Bayesian unsupervised learning of DNA regulatory binding regions
Advances in Artificial Intelligence
Heuristic Bayesian Segmentation for Discovery of Coexpressed Genes within Genomic Regions
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. Results: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. Availability: Software 'kPax' is available at http://www.rni.helsinki.fi/jic/softa.html Contact: pekka.marttinen@helsinki.fi Supplementary information: http://www.rni.helsinki.fi/~jic/softa.html