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Article: EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome

TitleEFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome
Authors
Issue Date25-Jun-2014
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcgenomics/
Citation
BMC genomics, 2014, v. 15, article no. 455 How to Cite?
AbstractBackground 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 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.
Persistent Identifierhttp://hdl.handle.net/10722/198060
ISSN
2015 Impact Factor: 3.867
2015 SCImago Journal Rankings: 2.343
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZeng, Sen_US
dc.contributor.authorYang, Jen_US
dc.contributor.authorChung, BHYen_US
dc.contributor.authorLau, YLen_US
dc.contributor.authorYang, W-
dc.date.accessioned2014-06-25T02:43:48Z-
dc.date.available2014-06-25T02:43:48Z-
dc.date.issued2014-06-25-
dc.identifier.citationBMC genomics, 2014, v. 15, article no. 455en_US
dc.identifier.issn1471-2164-
dc.identifier.urihttp://hdl.handle.net/10722/198060-
dc.description.abstractBackground 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 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.en_US
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcgenomics/-
dc.relation.ispartofBMC genomicsen_US
dc.rightsBMC genomics. Copyright © BioMed Central Ltd-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleEFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genomeen_US
dc.typeArticleen_US
dc.identifier.emailYang, J: jingy09@hku.hken_US
dc.identifier.emailChung, BHY: bhychung@hku.hken_US
dc.identifier.emailYang, W: yangwl@hkucc.hku.hken_US
dc.identifier.authorityChung, BHY=rp00473en_US
dc.identifier.authorityYang, W=rp00524en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2164-15-455-
dc.identifier.pmid24916671-
dc.identifier.pmcidPMC4061446-
dc.identifier.hkuros229168en_US
dc.identifier.hkuros230885-
dc.identifier.isiWOS:000337703600002-

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