Abstract
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.
Highlights
Sequence database homology searching is one of the most important applications in computational molecular biology
The panel shows results for 76 query profiles, chosen to sample the full range of query lengths in the Pfam protein domain database from 7 to 2,217 residues. These results show that HMMER3 performance is comparable to other fast database search programs; somewhat slower than NCBI BLAST, and somewhat faster than WUBLAST, for example
In describing the multiple segment Viterbi’’ (MSV) heuristic and other acceleration methods implemented in HMMER3, I have not addressed the question of whether the MSV heuristic is better or worse than other heuristics, such as those in BLAST or FASTA
Summary
Sequence database homology searching is one of the most important applications in computational molecular biology. The most widely used tool for sequence comparison and database search is BLAST [1,2,3]. Since BLAST’s introduction, some important advances have been made in the theory of sequence comparison, by using probabilistic inference methods based on profile hidden Markov models (profile HMMs) [4]. Though, the BLAST implementation computes optimal local alignment scores using ad hoc gap penalties. This implementation core may not be readily adaptable to a probabilistic insertion/deletion model and the more powerful ‘‘Forward/Backward’’ HMM algorithm that computes not just one best-scoring alignment, but a sum of probabilities over the entire local alignment ensemble. The Forward algorithm allows a more powerful and formal log-likelihood score statistic to be assigned to each target sequence, and Forward/Backward allows confidence values to be assigned to each aligned residue
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