Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites.

ABSTRACT: Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI-a sequential Monte Carlo motif-identification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we

SUBMITTER: Jajamovich GH 

PROVIDER: S-EPMC3241671 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature