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Hidden Markov model speed heuristic and iterative HMM search procedure
L Steven Johnson1, Sean R Eddy, Elon Portugaly
1Department of Immunology and Pathology, Washington University School of Medicine, St Louis, Missouri, USA. stevej@pathology.wustl.edu
BMC Bioinformatics
|August 20, 2010
Summary
We developed HMMERHEAD, a filtering method that significantly speeds up protein homology searches using profile hidden Markov models (profile-HMMs). This method enables JackHMMER, an iterative search tool that detects more remote protein homologs efficiently.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Profile hidden Markov models (profile-HMMs) are effective for detecting remote protein homology.
- Current scoring algorithms (Viterbi, Forward) are time-consuming for large database searches.
Purpose of the Study:
- To reduce the computational time required for profile-HMM searches.
- To develop an efficient iterative profile-HMM search method.
Main Methods:
- Implemented HMMERHEAD, a series of database filtering steps, prior to scoring algorithms.
- Developed JackHMMER, an iterative profile-HMM search method utilizing the HMMERHEAD heuristic.
- Eliminated subdatabase creation common in iterative methods.
Main Results:
- HMMERHEAD decreased Forward search time by 20-fold and Viterbi by 6-fold with minimal sensitivity loss.
- JackHMMER detected 14% more remote protein homologs compared to SAM's T2K.
- The HMMERHEAD heuristic significantly reduces scoring time against large sequence databases.
Conclusions:
- HMMERHEAD is an effective search heuristic for accelerating profile-HMM database searches.
- JackHMMER, enabled by HMMERHEAD, outperforms existing iterative methods like SAM's T2K and NCBI's PSI-BLAST in detecting remote protein homologs.
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