Profile hidden Markov models (profile HMMs) can be used to do sensitive database searching using statistical descriptions of a sequence family's consensus. HMMER uses profile HMMs, and can be useful in situations like:
- if you are working with an evolutionarily diverse protein family, a BLAST search with any individual sequence may not find the rest of the sequences in the family.
- the top hits in a BLAST search are hypothetical sequences from genome projects.
- your protein consists of several domains which are of different types.
HMMER (pronounced 'hammer', as in a more precise mining tool than BLAST) was developed by Sean Eddy at Washington University in St. Louis.
HMMER is a very cpu-intensive program and is parallelized using threads, so that each instance of hmmsearch or the other search programs can use all the cpus available on a node. HMMER on OSC clusters are intended for those who need to run HMMER searches on large numbers of query sequences.