RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.

ABSTRACT: Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feat

SUBMITTER: Castro-Mondragon JA 

PROVIDER: S-EPMC5737723 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature