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

• Published in: Nucleic acids research Vol. 45; no. 13; p. e119
• Main Authors: Castro-Mondragon, Jaime Abraham, Jaeger, Sébastien, Thieffry, Denis, Thomas-Chollier, Morgane, van Helden, Jacques
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27.07.2017
• Subjects: Algorithms; Amino Acid Motifs; Amino Acid Sequence; Animals; Binding Sites - genetics; Bioinformatics; Chromatin Immunoprecipitation; Cluster Analysis; Computer Science; Databases, Protein - statistics & numerical data; High-Throughput Nucleotide Sequencing; Humans; Life Sciences; Methods Online; Mice; Protein Binding; Sequence Analysis, Protein; Transcription Factors - chemistry; Transcription Factors - genetics; Transcription Factors - metabolism; Transcriptome Mapping; Monitoring Gene Expression
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkx314

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 feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines.