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

BMC Bioinformatics

Research Article

Mingming Liu¹  Liqing Zhang¹  Layne T. Watson² 

[1] Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA;Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA;Department of Mathematics, Virginia Polytechnic Institute & State University, Blacksburg, USA;Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, USA;

关键词: Genetic variants;    Functional outcome;    Hidden Markov model;   

DOI  :  10.1186/s12859-015-0781-z

 received in 2015-05-08, accepted in 2015-10-16,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Liu et al. 2015