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Profile hidden Markov models

¹Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, St Louis, MO 63110, USA. eddy@genetics.wustl.edu

Bioinformatics (Oxford, England)

|January 27, 1999

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Profile hidden Markov models (profile HMMs) offer a powerful method for identifying homologous sequences in large databases. These methods provide a position-specific scoring system that complements traditional sequence analysis techniques.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Profile hidden Markov models (profile HMMs) are increasingly utilized in bioinformatics.
These models represent a significant advancement over traditional sequence comparison methods.

Purpose of the Study:

To review the recent literature on profile HMM methods and software.
To highlight the utility of profile HMMs in large-scale sequence analysis and protein domain identification.

Main Methods:

Review of existing literature on profile HMMs.
Description of how profile HMMs convert multiple sequence alignments into position-specific scoring systems.
Discussion of available software and profile HMM libraries.

Main Results:

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Profile HMMs are effective for searching databases for remotely homologous sequences.
These methods complement standard pairwise comparison techniques for large-scale analyses.
Profile HMMs demonstrated performance comparable to threading methods in the CASP2 structure prediction exercise.

Conclusions:

Profile HMMs are a valuable tool in modern sequence analysis.
The availability of software and libraries facilitates their widespread application.
Profile HMMs show promise for protein structure prediction tasks.