Overview
ToPS is an objected-oriented framework implemented using C++ that facilitates the integration of probabilistic models for sequences over a user defined alphabet. ToPS contains the implementation of eight type of models to analyze discrete sequences:
- Independent and identically distributed model
- Variable-Length Markov Chain (VLMC)
- Inhomogeneous Markov Chain
- Hidden Markov Model
- Pair Hidden Markov Model
- Profile Hidden Markov Model
- Similarity Based Sequence Weighting
- Generalized Hidden Markov Model (GHMM)
The user can implement models either by manual description of the probability values in a configuration file, or by using training algorithms provided by the system. The ToPS framework also includes a set of programs that implement bayesian classifiers, sequence samplers, and sequence decoders. Finally, ToPS is an extensible and portable system that facilitates the implementation of other probabilistic models, and the development of new programs.
Publication
- Kashiwabara AY, Bonadio, Onuchic V, Amado F, Mathias R, and Durham, AM (2013) ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data. PLoS Comput Biol 9(10): e1003234. doi:10.1371/journal.pcbi.1003234