title: HMMCONVERTER 1.0: a toolbox for hidden Markov models.
abstract: 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.
authors.affiliation: Centre for High-Throughput Biology, Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, Canada.
authors.forename: Tin Yin
authors.initials: TY
authors.lastname: Lam
authors.forename: Irmtraud M
authors.initials: IM
authors.lastname: Meyer
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D015233:Models, Statistical; D012984:Software