MYOP: um arcabouço para predição de genes ab initio" / MYOP: A framework for building ab initio gene predictors

AUTOR(ES)

Andre Yoshiaki Kashiwabara

DATA DE PUBLICAÇÃO

2007

RESUMO

The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) is applied. One problem with these implementations is that they maintain fixed GHMM architectures that are hard-coded. Due to this problem and similarities between the programs, we have implemented the MYOP framework (Make Your Own Predictor) with the objective of providing a flexible environment that allows the rapid evaluation of each gene model. We have demonstrated the utility of this tool through the implementation and evaluation of 96 gene models in which each model has a set of states and each state has a duration distribution and a probabilistic model. We have shown that a sophisticated probabilisticmodel is not sufficient to obtain better predictor, showing the experimentation relevance and the importance of a system as MYOP.

ASSUNTO(S)

bioinformatics predição de genes cadeia de markov oculta generalizada. gene prediction generalized hidden markov model bioinformática

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