Abstract
BackgroundNumerous 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.ResultsThe 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.ConclusionThis 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0781-z) contains supplementary material, which is available to authorized users.
Highlights
Numerous tools have been developed to predict the fitness effects of genetic variants on corresponding proteins
Loss of function (LoF) mutations cause the gene product to have reduced activity or complete loss of function; gain of function (GoF) mutations change the gene product to have a new and possibly abnormal function; switch of function (SoF) mutations cause the gene product to switch from one set of functions to another set of functions [8], and may involve both loss of the original functions and gain of new functions; conservation of function (CoF) mutations, coined in this study, refer to mutations that are neutral and do not alter gene functions
The discovery of large serial gain of function mutations in thyroid stimulating hormone receptor (TSHR) is of great interest, revealing a new disease mechanism of mutations that constantly increase the basal activity of a receptor [21]
Summary
Numerous tools have been developed to predict the fitness effects (i.e., neutral, deleterious, or beneficial) of genetic variants on corresponding proteins. Prediction in terms of whether a variant causes the variant bearing protein to lose the original function or gain new function is 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. To help narrow down target variants that may have phenotypic and/or pathological effect, various computational tools (e.g., [1,2,3,4,5,6]) have been introduced to predict the effect of genetic variants These tools provide either a quantitative score indicating the degree of deleteriousness.
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