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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.