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Article: Predicting regulatory variants with composite statistic

TitlePredicting regulatory variants with composite statistic
Authors
Issue Date2016
Citation
Bioinformatics, 2016, v. 32, 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

 

DC FieldValueLanguage
dc.contributor.authorLi, J-
dc.date.accessioned2016-10-14T13:49:38Z-
dc.date.available2016-10-14T13:49:38Z-
dc.date.issued2016-
dc.identifier.citationBioinformatics, 2016, v. 32, p. 2729-2736-
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.relation.ispartofBioinformatics-
dc.titlePredicting regulatory variants with composite statistic-
dc.typeArticle-
dc.identifier.emailLi, J: mulin@hku.hk-
dc.identifier.hkuros270350-
dc.identifier.volume32-
dc.identifier.spage2729-
dc.identifier.epage2736-

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