Advertisement

In Silico Prediction of Deleteriousness for Nonsynonymous and Splice-Altering Single Nucleotide Variants in the Human Genome

  • Xueqiu Jian
  • Xiaoming Liu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1498)

Abstract

In silico prediction methods have increasingly been valuable and popular in molecular biology, especially in human genetics, for deleteriousness prediction to filter and prioritize huge amounts of DNA variation identified by sequencing human genomes. There is a rich collection of available methods developed upon different levels/aspects of knowledge about how DNA variations affect gene expression. Given the fact that their predictions are not always consistent or even opposite of what was expected, using consensus prediction or majority vote among these methods is preferred to trusting any single one. Because querying different databases for different methods is both tedious and time-consuming for such big data sets, one database integrating predictions from multiple databases can facilitate the process. In this chapter, we describe the general steps of obtaining comprehensive predictions and annotations for large numbers of variants from dbNSFP, the first and probably the most widely used database of its kind.

Key words

dbNSFP dbscSNV Single nucleotide variant Nonsynonymous Splice site In silico Functional prediction Database Protocol 

References

  1. 1.
    Liu X, Jian X, Boerwinkle E (2011) dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum Mutat 32(8):894–899CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Liu X, Jian X, Boerwinkle E (2013) dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat 34(9):E2393–E2402CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Liu X, Wu C, Li C, Boerwinkle E (2015) dbNSFP v3.0: a one-stop database of functional predictions and annotations for human non-synonymous and splice site SNVs. Hum Mutat 37(3):235–241. doi: 10.1002/humu.22932 CrossRefGoogle Scholar
  4. 4.
    Jian X, Boerwinkle E, Liu X (2014) In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Res 42:13534–13544CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11:863–874CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Chun S, Fay JC (2009) Identification of deleterious mutations within three human genomes. Genome Res 19:1553–1561CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11:361–362CrossRefPubMedGoogle Scholar
  9. 9.
    Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39(17):e118CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, Day IN, Gaunt TR (2013) Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 34:57–65CrossRefPubMedGoogle Scholar
  11. 11.
    Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7(10):e46688CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Carter H, Douville C, Stenson PD, Cooper DN, Karchin R (2013) Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14(Suppl 3):3CrossRefGoogle Scholar
  13. 13.
    Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Quang D, Chen Y, Xie X (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31:761–763CrossRefPubMedGoogle Scholar
  15. 15.
    Shihab HA, Rogers MF, Gough J, Mort M, Cooper DN, Day IN, Gaunt TR, Campbell C (2015) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31:1536–1543CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, Liu X (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Gene 24:2125–2137CrossRefGoogle Scholar
  17. 17.
    Gulko B, Hubisz MJ, Gronau I, Siepel A (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat Genet 47:276–283CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol 6(12):e1001025CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A (2010) Detection of non-neutral substitution rates on mammalian phylogenies. Genome Res 20:110–121CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    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 (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15:1034–1050CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Garber M, Guttman M, Clamp M, Zody MC, Friedman N, Xie X (2009) Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics 25(12):i54–i62CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Center for Human Genetics, Institute of Molecular MedicineThe University of Texas Health Science Center at HoustonHoustonUSA
  2. 2.Human Genetics Center, School of Public HealthThe University of Texas Health Science Center at HoustonHoustonUSA

Personalised recommendations