Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models

Abstract

The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.

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References 47

1. 1.

   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.

   Not in our library10.1038/nmeth0410-248

2. 2.

   Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403-410.

   Not in our library10.1016/s0022-2836(05)80360-2

3. 3.

   Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389-3402.

   Not in our library10.1093/nar/25.17.3389

4. 4.

   Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, et al. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 2004;32:D115-119.

   Not in our library10.1093/nar/gkh131

5. 5.

   Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-29.

   Not in our library10.1038/75556

6. 6.

   Bao L, Zhou M, Cui Y. nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Res. 2005;33:W480-482.

   Not in our library10.1093/nar/gki372

7. 7.

   Bateman A, Haft DH. HMM-based databases in InterPro. Brief Bioinformatics. 2002;3:236-245.

   Not in our library10.1093/bib/3.3.236

8. 8.

   Bromberg Y, Rost B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007;35:3823-3835.

   Not in our library10.1093/nar/gkm238

9. 9.

   Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat. 2009;30:1237-1244.

   Not in our library10.1002/humu.21047

10. 10.

    Capriotti E, Altman RB. A new disease-specific machine learning approach for the prediction of cancer-causing missense variants. Genomics. 2011;98:310-317.

    Not in our library10.1016/j.ygeno.2011.06.010

11. 11.

    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;22:2729-2734.

    Not in our library10.1093/bioinformatics/btl423

12. 12.

    Eddy SR. Hidden Markov models. Curr Opin Struct Biol. 1996;6:361-365.

    Not in our library10.1016/s0959-440x(96)80056-x

13. 13.

    Eddy SR. A new generation of homology search tools based on probabilistic inference. Genome Inform. 2009;23:205-211.

    Not in our library

14. 14.

    Fahmy AS, Ali AA, Mohamed SA. Characterization of a cysteine protease from wheat Triticum aestivum (cv. Giza 164). Bioresour Technol. 2004;91:297-304.

    Not in our library10.1016/s0960-8524(03)00193-7

15. 15.

    Ferrer-Costa C, Orozco M, Cruz X dela. Sequence-based prediction of pathological mutations. Proteins. 2004;57:811-819.

    Not in our library10.1002/prot.20252

16. 16.

    Ferrer-Costa C, Orozco M, Cruz X dela. Use of bioinformatics tools for the annotation of disease-associated mutations in animal models. Proteins. 2005;61:878-887.

    Not in our library10.1002/prot.20664

17. 17.

    Gough J, Karplus K, Hughey R, Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol. 2001;313:903-919.

    Not in our library10.1006/jmbi.2001.5080

18. 18.

    Hicks S, Wheeler DA, Plon SE, Kimmel M. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum Mutat. 2011;32:661-668.

    Not in our library10.1002/humu.21490

19. 19.

    Ilic K, Kellogg EA, Jaiswal P, Zapata F, Stevens PF, Vincent LP, Avraham S, Reiser L, Pujar A, Sachs MM, Whitman NT, McCouch SR, et al. The plant structure ontology, a unified vocabulary of anatomy and morphology of a flowering plant. Plant Physiol. 2007;143:587-599.

    Not in our library10.1104/pp.106.092825

20. 20.

    Krogh A, Brown M, Mian IS, Sjölander K, Haussler D. Hidden Markov models in computational biology. Applications to protein modeling. J Mol Biol. 1994;235:1501-1531.

    Not in our library10.1006/jmbi.1994.1104

21. 21.

    Kullback S, Leibler RA. On Information and Sufficiency. The Annals of Mathematical Statistics. 1951;22:79-86.

    Not in our library

22. 22.

    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.

    Not in our library10.1093/bioinformatics/btp528

23. 23.

    Lima Morais DAde, Fang H, Rackham OJL, Wilson D, Pethica R, Chothia C, Gough J. SUPERFAMILY 1.75 including a domain-centric gene ontology method. Nucleic Acids Res. 2011;39:D427-434.

    Not in our library10.1093/nar/gkq1130

24. 24.
    dbNSFP: A lightweight database of human nonsynonymous SNPs and their functional predictions · 2011

    No notice
25. 25.

