilling G, Eckloff BW, Wieben ED, Redfield MM, Burnett JC Jr, Yee VC, Weinshilboum RM: Natriuretic peptide receptor-3 gene (NPR3): nonsynonymous polymorphism results in significant reduction in protein expression because of accelerated degradation. Circ Cardiovasc Genet 2013, 6:201-210.
77. Albrechtsen A, Grarup N, Li Y, Sparso T, Tian G, Cao H, Jiang T, Kim SY, Korneliussen T, Li Q, Nie C, Wu R, Skotte L, Morris AP, Ladenvall C, Cauchi S, Stančáková A, Andersen G, Astrup A, Banasik K, Bennett AJ, Bolund L, Charpentier G, Chen Y, Dekker JM, Doney AS, Dorkhan M, Forsen T, Frayling TM, Groves CJ, et al: Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 2013, 56:298-310.
78. Flanigan KM, Dunn DM, von Niederhausern A, Soltanzadeh P, Gappmaier E, Howard MT, Sampson JB, Mendell JR, Wall C, King WM, Pestronk A, Florence JM, Connolly AM, Mathews KD, Stephan CM, Laubenthal KS, Wong BL, Morehart PJ, Meyer A, Finkel RS, Bonnemann CG, Medne L, Day JW, Dalton JC, Margolis MK, Hinton VJ, United Dystrophinopathy Project Consortium, Weiss RB: Mutational spectrum of DMD mutations in dystrophinopathy patients: application of modern diagnostic techniques to a large cohort. Hum Mutat 2009, 30:1657-1666.