SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features

被引：180

作者：

Yates, Christopher M. ^{[1
]}

Filippis, Ioannis ^{[1
]}

Kelley, Lawrence A. ^{[1
]}

Sternberg, Michael J. E. ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Ctr Integrat Syst Biol & Bioinformat, London SW7 2AZ, England

来源：

JOURNAL OF MOLECULAR BIOLOGY | 2014年 / 426卷 / 14期

基金：

英国生物技术与生命科学研究理事会; 英国医学研究理事会;

关键词：

PROTEIN-INTERACTION SITES; DATABASE; POLYMORPHISMS; MUTATIONS; GENERATION; STABILITY; RESIDUES; CANCER; INDEX; SCORE;

D O I：

10.1016/j.jmb.2014.04.026

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Per! and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at vvww.sbg.bio.ic.ac.uk/suspect/download.html. (C) 2014 Published by Elsevier Ltd.

引用

页码：2692 / 2701

页数：10

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