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Open Access Highly Accessed Methodology article

HMM Logos for visualization of protein families

Benjamin Schuster-Böckler12, Jörg Schultz3 and Sven Rahmann14*

Author Affiliations

1 Department of Mathematics and Computer Science Freie Universität Berlin, Germany

2 Present address: Prundsbergstr. 23a, D-82064 Strasslach, Germany

3 Department of Bioinformatics, Biozentrum, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany

4 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany

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BMC Bioinformatics 2004, 5:7  doi:10.1186/1471-2105-5-7

Published: 21 January 2004

Abstract

Background

Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way.

Results

We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the state's expected contribution).

A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics http://logos.molgen.mpg.de webcite.

Conclusions

We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras.

Keywords:
Hidden Markov Model; Sequence Logo; HMM Logo; profile; information content; hitting probability; dynamic programming; small GTPases