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.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Lam TY,Meyer IMdoi
10.1093/nar/gkp662subject
Has Abstractpub_date
2009-11-01 00:00:00pages
e139issue
21eissn
0305-1048issn
1362-4962pii
gkp662journal_volume
37pub_type
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