Connecting transcription factors, their experimentally-characterized binding sites and regulated genes, as well as promoter reports with mapped annotated TF-binding sites and high-throughput data (ChIP-seq etc.).
Displaying genes with which promoters the enhancer interacts, tissues and cell types/lines the enhancer is active in, and genomic regions such as histone modification sequences, DNase I hypersensitivity sites, and transcription factor binding sites that overlap with the enhancer.
More than 70,000 containing details from the primary literature for more than 300 species, with a focus on human, mouse, rat, yeast, and plants.
Transcription factor reports
More than 48,000 (and 1,700 miRNA), a subset of which provide GO functional assignments, disease associations and expression pattern assignments.
Transcription factor–site interactions
More than 68,000 manually annotated; plus more than 74,000 miRNA-target site interactions.
TFBS ChIP-seq reports
More than 2,000 high-throughput experiments comprising 91M transcription factor bound fragments/intervals, many of which have been annotated with the best-scoring binding site and neighbouring genes, as well as 161 DNase hypersensitivity ChIP-Seq experiments comprising 15M fragments and 1M histone modification fragments.
Positional weight matrices (PWMs)
More than 10,000, to be used by MatchTM, FMatch, CMsearch and a number of geneXplain bricks to predict TF binding sites.
More than 360,000 for human and nine other organisms, including transcription start sites, CpG islands, single nucleotide polymorphisms (SNPs) and various other annotations.
Transcription factor binding sites
Including tools for prediction, de novo motif identification, matrix comparison and miRNA regulator identification.
MATCH Suite – an integrated tool for TFBS search
With Match Suite you can identify transcription factor binding sites (TFBSs) enriched in the promoters in focus as well as in a single sequence, for human genes. Additionally, Match Suite enables to analyze functional enrichment of your gene set, find only transcription factors (TFs) expressed in the tissue of your interest, filter the predicted TFs by those intersecting with conservative regions of the genome and much more.