TRANSFAC Retrieval Program: A Network Model Database of Eukaryotic Transcription Regulating Sequences and Proteins

Knüppel, Rainer; Dietze, Peter; Lehnberg, W.; Frech, Kornelie; Wingender, Edgar

doi:10.1089/cmb.1994.1.191

Cited by 95 publications

(71 citation statements)

References 12 publications

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“…However, understanding the mechanism(s) responsible for these effects will require the identification of the protein(s) binding within this domain. The nucleotide change at -374 resulting from a T-to-A substitution disrupts the binding of a variety of CTF/NF1 transcription factors established from the TRANSFAC database (24). A number of these proteins have been demonstrated to be involved in negative and positive gene regulation (25).…”

Section: Discussionmentioning

confidence: 99%

Effects of Novel Polymorphisms in the RAGE Gene on Transcriptional Regulation and Their Association With Diabetic Retinopathy

et al. 2001

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Interactions between advanced glycation end products (AGEs) and the receptor for AGE (RAGE) are implicated in the vascular complications in diabetes. We have identified eight novel polymorphisms, of which the ؊1420 (GGT)n, ؊1393 G/T, ؊1390 G/T, and ؊1202 G/A were in the overlapping PBX2 3 untranslated region (UTR), and the ؊429 T/C (66.5% TT, 33.5% TC/CC), ؊407 to -345 deletion (99% I, 1% I/D, 0% D), ؊374 T/A (66.4% TT, 33.6% TA/AA), and ؉20 T/A were in the RAGE promoter. To evaluate the effects on transcriptional activity, we measured chloramphenicol acetyl transferase (CAT) reporter gene expression, driven by variants of the -738 to ؉49 RAGE gene fragment containing the four polymorphisms identified close to the transcriptional start site. The -429 C, ؊374 A, and 63-bp deletion alleles resulted in a mean increase of CAT expression of twofold (P < 0.0001), threefold (P < 0.001), and fourfold (P < 0.05), respectively, with the -374 T and A alleles yielding highly differential binding of nuclear protein extract from both monocyte-and hepatocyte-derived cell lines. The prevalence of the functional polymorphisms were investigated in subjects with type 2 diabetes (106 with and 109 without retinopathy), with the -429 C allele showing an increase in the retinopathy group (P < 0.05). These data suggest that the polymorphisms involved in differences in RAGE gene regulation may influence the pathogenesis of diabetic vascular complications. Diabetes 50:1505-1511, 2001

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Section: Discussionmentioning

confidence: 99%

Effects of Novel Polymorphisms in the RAGE Gene on Transcriptional Regulation and Their Association With Diabetic Retinopathy

et al. 2001

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“…A matrix core is identified in each PFM by TRANSFAC as the five most conserved contiguous columns (highest confidence) (Knuppel et al (1994)). Extended cores were constructed to include columns that are adjacent to the cores and whose information content is greater than the information content of the highest entropy column in the core.…”

Section: Pfm-family Constructionmentioning

confidence: 99%

“…These binding sites are often represented as matrices, which are known in the literature under a variety of names: position weight matrices, position frequency matrices, alignment matrices, profiles, etc (Knuppel et al (1994), ; Lenhard and Wasserman (2002)). We refer to a matrix consisting of nucleotide counts per position as a position frequency matrix (PFM).…”

Section: Introductionmentioning

confidence: 99%

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Similarity of position frequency matrices for transcription factor binding sites

Schones¹,

Sumazin²,

Zhang³

2004

Bioinformatics

103

107

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Motivation:Transcription-factor binding sites in promoter sequences of higher eukaryotes are commonly modeled using position frequency matrices. The ability to compare position frequency matrices representing binding sites is especially important for de novo sequence motif discovery, where it is desirable to compare putative matrices to one another and to known matrices. Results: We describe a position frequency matrix similarity quantification method based on product-multinomial distributions, demonstrate its ability to identify position frequency matrix similarity and show that it has a better false positive to false negative ratio compared to existing methods.We group transcription factor binding site frequency matrices from two libraries into matrix families, and identify the matrices that are common and unique to these libraries. We identify similarities and differences between the skeletal-muscle-specific and non-musclespecific frequency matrices for the binding sites of Mef-2, Myf, Sp-1, SRF and TEF of Wasserman and Fickett (1998). We further identify known frequency matrices and matrix families that are strongly similar to the matrices given by Wasserman and Fickett. We provide methodology and tools to compare and query libraries of frequency matrices for transcription factor binding sites. Availability: Software is available to use over the web at

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“…1b, and comparisons of the discovered motifs with those described previously are shown in Supplementary Table 2. The discovered motifs provide significantly more information than was previously available; for 21 of the regulators there was no prior specificity information in the literature, and detailed probability matrices had previously been determined for only 17 regulators for which we report motifs 14 . For Cin5, which showed the largest difference between the computationally derived motif (TTACRTAA) and the previously reported site (TTACTAA; Supplementary Table 2), we found that the motif we report is also the preferred target in vitro ( Supplementary Fig.…”

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confidence: 99%

Transcriptional regulatory code of a eukaryotic genome

Harbison

Gordon

Lee

et al. 2004

Nature

1,978

2,937

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DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression 1 . Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation 2-6 , but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.We used genome-wide location analysis 7-10 to determine the genomic occupancy of 203 DNA-binding transcriptional regulators in rich media conditions and, for 84 of these regulators, in at least 1 of 12 other environmental conditions (Supplementary Table 1, Supplementary Fig. 1; http://web.wi.mit.edu/young/regulatory_code). These 203 proteins are likely to include nearly all of the DNA-binding transcriptional regulators encoded in the yeast genome. Regulators were selected for profiling in an additional environment if they were essential for growth in that environment or if there was other evidence implicating them in the regulation of gene expression in that environment. The genome-wide location data identified 11,000 unique interactions between regulators and promoter regions at high confidence (P ≤ 0.001).

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TRANSFAC Retrieval Program: A Network Model Database of Eukaryotic Transcription Regulating Sequences and Proteins

Cited by 95 publications

References 12 publications

Effects of Novel Polymorphisms in the RAGE Gene on Transcriptional Regulation and Their Association With Diabetic Retinopathy

Effects of Novel Polymorphisms in the RAGE Gene on Transcriptional Regulation and Their Association With Diabetic Retinopathy

Similarity of position frequency matrices for transcription factor binding sites

Transcriptional regulatory code of a eukaryotic genome

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