Hidden Markov model speed heuristic and iterative HMM search procedure

BackgroundProfile 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.ResultsWe 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.ConclusionsOur 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.

[1]  C. Chothia,et al.  Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  BMC Bioinformatics , 2005 .

[3]  M. Madera,et al.  A comparison of profile hidden Markov model procedures for remote homology detection. , 2002, Nucleic acids research.

[4]  Steven E. Brenner,et al.  Statistical evaluation of pairwise protein sequence comparison with the Bayesian bootstrap , 2005, Bioinform..

[5]  Philip E. Bourne,et al.  Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction , 2006, BMC Bioinformatics.

[6]  W. Pearson.  Comparison of methods for searching protein sequence databases , 1995, Protein science : a publication of the Protein Society.

[7]  Tim J. P. Hubbard,et al.  SCOP: a structural classification of proteins database , 1998, Nucleic Acids Res..

[8]  Patrice Koehl,et al.  The ASTRAL compendium for protein structure and sequence analysis , 2000, Nucleic Acids Res..

[9]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[10]  Chris Sander,et al.  Removing near-neighbour redundancy from large protein sequence collections , 1998, Bioinform..

[11]  Richard Hughey,et al.  Hidden Markov models for detecting remote protein homologies , 1998, Bioinform..

[12]  Patrice Koehl,et al.  ASTRAL compendium enhancements , 2002, Nucleic Acids Res..

[13]  William Noble Grundy,et al.  Homology Detection via Family Pairwise Search , 1998, J. Comput. Biol..

[14]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[15]  Thomas L. Madden,et al.  Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. , 2001, Nucleic acids research.