Cited by 6,975Genomics and Phylogenetic StudiesAlgorithms and Data CompressionFractal and DNA sequence analysis

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Accelerated Profile HMM Searches

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Eddy, Sean R.·2011·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1002195PMC PMC3197634

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Abstract

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.

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Authors: Eddy, S. R.
Affiliations: Howard Hughes Medical Institute (US)
Corresponding author: Eddy, Sean R.
ORCID: Eddy, Sean R.
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2011-10-20
Volume: 7
Issue: 10
Pages: e1002195
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1002195
PMC: PMC3197634
OpenAlex: W2138122982
Cited by: 6,975
References: 46
Citations by year: 2026: 791 · 2025: 804 · 2024: 768 · 2023: 693 · 2022: 671 · 2021: 706 · 2020: 496 · 2019: 411 · 2018: 386 · 2017: 312 · 2016: 271 · 2015: 258 · 2014: 206 · 2013: 145 · 2012: 52
FWCI: 52.51
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Howard Hughes Medical Institute
Hebrew University of Jerusalem
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
Keywords: profile hidden Markov models, sequence homology search, protein sequence alignment, computational acceleration, heuristic filtering, forward-backward algorithm
MeSH terms: Algorithms, Markov Chains, Software, Information Storage and Retrieval, Sequence Alignment, Computational Biology, Databases, Protein

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