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 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.

Specific chromatin landscapes and transcription factors couple breast cancer subtype with metastatic relapse to lung or brain

“…We first filtered this file for confidence scores greater than 0. Next, we collapsed TFs into clusters based on RSAT matrix clustering of JASPAR CORE vertebrate TFs (http://jaspar.genereg.net/matrix-clusters/vertebrates/) [51]. We named each cluster using the most common TF family within the cluster.…”

RSAT Var-tools: an accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

“…variation-scan performance was assessed by randomly selecting a variant from the 1000 genomes project [21] and a motif from the RSAT non-redundant motifs collection [24] . The randomly selected variant was used to create sets with different numbers of replicates, ranging from one thousand to nine millions, to estimate the relation between running time and the amount of evaluated variants.…”

“…According to the original study [25] , binding sites for the following TFs were enriched in the sequences of interest: GATA1, KLF1, DHS, TAL1, ETS, FLI1 and AP-1. Therefore, a total of 48 PSSMs annotated as related to these TFs were retrieved from the non-redundant RSAT motif collection [24] , and given as input to variation-scan .…”

“…Finally, we used retrieve-variation-seq to retrieve the sequence variants around each SOI, and predicted the impact of the variation on TF binding for each motif of the JASPAR non-redundant RSAT motif collection [24] using variation-scan , with a threshold of 1e-4 on the p-value and 100 on the p-value ratio.…”

“…Using the high-confidence SNPs from two "Platinum" Genomes [26] , we determined haplotype variants that are likely to affect one TF binding site. We selected variants 30bps apart, located in open chromatin, to be analysed with variation-scan using the non redundant motif collection at RSAT [24] . We detected 7406 haplotype sites with more than one SNP with a probable effect in binding of 361 TFs.…”