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Bioinformatics Advance Access originally published online on April 25, 2008
Bioinformatics 2008 24(11):1399-1400; doi:10.1093/bioinformatics/btn201
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MAMOT: hidden Markov modeling tool

Frédéric Schütz and Mauro Delorenzi *

Swiss Institute of Bioinformatics and Bioinformatics Core Facility of the NCCR Molecular Oncology, CH–1015 Lausanne, Switzerland

*To whom correspondence should be addressed.


  Abstract

Summary: Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research.

One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding.

Availability: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Contact: Mauro.Delorenzi{at}isb-sib.ch

Associate Editor: Limsoon Wong

Received on February 22, 2008; revised on April 21, 2008; accepted on April 21, 2008

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