Visualization and Exploration of Conserved Regulatory Modules Using ReXSpecies 2

Abstract

BackgroundThe prediction of transcription factor binding sites is difficult for many reasons. Thus, filtering methods are needed to enrich for biologically relevant (true positive) matches in the large amount of computational predictions that are frequently generated from promoter sequences.ResultsReXSpecies 2 filters predictions of transcription factor binding sites and generates a set of figures displaying them in evolutionary context. More specifically, it uses position specific scoring matrices to search for motifs that specify transcription factor binding sites. It removes redundant matches and filters the remaining matches by the phylogenetic group that the matrices belong to. It then identifies potential transcriptional modules, and generates figures that highlight such modules, taking evolution into consideration. Module formation, scoring by evolutionary criteria and visual clues reduce the amount of predictions to a manageable scale. Identification of transcription factor binding sites of particular functional importance is left to expert filtering. ReXSpecies 2 interacts with genome browsers to enable scientists to filter predictions together with other sequence-related data.ConclusionsBased on ReXSpecies 2, we derive plausible hypotheses about the regulation of pluripotency. Our tool is designed to analyze transcription factor binding site predictions considering their common pattern of occurrence, highlighting their evolutionary history.

LASAGNA-Search: an Integrated Web Tool for Transcription Factor Binding Site Search and Visualization

“…However, these tools have no access to the PWM database in the suite, nor do they scan promoters of specific genes or offer visualization of hits. Two tools motivated by evolutionary conservation are COTRASIF (26) and ReXSpecies2 (27). COTRASIF collects 138 JASPAR and 398 TRANSFAC PWMs and offers whole-genome Ensembl promoter sequences.…”

The challenges and importance of structural variation detection in livestock

“…Even still, such comparisons will need to be conservative. Given the current constraints on the in-silico identification of transcription factor binding sites ( Struckmann et al, 2011 ) and the limited number of genomic regions that are highly conserved among eutherian mammals ( Lindblad-Toh et al, 2011 ), functional prediction from cross-species sequence homology is stymied by accumulated mutations after species divergence. New efforts devoted to functional sequence prediction in food animals, such as the AGENCODE project (Silverstein, J., personal communication), seek to emulate the human model and will provide excellent starting material for the community.…”

Identification of Candidate Transcription Factor Binding Sites in the Cattle Genome

“…In silico prediction methods for the detection of TFBSs can be classified by the order in which they apply sequence homology among several related species. The first method—termed the alignment-free method—uses motif detection algorithms on unaligned genomic sequence prior to comparisons of sequence homology [4] . By contrast, phylogenetic footprinting uses conserved sequence alignments across several animal species as a starting point for TFBS motif detection [4] .…”

“…The first method—termed the alignment-free method—uses motif detection algorithms on unaligned genomic sequence prior to comparisons of sequence homology [4] . By contrast, phylogenetic footprinting uses conserved sequence alignments across several animal species as a starting point for TFBS motif detection [4] . Both techniques are subject to unique benefits and disadvantages based on their starting approaches.…”