PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny
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چکیده
منابع مشابه
PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny
A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship be...
متن کاملPhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling
PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. F...
متن کاملGIMSAN: a Gibbs motif finder with significance analysis
UNLABELLED We present GIMSAN (GIbbsMarkov with Significance ANalysis): a novel tool for de novo motif finding. GIMSAN combines GibbsMarkov, our variant of the Gibbs Sampler, described here for the first time, with our recently introduced significance analysis. AVAILABILITY GIMSAN is currently available as a web application and a stand-alone application on Unix and PBS (Portable Batch System) ...
متن کاملPhyloGibbs: A Gibbs Sampler Incorporating Phylogenetic Information
We present a new Gibbs sampler algorithm with the motivation of finding motifs, representing candidate binding sites for transcription factors, in closely related species. Since much conservation here arises not from the existence of functional sites but simply from the lack of sufficient evolutionary divergence between the species, a conventional Gibbs sampler will fail. We compare the effecti...
متن کاملInfo-gibbs: a Motif Discovery Algorithm That Directly Optimizes Information Content during Sampling
MOTIVATION Discovering cis-regulatory elements in genome sequence remains a challenging issue. Several methods rely on the optimization of some target scoring function. The information content (IC) or relative entropy of the motif has proven to be a good estimator of transcription factor DNA binding affinity. However, these information-based metrics are usually used as a posteriori statistics r...
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ژورنال
عنوان ژورنال: PLoS Computational Biology
سال: 2005
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.0010067