TRANSFAC®: transcriptional regulation, from patterns to profiles

By V. Matys, E. Fricke, R. Geffers, E. Gößling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A. Kel, O. Kel-Margoulis and 11 more. Nucleic Acids Res., 2003

2,253 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/30adafb14c8799cbd6ac55855ee5a26d514d673c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Manolis Kellis
2. Martha L. Bulyk
3. William Stafford Noble
4. Richard A. Young
5. Wyeth W. Wasserman
6. Jason Ernst
7. Albin Sandelin
8. Xiaohui S. Xie

Citations in the corpus

• CEAS: cis-regulatory element annotation system (2006, methodology): “For known motif-scanning methods, the best is TRANSFAC (15) or JASPAR (16) motif scan.”
• Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. (2007, methodology): “tion factor motifs were defined by scanning all human gene promoter sequences for the presence of 361 experimentally defined transcription factor–binding sites [19].”
• MIPS: analysis and annotation of proteins from whole genomes in 2005 (2005, positive): “32, Database issue a Oxford University Press 2004; all rights reserved [TRANSFAC ( 3 )] and metabolic pathways are either part of the core yeast database or can be retrieved using the BioRS data integration system.”
• Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence (2011, background): “This approach takes advantage of the many genome sequences that are available, as well as databases describing the sequence specificity of individual TF”
• CYGD: the Comprehensive Yeast Genome Database (2004, background): “The collection of yeast transcription factors, their respective target genes and binding sites in CYGD is structurally based on the TRANSFAC1 database ( 16 ).”
• A hierarchical network of transcription factors governs androgen receptor-dependent prostate cancer growth. (2007, methodology): “To search for these AR partners, we mapped all of the mammalian transcription factor motifs in the TRANSFAC database (Matys et al., 2003) to the 90 AR binding regions to identify motifs significantly enriched compared to the chromosomes 21 and 22 genomic background.”
• Natural similarity measures between position frequency matrices with an application to clustering (2008, methodology): “As a further test set, we used a vertebrate subset of Transfac (Matys et al. , 2003) PFMs of version 11.1.”
• HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants (2011, methodology): “Briefly, a set of PWMs was collected from TRANSFAC (25), JASPAR (26), and protein-binding microarray (PBM) experiments (27–29).”
• Computational prediction of transcription-factor binding site locations (2003, methodology): “The largest and most commonly used collection is the TRANSFAC database [28], which catalogs eukaryotic TFs and their known binding sites, and provides PWMs. Likewise, a number of tools, such as MatInd and MatInspector [29], MATRIX SEARCH [30], SIGNAL SCAN [31], and rVISTA [32], have been developed…”
• Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network (2007, background): “], and promoter binding motifs are available in a diversity of species [”