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, 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.
Johnson
L Steven
LS
Department of Immunology and Pathology, Washington University School of Medicine, St Louis, Missouri, USA. stevej@pathology.wustl.edu
Eddy
Sean R
SR
Portugaly
Elon
E
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D030562:Databases, Protein; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D012984:Software
D011506:Proteins