HMMER: Profile hidden Markov models (profile HMMs) can be used to do sensitive database searching using statistical descriptions of a sequence family's consensus. HMMER is a freely distributable implementation of profile HMM software for protein sequence analysis.
The latest version is: HMMER 2.3.2 (2003-10-03).
Note: HMMER programs are computational expensive, but can run in parallel.
Hidden Markov models
Hidden Markov models are probabilistic models that can assign likelihoods to all possible combinations of gaps, matches, and mismatches to determine the most likely multiple sequence alignment (MSA) or set of possible MSAs. HMMs can produce a single highest-scoring output but can also generate a family of possible alignments that can then be evaluated for biological significance. Because HMMs are probabilistic, they do not produce the same solution every time they are run on the same dataset; thus they cannot be guaranteed to converge to an optimal alignment. HMMs can produce both global and local alignments. Although HMM-based methods have been developed relatively recently, they offer significant improvements in computational speed, especially for sequences that contain overlapping regions.
- POA: Partial Order Alignment — a fast (a very simple) program for multiple sequence alignment in bioinformatics. Its advantages are speed, scalability, sensitivity, and the superior ability to handle branching / indels in the alignment.
- SAM: Sequence Alignment and Modeling System
- Durbin R, Eddy S, Krogh A, Mitchison G (1998). "Biological sequence analysis: probabilistic models of proteins and nucleic acids". Cambridge University Press. ISBN 0-5216-2971-3.
- Grasso C, Lee C (2004). "Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems". Bioinformatics 20(10):1546-1556.