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dc.contributor.authorLiu, Mingmingen_US
dc.contributor.authorWatson, Layne T.en_US
dc.contributor.authorZhang, Liqingen_US
dc.identifier.citationBMC Bioinformatics. 2014 Jan 09;15(1):5en_US
dc.description.abstractBackground 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
dc.rightsCreative Commons Attribution 4.0 International*
dc.titleQuantitative prediction of the effect of genetic variation using hidden Markov modelsen_US
dc.typeArticle - Refereed
dc.description.versionPeer Reviewed
dc.rights.holderMingming Liu et al.; licensee BioMed Central Ltd.en_US
dc.title.serialBMC Bioinformatics

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