Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

Hidden Markov model speed heuristic and iterative HMM search procedure

L Steven Johnson1*, Sean R Eddy2 and Elon Portugaly3

Author Affiliations

1 Department of Immunology and Pathology, Washington University School of Medicine, St. Louis, Missouri, USA

2 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA

3 School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

For all author emails, please log on.

BMC Bioinformatics 2010, 11:431  doi:10.1186/1471-2105-11-431

Published: 18 August 2010

Abstract

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.