title: HMMvar-func: a new method for predicting the functional outcome of genetic variants.
abstract: 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 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.
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. mingml@cs.vt.edu.
authors.forename: Mingming
authors.initials: M
authors.lastname: Liu
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. john.RS.Smith@cambridge.co.uk.|Department of Mathematics, Virginia Polytechnic Institute & State University, Blacksburg, USA. john.RS.Smith@cambridge.co.uk.|Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, USA. john.RS.Smith@cambridge.co.uk.
authors.forename: Layne T
authors.initials: LT
authors.lastname: Watson
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. lqzhang@cs.vt.edu.
authors.forename: Liqing
authors.initials: L
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D020407:Internet; D008390:Markov Chains; D009154:Mutation; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D011989:Receptors, Thyrotropin / Q000235:genetics; D014584:User-Computer Interface
chemical_list: D011989:Receptors, Thyrotropin