    Madera M, Gough J. A comparison of profile hidden Markov model procedures for remote homology detection. Nucleic Acids Res. 2002;30:4321-4328.

    Not in our library10.1093/nar/gkf544

26. 26.

    Mort M, Evani US, Krishnan VG, Kamati KK, Baenziger PH, Bagchi A, Peters BJ, Sathyesh R, Li B, Sun Y, Xue B, Shah NH, et al. In silico functional profiling of human disease-associated and polymorphic amino acid substitutions. Hum Mutat. 2010;31:335-346.

    Not in our library10.1002/humu.21192

27. 27.

    Mottaz A, David FPA, Veuthey A-L, Yip YL. Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics. 2010;26:851-852.

    Not in our library10.1093/bioinformatics/btq028

28. 28.

    Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res. 2001;11:863-874.

    Not in our library10.1101/gr.176601

29. 29.

    Ng PC, Henikoff S. Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet. 2006;7:61-80.

    Not in our library10.1146/annurev.genom.7.080505.115630

30. 30.

    Ogiso E, Takahashi Y, Sasaki T, Yano M, Izawa T. The role of casein kinase II in flowering time regulation has diversified during evolution. Plant Physiol. 2010;152:808-820.

    Not in our library10.1104/pp.109.148908

31. 31.

    Olatubosun A, Väliaho J, Härkönen J, Thusberg J, Vihinen M. PON-P: Integrated predictor for pathogenicity of missense variants. Hum Mutat. 2012;33:1166-1174.

    Not in our library10.1002/humu.22102

32. 32.

    Peterson TA, Adadey A, Santana-Cruz I, Sun Y, Winder A, Kann MG. DMDM: domain mapping of disease mutations. Bioinformatics. 2010;26:2458-2459.

    Not in our library10.1093/bioinformatics/btq447

33. 33.

    Pujar A, Jaiswal P, Kellogg EA, Ilic K, Vincent L, Avraham S, Stevens P, Zapata F, Reiser L, Rhee SY, Sachs MM, Schaeffer M, et al. Whole-plant growth stage ontology for angiosperms and its application in plant biology. Plant Physiol. 2006;142:414-428.

    Not in our library10.1104/pp.106.085720

34. 34.

    Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, et al. The Pfam protein families database. Nucleic Acids Res. 2012;40:D290-301.

    Not in our library10.1093/nar/gkr1065

35. 35.

    Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 2002;30:3894-3900.

    Not in our library10.1093/nar/gkf493

36. 36.

    Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39:e118.

    Not in our library10.1093/nar/gkr407

37. 37.

    Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008;83:610-615.

    Not in our library10.1016/j.ajhg.2008.09.017

38. 38.

    Sjölander K, Karplus K, Brown M, Hughey R, Krogh A, Mian IS, Haussler D. Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Comput Appl Biosci. 1996;12:327-345.

    Not in our library10.1093/bioinformatics/12.4.327

39. 39.

    Smith CL, Eppig JT. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med. 2009;1:390-399.

    Not in our library10.1002/wsbm.44

40. 40.

    Sonnhammer EL, Eddy SR, Durbin R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 1997;28:405-420.

    Not in our library10.1002/(sici)1097-0134(199707)28:3<405::aid-prot10>3.0.co;2-l

41. 41.
    The Human Gene Mutation Database: 2008 update · 2009

    No notice
42. 42.

    Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics. 2007;23:1282-1288.

    Not in our library10.1093/bioinformatics/btm098

43. 43.

    Tavtigian SV, Deffenbaugh AM, Yin L, Judkins T, Scholl T, Samollow PB, Silva Dde, Zharkikh A, Thomas A. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet. 2006;43:295-305.

    Not in our library10.1136/jmg.2005.033878

44. 44.

    A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061-1073.

    Not in our library10.1038/nature09534

45. 45.

    Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13:2129-2141.

    Not in our library10.1101/gr.772403

46. 46.

    Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011;32:358-368.

    Not in our library10.1002/humu.21445

47. 47.

    Thusberg J, Vihinen M. Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum Mutat. 2009;30:703-714.

    Not in our library10.1002/humu.20938

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