Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research.

We present a method to visualize all of their central aspects graphically, thus generalizing the concept of sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. As for sequence logos, the stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies of the letters. As a new feature, the stack width now 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 expected contribution).

[Genome Research Limited]


New to Logomat-M?

The About Logomat-M explains how to install and run the software and what most parts of the program do.


  • HMM Logo web server "LogoMat-M" was created and maintained by Benjamin Schuster-Böckler
  • The logo server has been superseded by a unified alignment and HMM logo server. http://skylign.org/.

Source code


If you use HMM-Logos in your publication, please cite:

  • HMM Logos for visualization of protein families.

    Schuster-Böckler B, Schultz J and Rahmann S

    BMC bioinformatics 2004;5;7

* quick link - http://q.sanger.ac.uk/dp5f98sc