Journal
BIOINFORMATICS
Volume 30, Issue 11, Pages 1625-1626Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu057
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Funding
- National Institutes of Health [R01-HG004348-01]
- UC Davis Genome Center
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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.
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