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Bioinformatics. 2014 Jun 1;30(11):1625-6. doi: 10.1093/bioinformatics/btu057. Epub 2014 Jan 30.

StochHMM: a flexible hidden Markov model tool and C++ library.

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  • 1Genome Center, One Shields Ave., University of California, Davis, CA 95616, USA.

Abstract

Hidden Markov models (HMMs) are probabilistic models that are well-suited to solve many different classification problems in computation biology. StochHMM provides a command-line program and C++ library that can implement a traditional HMM from a simple text file. StochHMM provides researchers the flexibility to create higher-order emissions, integrate additional data sources and/or user-defined functions into multiple points within the HMM framework. Additional features include user-defined alphabets, ability to handle ambiguous characters in an emission-dependent manner, user-defined weighting of state paths and ability to tie transition probabilities to sequence.

AVAILABILITY AND IMPLEMENTATION:

StochHMM is implemented in C++ and is available under the MIT License. Software, source code, documentation and examples can be found at http://github.com/KorfLab/StochHMM.

© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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