Show simple item record

dc.contributor.authorLiu, Mingming
dc.contributor.authorWatson, Layne T
dc.contributor.authorZhang, Liqing
dc.identifier.citationBMC Bioinformatics. 2015 Oct 30;16(1):351en_US
dc.description.abstractAbstract Background 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 . Conclusion 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.
dc.rightsCreative Commons Attribution 4.0 International*
dc.titleHMMvar-func: a new method for predicting the functional outcome of genetic variantsen_US
dc.typeArticle - Refereed
dc.description.versionPeer Reviewed
dc.rights.holderLiu et al.en_US
dc.title.serialBMC Bioinformatics

Files in this item


This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International