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EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome
BACKGROUND: Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061446/ https://www.ncbi.nlm.nih.gov/pubmed/24916671 http://dx.doi.org/10.1186/1471-2164-15-455 |
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author | Zeng, Shuai Yang, Jing Chung, Brian Hon-Yin Lau, Yu Lung Yang, Wanling |
author_facet | Zeng, Shuai Yang, Jing Chung, Brian Hon-Yin Lau, Yu Lung Yang, Wanling |
author_sort | Zeng, Shuai |
collection | PubMed |
description | BACKGROUND: Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data. RESULTS: Here we report a novel algorithm that features two innovative changes: 1. making better use of sequence conservation information by grouping the homologous protein sequences into six blocks according to evolutionary distances to human and evaluating sequence conservation in each block independently, and 2. including as many such homologous sequences as possible in analyses. Random forests are used to evaluate sequence conservation in each block and to predict potential impact of an AAS on protein function. Testing of this algorithm on a comprehensive dataset showed significant improvement on prediction accuracy upon currently widely-used programs. The algorithm and a web-based application tool implementing it, EFIN (Evaluation of Functional Impact of Nonsynonymous SNPs) were made freely available (http://paed.hku.hk/efin/) to the public. CONCLUSIONS: Grouping homologous sequences into different blocks according to the evolutionary distance of the species to human and evaluating sequence conservation in each group independently significantly improved prediction accuracy. This approach may help us better understand the roles of genetic variants in human disease and health. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-455) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4061446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40614462014-06-19 EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome Zeng, Shuai Yang, Jing Chung, Brian Hon-Yin Lau, Yu Lung Yang, Wanling BMC Genomics Methodology Article BACKGROUND: Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data. RESULTS: Here we report a novel algorithm that features two innovative changes: 1. making better use of sequence conservation information by grouping the homologous protein sequences into six blocks according to evolutionary distances to human and evaluating sequence conservation in each block independently, and 2. including as many such homologous sequences as possible in analyses. Random forests are used to evaluate sequence conservation in each block and to predict potential impact of an AAS on protein function. Testing of this algorithm on a comprehensive dataset showed significant improvement on prediction accuracy upon currently widely-used programs. The algorithm and a web-based application tool implementing it, EFIN (Evaluation of Functional Impact of Nonsynonymous SNPs) were made freely available (http://paed.hku.hk/efin/) to the public. CONCLUSIONS: Grouping homologous sequences into different blocks according to the evolutionary distance of the species to human and evaluating sequence conservation in each group independently significantly improved prediction accuracy. This approach may help us better understand the roles of genetic variants in human disease and health. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-455) contains supplementary material, which is available to authorized users. BioMed Central 2014-06-10 /pmc/articles/PMC4061446/ /pubmed/24916671 http://dx.doi.org/10.1186/1471-2164-15-455 Text en © Zeng et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Zeng, Shuai Yang, Jing Chung, Brian Hon-Yin Lau, Yu Lung Yang, Wanling EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title | EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title_full | EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title_fullStr | EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title_full_unstemmed | EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title_short | EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
title_sort | efin: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061446/ https://www.ncbi.nlm.nih.gov/pubmed/24916671 http://dx.doi.org/10.1186/1471-2164-15-455 |
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