Unit

UNIT 7.20 Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2

  1. Ivan Adzhubei1,
  2. Daniel M. Jordan1,2,
  3. Shamil R. Sunyaev1

Published Online: 1 JAN 2013

DOI: 10.1002/0471142905.hg0720s76

Current Protocols in Human Genetics

Current Protocols in Human Genetics

How to Cite

Adzhubei, I., Jordan, D. M. and Sunyaev, S. R. 2013. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Current Protocols in Human Genetics. 76:7.20:7.20.1–7.20.41.

Author Information

  1. 1

    Division of Genetics, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts

  2. 2

    Program in Biophysics, Harvard University, Cambridge, Massachusetts

Publication History

  1. Published Online: 1 JAN 2013

Abstract

PolyPhen-2 (Polymorphism Phenotyping v2), available as software and via a Web server, predicts the possible impact of amino acid substitutions on the stability and function of human proteins using structural and comparative evolutionary considerations. It performs functional annotation of single-nucleotide polymorphisms (SNPs), maps coding SNPs to gene transcripts, extracts protein sequence annotations and structural attributes, and builds conservation profiles. It then estimates the probability of the missense mutation being damaging based on a combination of all these properties. PolyPhen-2 features include a high-quality multiple protein sequence alignment pipeline and a prediction method employing machine-learning classification. The software also integrates the UCSC Genome Browser's human genome annotations and MultiZ multiple alignments of vertebrate genomes with the human genome. PolyPhen-2 is capable of analyzing large volumes of data produced by next-generation sequencing projects, thanks to built-in support for high-performance computing environments like Grid Engine and Platform LSF. Curr. Protoc. Hum. Genet. 76:7.20.1-7.20.41. © 2013 by John Wiley & Sons, Inc.

Keywords:

  • human genetic variation;
  • single-nucleotide polymorphism (SNP);
  • mutation effect prediction;
  • computational biology;
  • PolyPhen-2