Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space
Author(s) -
Swati Kaushik,
Anu G. Nair,
Eshita Mutt,
Hari Prasanna Subramanian,
Ramanathan Sowdhamini
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv538
Subject(s) - hidden markov model , computer science , annotation , false positive paradox , sequence alignment , data mining , homology (biology) , java , protein sequencing , artificial intelligence , cluster analysis , computational biology , biology , peptide sequence , genetics , programming language , gene
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 available methods.
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