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. CONCLUSION: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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Liu M,Watson LT,Zhang Ldoi
10.1186/s12859-015-0781-zsubject
Has Abstractpub_date
2015-10-30 00:00:00pages
351issn
1471-2105pii
10.1186/s12859-015-0781-zjournal_volume
16pub_type
杂志文章abstract:BACKGROUND:High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Althou...
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更新日期:2014-07-05 00:00:00
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pub_type: 杂志文章
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更新日期:2011-03-08 00:00:00
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