Dataset Information

Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes.

ABSTRACT: The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5'-GCGTG-3'. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide functional DREs. In total, 72318 human, 70720 mouse, and 88651 rat high-scoring (MS ? 0.8437) putative DREs were identified. Gene-encoding intragenic DNA regions had ?1.6 times more putative DREs than the noncoding intergenic DNA regions. Furthermore, the promoter region spanning ±1.5 kb of a TSS had the highest density of putative DREs within the genome. Chromosomal analysis found that the putative DRE densities of chromosomes X and Y were significantly lower than the mean chromosomal density. Interestingly, the 10 kb upstream promoter region on chromosome X of the genomes were significantly less dense than the chromosomal mean, while the same region in chromosome Y was the most dense. In addition to providing a detailed genomic map of all DRE cores in the human, mouse, and rat genomes, these data will further aid the elucidation of AhR-mediated signal transduction.

SUBMITTER: Dere E

PROVIDER: S-EPMC4038167 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes.

Dere Edward E Forgacs Agnes L AL Zacharewski Timothy R TR Burgoon Lyle D LD

Chemical research in toxicology 20110317 4

The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5'-GCGTG-3'. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide fu ...[more]

PMID: 21370876

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Project description:BackgroundIt is known that transcription factors frequently act together to regulate gene expression in eukaryotes. In this paper we describe a computational analysis of transcription factor site dependencies in human, mouse and rat genomes.ResultsOur approach for quantifying tendencies of transcription factor binding sites to co-occur is based on a binding site scoring function which incorporates dependencies between positions, the use of information about the structural class of each transcription factor (major/minor groove binder), and also considered the possible implications of varying GC content of the sequences. Significant tendencies (dependencies) have been detected by non-parametric statistical methodology (permutation tests). Evaluation of obtained results has been performed in several ways: reports from literature (many of the significant dependencies between transcription factors have previously been confirmed experimentally); dependencies between transcription factors are not biased due to similarities in their DNA-binding sites; the number of dependent transcription factors that belong to the same functional and structural class is significantly higher than would be expected by chance; supporting evidence from GO clustering of targeting genes. Based on dependencies between two transcription factor binding sites (second-order dependencies), it is possible to construct higher-order dependencies (networks). Moreover results about transcription factor binding sites dependencies can be used for prediction of groups of dependent transcription factors on a given promoter sequence. Our results, as well as a scanning tool for predicting groups of dependent transcription factors binding sites are available on the Internet.ConclusionWe show that the computational analysis of transcription factor site dependencies is a valuable complement to experimental approaches for discovering transcription regulatory interactions and networks. Scanning promoter sequences with dependent groups of transcription factor binding sites improve the quality of transcription factor predictions.

Dataset Information

Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes.

Publications

Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes.

Similar Datasets

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