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ToPS: a framework to manipulate probabilistic models of sequence data.

André Yoshiaki Kashiwabara1, Igor Bonadio, Vitor Onuchic

  • 1Graduate Program in Informatics, Federal University of Technology - Paraná, Cornélio Procópio, Paraná, Brazil.

Plos Computational Biology
|October 8, 2013
PubMed
Summary
This summary is machine-generated.

ToPS is a flexible computational framework for bioinformatics analysis using eight discrete Markovian models. It enables training, simulation, and decoding for applications like gene prediction and protein profiling.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Discrete Markovian models are essential for analyzing sequential data.
  • Applications include gene prediction, CpG island detection, alignment, and protein profiling.
  • Existing frameworks may lack flexibility in model combination and parameter setting.

Purpose of the Study:

  • To present ToPS, a versatile computational framework for bioinformatics.
  • To integrate diverse Markovian models for enhanced sequence analysis.
  • To provide robust tools for model training, simulation, and decoding.

Main Methods:

  • Implementation of eight distinct Markovian models: IID, VLMC, IMC, HMM, PHMM, PHMM, GHMM, and sequence weighting.
  • Inclusion of training, simulation, and decoding functionalities.
  • Integration of Akaike and Bayesian Information Criteria (AIC and BIC) for parameter optimization.

Main Results:

  • ToPS supports stand-alone model usage, Bayesian classification, and complex probabilistic architectures via GHMMs.
  • A novel, efficient decoding method for generalized hidden Markov models (GHMMs) is introduced.
  • The framework demonstrates flexibility in combining various probabilistic models.

Conclusions:

  • ToPS offers a comprehensive and adaptable platform for a wide range of bioinformatics tasks.
  • The framework facilitates advanced sequence analysis through integrated modeling and efficient decoding.
  • ToPS enhances the capabilities of discrete Markovian models in biological sequence analysis.