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Article: Predicting regulatory variants with composite statistic

TitlePredicting regulatory variants with composite statistic
Authors
Issue Date2016
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2016, v. 32 n. 18, p. 2729-2736 How to Cite?
AbstractMotivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS.
Persistent Identifierhttp://hdl.handle.net/10722/234845
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643

 

DC FieldValueLanguage
dc.contributor.authorLi, J-
dc.contributor.authorPan, Z-
dc.contributor.authorLiu, Z-
dc.contributor.authorWu, J-
dc.contributor.authorWang, P-
dc.contributor.authorZhu, Y-
dc.contributor.authorXu, F-
dc.contributor.authorXia, Z-
dc.contributor.authorSham, PC-
dc.contributor.authorKocher, Jean-Pierr-
dc.contributor.authorLi, M-
dc.contributor.authorLiu, JS-
dc.contributor.authorWang, LJ-
dc.date.accessioned2016-10-14T13:49:38Z-
dc.date.available2016-10-14T13:49:38Z-
dc.date.issued2016-
dc.identifier.citationBioinformatics, 2016, v. 32 n. 18, p. 2729-2736-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/234845-
dc.description.abstractMotivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformatics-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here]. -
dc.titlePredicting regulatory variants with composite statistic-
dc.typeArticle-
dc.identifier.emailLi, J: mulin@hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.emailLi, M: mxli@hku.hk-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.authorityLi, M=rp01722-
dc.identifier.doi10.1093/bioinformatics/btw288-
dc.identifier.pmid27273672-
dc.identifier.hkuros270350-
dc.identifier.volume32-
dc.identifier.issue18-
dc.identifier.spage2729-
dc.identifier.epage2736-
dc.publisher.placeUnited Kingdom-

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