TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes
V. Matys,Olga V. Kel-Margoulis,Ellen Fricke,Ines Liebich,Sigrid Land,A. Barre-Dirrie,Ingmar Reuter,D. Chekmenev,Mathias Krull,Klaus Hornischer,Nico Voss,Philip Stegmaier,Birgit Lewicki-Potapov,H. Saxel,Alexander E. Kel,Edgar Wingender +15 more
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TLDR
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.Abstract:
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 http://www.gene-regulation.com/pub/databases.html.read more
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A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes
Bhaskar Dutta,Lajos Pusztai,Yuan Qi,Fabrice Andre,Vladimir Lazar,Giampaolo Bianchini,Naoto T. Ueno,Roshan Agarwal,B Wang,Christine Y. Shiang,Gabriel N. Hortobagyi,Gordon B. Mills,William Fraser Symmans,Gábor Balázsi +13 more
TL;DR: Clinical subtype-specific driver-networks identified through data integration are reproducible and functionally important and found that TNBC driver-network members genes have increased functional specificity to TNBC cell lines and higher functional sensitivity compared with genes selected by differential expression alone.
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ZFNGenome: a comprehensive resource for locating zinc finger nuclease target sites in model organisms
Deepak Reyon,Jessica R Kirkpatrick,Jeffry D. Sander,Feng Zhang,Daniel F. Voytas,J. Keith Joung,Drena Dobbs,Clark R. Coffman +7 more
TL;DR: The motivation for this study is to make resources for genome modifications using OPEN-generated ZFNs more accessible to researchers by creating a user-friendly interface that identifies and provides quality scores for all potential ZFN target sites in the complete genomes of several model organisms.
Journal ArticleDOI
Characterization of three growth hormone-responsive transcription factors preferentially expressed in adult female liver
TL;DR: Cutl2 and Trim24 both display transcriptional repressor activity and could contribute to the loss of GH-regulated, male-specific liver gene expression seen in male mice deficient in STAT5b or HNF4alpha, and Binding sites for Cutl1, whose DNA-binding specificity is close to that of Cutl2, were statistically overrepresented inSTAT5b-dependent male- specific mouse genes, lending support to this hypothesis.
Journal ArticleDOI
Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection
TL;DR: It is shown that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor and a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC are made.
Journal ArticleDOI
In vivo validation of a computationally predicted conserved Ath5 target gene set.
Filippo Del Bene,Laurence Ettwiller,Laurence Ettwiller,Dorota Skowronska-Krawczyk,Herwig Baier,Jean-Marc Matter,Ewan Birney,Joachim Wittbrodt +7 more
TL;DR: This procedure is a fast and predictive tool to in silico filter the target genes of a given transcription factor with defined binding site and validate a selection of the linked target genes by showing coexpression with and transcriptional regulation by Ath5.
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TRANSFAC®: transcriptional regulation, from patterns to profiles
V. Matys,Ellen Fricke,Robert Geffers,Ellen Gößling,Martin Haubrock,Reinhard Hehl,Klaus Hornischer,Dagmar Karas,Alexander E. Kel,Olga V. Kel-Margoulis,Dorothee-U. Kloos,Sigrid Land,Birgit Lewicki-Potapov,Holger Michael,Richard Münch,Ingmar Reuter,Stella Rotert,H. Saxel,Maurice Scheer,S. Thiele,Edgar Wingender +20 more
TL;DR: The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data.
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