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HMMCONVERTER 1.0: a toolbox for hidden Markov models

Lam, Tin Yin, Meyer, Irmtraud M.
Nucleic acids research 2009 v.37 no.21 pp. e139
Markov chain, algorithms, bioinformatics, computer software, data collection, humans, models, nucleic acids, prediction
Hidden Markov models (HMMs) and their variants are widely used in Bioinformatics applications that analyze and compare biological sequences. Designing a novel application requires the insight of a human expert to define the model's architecture. The implementation of prediction algorithms and algorithms to train the model's parameters, however, can be a time-consuming and error-prone task. We here present HMMCONVERTER, a software package for setting up probabilistic HMMs, pair-HMMs as well as generalized HMMs and pair-HMMs. The user defines the model itself and the algorithms to be used via an XML file which is then directly translated into efficient C++ code. The software package provides linear-memory prediction algorithms, such as the Hirschberg algorithm, banding and the integration of prior probabilities and is the first to present computationally efficient linear-memory algorithms for automatic parameter training. Users of HMMCONVERTER can thus set up complex applications with a minimum of effort and also perform parameter training and data analyses for large data sets.