HMMvar-func: a new method for predicting the functional outcome of genetic variants

dc.contributor.authorLiu, Mingmingen
dc.contributor.authorWatson, Layne T.en
dc.contributor.authorZhang, Liqingen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2017-08-03T19:59:15Zen
dc.date.available2017-08-03T19:59:15Zen
dc.date.issued2015-10-30en
dc.date.updated2017-08-03T10:58:41Zen
dc.description.abstractBackground Numerous tools have been developed to predict the fitness effects (i.e., neutral, deleterious, or beneficial) of genetic variants on corresponding proteins. However, prediction in terms of whether a variant causes the variant bearing protein to lose the original function or gain new function is also needed for better understanding of how the variant contributes to disease/cancer. To address this problem, the present work introduces and computationally defines four types of functional outcome of a variant: gain, loss, switch, and conservation of function. The deployment of multiple hidden Markov models is proposed to computationally classify mutations by the four functional impact types. Results The functional outcome is predicted for over a hundred thyroid stimulating hormone receptor (TSHR) mutations, as well as cancer related mutations in oncogenes or tumor suppressor genes. The results show that the proposed computational method is effective in fine grained prediction of the functional outcome of a mutation, and can be used to help elucidate the molecular mechanism of disease/cancer causing mutations. The program is freely available at http://bioinformatics.cs.vt.edu/zhanglab/HMMvar/download.php Conclusions This work is the first to computationally define and predict functional impact of mutations, loss, switch, gain, or conservation of function. These fine grained predictions can be especially useful for identifying mutations that cause or are linked to cancer.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBMC Bioinformatics. 2015 Oct 30;16(1):351en
dc.identifier.doihttps://doi.org/10.1186/s12859-015-0781-zen
dc.identifier.urihttp://hdl.handle.net/10919/78634en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderLiu et al.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleHMMvar-func: a new method for predicting the functional outcome of genetic variantsen
dc.title.serialBMC Bioinformaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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