Nhmmer: DNA homology search with profile HMMs

Travis J Wheeler,Sean R Eddy

doi:10.1093/bioinformatics/btt403

Travis J Wheeler, Sean R Eddy

Open Access

https://doi.org/10.1093/bioinformatics/btt403

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Abstract

Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome.Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.Contact: wheelert@janelia.hhmi.org

Highlights

A widely used general purpose tool for DNA/DNA sequence comparison is blastn (Altschul et al, 1990; Camacho et al, 2009), which heuristically approximates the Smith–Waterman algorithm (Smith and Waterman, 1981) for recognizing local regions of similarity between two sequences
HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X
Another area that deserves attention is the development of methods that maximize the power of computational sequence comparison tools to detect remote homologies

Summary

Introduction

A widely used general purpose tool for DNA/DNA sequence comparison is blastn (Altschul et al, 1990; Camacho et al, 2009), which heuristically approximates the Smith–Waterman algorithm (Smith and Waterman, 1981) for recognizing local regions of similarity between two sequences. Profile hidden Markov models (profile HMMs) (Durbin et al, 1998; Krogh et al, 1994) represent an important advance in terms of sensitivity of sequence searches for remote homology.

Results

Conclusion