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Open Access Highly Accessed Methodology article

Quantitative prediction of the effect of genetic variation using hidden Markov models

Mingming Liu1, Layne T Watson12 and Liqing Zhang1*

Author Affiliations

1 Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

2 Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

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BMC Bioinformatics 2014, 15:5  doi:10.1186/1471-2105-15-5

Published: 9 January 2014

Abstract

Background

With the development of sequencing technologies, more and more sequence variants are available for investigation. Different classes of variants in the human genome have been identified, including single nucleotide substitutions, insertion and deletion, and large structural variations such as duplications and deletions. Insertion and deletion (indel) variants comprise a major proportion of human genetic variation. However, little is known about their effects on humans. The absence of understanding is largely due to the lack of both biological data and computational resources.

Results

This paper presents a new indel functional prediction method HMMvar based on HMM profiles, which capture the conservation information in sequences. The results demonstrate that a scoring strategy based on HMM profiles can achieve good performance in identifying deleterious or neutral variants for different data sets, and can predict the protein functional effects of both single and multiple mutations.

Conclusions

This paper proposed a quantitative prediction method, HMMvar, to predict the effect of genetic variation using hidden Markov models. The HMM based pipeline program implementing the method HMMvar is freely available at https://bioinformatics.cs.vt.edu/zhanglab/hmm webcite.