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

Nucleic Acids Res. 2017 Jul 27;45(13):e119. 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 (, accessible through a user-friendly web interface or command-line for its integration in pipelines.

MeSH terms

  • Algorithms
  • Amino Acid Motifs
  • Amino Acid Sequence
  • Animals
  • Binding Sites / genetics
  • Chromatin Immunoprecipitation
  • Cluster Analysis
  • Databases, Protein / statistics & numerical data*
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Mice
  • Protein Binding
  • Sequence Analysis, Protein
  • Transcription Factors / chemistry*
  • Transcription Factors / genetics
  • Transcription Factors / metabolism*


  • Transcription Factors