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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. 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.

Original publication

DOI

10.1186/1471-2105-5-7

Type

Journal article

Journal

BMC bioinformatics

Publication Date

21/01/2004

Volume

5

Addresses

Department of Mathematics and Computer Science, Freie Universität Berlin, Germany. bendaboots@gmx.de

Keywords

Monomeric GTP-Binding Proteins, Markov Chains, Sequence Alignment, Computational Biology, Amino Acid Motifs, Computer Graphics, Databases, Protein