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Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space
KTH, School of Computer Science and Communication (CSC). Tata Institute of Fundamental Research, India.ORCID iD: 0000-0002-1952-9583
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2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 3, 338-344 p.Article in journal (Refereed) PublishedText
Abstract [en]

Motivation: In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the availablemethods. Results: We have developed a method to identify remote homologues more effectively from any protein sequence database by using several cascading events with Hidden Markov Models (C-HMM). We have implemented clustering of hits and profile generation of hit clusters to effectively reduce the computational timings of the cascaded sequence searches. Our C-HMM approach could cover 94, 83 and 40% coverage at family, superfamily and fold levels, respectively, when applied on diverse protein folds. We have compared C-HMM with various remote homology detection methods and discuss the trade-offs between coverage and false positives.

Place, publisher, year, edition, pages
Oxford University Press, 2016. Vol. 32, no 3, 338-344 p.
National Category
Computer Science Biochemistry and Molecular Biology
URN: urn:nbn:se:kth:diva-183321DOI: 10.1093/bioinformatics/btv538ISI: 000370203000004PubMedID: 26454276ScopusID: 2-s2.0-84962263993OAI: diva2:910533

QC 20160309

Available from: 2016-03-09 Created: 2016-03-07 Last updated: 2016-03-19Bibliographically approved

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Nair, Anu G.
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