Detection of dispersed short tandem repeats using reversible jump Markov chain Monte Carlo.

Tandem repeats occur frequently in biological sequences. They are important for studying genome evolution and human disease. A number of methods have been designed to detect a single tandem repeat in a sliding window. In this article, we focus on the case that an unknown number of tandem repeat segments of the same pattern are dispersively distributed in a sequence. We construct a probabilistic generative model for the tandem repeats, where the sequence pattern is represented by a motif matrix. A Bayesian approach is adopted to compute this model. Markov chain Monte Carlo (MCMC) algorithms are used to explore the posterior distribution as an effort to infer both the motif matrix of tandem repeats and the location of repeat segments. Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms are used to address the transdimensional model selection problem raised by the variable number of repeat segments. Experiments on both synthetic data and real data show that this new approach is powerful in detecting dispersed short tandem repeats. As far as we know, it is the first work to adopt RJMCMC algorithms in the detection of tandem repeats.

An MCMC algorithm for detecting short adjacent repeats shared by multiple sequences.

MOTIVATION: Repeats detection problems are traditionally formulated as string matching or signal processing problems. They cannot readily handle gaps between repeat units and are incapable of detecting repeat patterns shared by multiple sequences. This study detects short adjacent repeats with interunit insertions from multiple sequences. For biological sequences, such studies can shed light on molecular structure, biological function and evolution. RESULTS: The task of detecting short adjacent repeats is formulated as a statistical inference problem by using a probabilistic generative model. An Markov chain Monte Carlo algorithm is proposed to infer the parameters in a de novo fashion. Its applications on synthetic and real biological data show that the new method not only has a competitive edge over existing methods, but also can provide a way to study the structure and the evolution of repeat-containing genes. AVAILABILITY: The related C++ source code and datasets are available at http://ihome.cuhk.edu.hk/%7Eb118998/share/BASARD.zip. CONTACT: xfan@sta.cuhk.edu.hk

A full bayesian approach for boolean genetic network inference.

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.