TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes

By V. Matys, O. Kel-Margoulis, E. Fricke, I. Liebich, S. Land, A. Barre-Dirrie, I. Reuter, D. Chekmenev, M. Krull, K. Hornischer and 6 more. Nucleic Acids Res., 2005

2,392 citations recorded by the source corpus.

The TRANSFAC® database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel® on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC®. The list of databases which are linked to the common GENE table of TRANSFAC® and TRANSCompel® has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel® contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC®, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC® 7.0 and TRANSCompel® 7.0, are accessible under .

Subfields: Molecular Biology

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Citations in the corpus

TRED: a transcriptional regulatory element database, new entries and other development (2007, background): “In order to provide users with more complete information of the genes, cross-references to other well-known database such as PubMed, GenBank, GeneCards (1) and TRANSFAC ( 2 ) were established in TRED.”
FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. (2008, neutral): “Cell 132, 958–970, March 21, 2008 ª2008 Elsevier Inc. 967 motif (Matys et al., 2006) and the genomic background sequence composition to identify hits above certain relative entropy cutoff t. Letting x i , a value between 0 and 1, denote the relative location of motif hit i on the ChIP regions (0…”
hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets (2019, methodology): “For the prediction of candidate TFBS(s) on given sequence(s), we integrated the motif matrices of humans from the hTFtarget database and other resources, including HOCOMOCO [22], JASPAR [23], and TRANSFAC v2018 databases [24].”
Molecular signatures database (MSigDB) 3.0 (2011, background): “For example, Gene Ontology (GO) (Ashburner, 2000) represents a hierarchy of controlled terms to describe individual gene products, while TRANSFAC (Matys, 2006) stores information about transcription factor binding sites.”
ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data (2012, methodology): “Known transcription factor binding matrices were downloaded from the JASPAR (18), Transfac (19), Cistrome (20) and UniPROBE (21) databases.”
JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles (2009, neutral): “While the community has valued the open-access policy and non-redundant nature of JASPAR, a common complaint was that the size of the core collection was small compared to the commercial TransFac database ( 9 ), currently the only comprehensive alternative to JASPAR.”
PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants (2016, methodology): “0) (34)), literature (22) and motifs discovered from ChIPseq peaks using MEME-ChIP (35).”
Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, methodology): “…Chen et al. (2017)) use binding motifs represented as position weight matrix (PWM) models that have been obtained from databases like TRANSFAC (Matys et al. , 2006), Jaspar (Math-69 elier et al. , 2016), UniProbe (Newburger and Bulyk, 2009) or CisBP (Weirauch et al. , 2014), or from motif…”
Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, background): “Several studies have indicated that primary TF DNA binding specificity evolves slowly, and is extremely conserved between mammalian species (see e g., Bohmann et al., 1987; Struhl, 1987; Merika and Orkin, 1993; Amoutzias et al., 2007; Wei et al., 2010; Jolma et al., 2013).”
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool (2013, methodology): “These six libraries include the ability to identify transcription factors that are enriched for target genes within the input list using four different options: 1) ChEA [10]; 2) position weight matrices (PWMs) from TRANSFAC [11] and JASPAR [12]; 3) target genes generated from PMWs downloaded from th”
Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes (2012, methodology): “target relationships (from TRANSFAC [38]), protein–protein”
DEEP: a general computational framework for predicting enhancers (2014, methodology): “Usage of DHSs combined with TF binding site (TFBS) motifs extracted from well-known databases (Transfac (17), Jaspar (18), HOCOMOCO (19)) is promising, but has several limitations.”