Hidden Markov model speed heuristic and iterative HMM search procedure

Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEA...

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Veröffentlicht in:BMC bioinformatics Jg. 11; H. 1; S. 431
Hauptverfasser: Johnson, L Steven, Eddy, Sean R, Portugaly, Elon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 18.08.2010
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Artificial Intelligence
Base Sequence
Bioinformatics
Biomedical and Life Sciences
Comparative genomics
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer science
Databases, Protein
Genetic algorithms
Hidden Markov models
Iterative methods
Life Sciences
Markov Chains
Methods
Microarrays
Proteins
Proteins - chemistry
Research Article
Sequence Alignment - methods
Software
Studies
United States
Test Database
Search Time
Profile Hide Markov Model
Entropy Weighting
Iterative Search
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-11-431