Accelerated Profile HMM Searches
Open accessAbstract
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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Additional functional analyses are provided in the form of KEGG orthologue annotations, calculated using HMMER and the KOfam library on predicted protein sequences.
“Additional functional analyses are also provided in the form of KEGG orthologue (KO) (23) annotations, which are calculated using HMMER (40) and the KOfam library (using a slightly modified form of the profile hidden Markov model (HMM) database of KEGG orthologues (41)) on the predicted protein sequences.”
From MGnify: the microbiome analysis resource in 2020 (2019) · cites this paper as [40]
The availability of predicted structures may help identify meaningful evolutionary relationships.
“Although some advanced sequence-based methods can already assist in this task39–41, the availability of predicted structures may help identify meaningful evolutionary relationships.”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [40]
Weighting true positive, false positive and false negative counts by the reciprocal of their family, superfamily or fold size makes folds, superfamilies and families contribute linearly with their size instead of quadratically.
“All counts (TP, FP and FN) were weighted by the reciprocal of their family, superfamily or fold size. In this way, folds, superfamilies and families contribute linearly with their size instead of quadratically36.”
From Fast and accurate protein structure search with Foldseek (2024) · cites this paper as [36]
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- [29]Johnson S 2006 Remote Protein Homology Detection Using Hidden Markov Models [Ph.D. thesis] (St. Louis) Washington University School of Medicine, Washington University in St. Louis 92
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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
- 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
- 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 › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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