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Concerted bioinformatic analysis of the genome-scale blood transcription factor compendium reveals new control mechanisms.

ABSTRACT: Transcription factors play a key role in the development of a disease. ChIP-sequencing has become a preferred technique to investigate genome-wide binding patterns of transcription factors in vivo. Although this technology has led to many important discoveries, the rapidly increasing number of publicly available ChIP-sequencing datasets still remains a largely unexplored resource. Using a compendium of 144 publicly available murine ChIP-sequencing datasets in blood, we show that systematic bioinformatic analysis can unravel diverse aspects of transcription regulation; from genome-wide binding preferences, finding regulatory partners and assembling regulatory complexes, to identifying novel functions of transcription factors and investigating transcription dynamics during development.

SUBMITTER: Joshi A

PROVIDER: S-EPMC4263230 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Concerted bioinformatic analysis of the genome-scale blood transcription factor compendium reveals new control mechanisms.

Joshi Anagha A Gottgens Berthold B

Molecular bioSystems 20141101 11

Transcription factors play a key role in the development of a disease. ChIP-sequencing has become a preferred technique to investigate genome-wide binding patterns of transcription factors in vivo. Although this technology has led to many important discoveries, the rapidly increasing number of publicly available ChIP-sequencing datasets still remains a largely unexplored resource. Using a compendium of 144 publicly available murine ChIP-sequencing datasets in blood, we show that systematic bioin ...[more]

PMID: 25133983

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Project description:BackgroundTranscription factors (TF) regulate expression by binding to specific DNA sequences. A binding event is functional when it affects gene expression. Functionality of a binding site is reflected in conservation of the binding sequence during evolution and in over represented binding in gene groups with coherent biological functions. Functionality is governed by several parameters such as the TF-DNA binding strength, distance of the binding site from the transcription start site (TSS), DNA packing, and more. Understanding how these parameters control functionality of different TFs in different biological contexts is a must for identifying functional TF binding sites and for understanding regulation of transcription.Methodology/principal findingsWe introduce a novel method to screen the promoters of a set of genes with shared biological function (obtained from the functional Gene Ontology (GO) classification) against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. More than 8,000 human (and 23,000 mouse) genes, were assigned to one of 134 GO sets. Their promoters were searched (from 200 bp downstream to 1,000 bp upstream the TSS) for 414 known DNA motifs. We optimized the sequence similarity score threshold, independently for every location window, taking into account nucleotide heterogeneity along the promoters of the target genes. The method, combined with binding sequence and location conservation between human and mouse, identifies with high probability functional binding sites for groups of functionally-related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were tested experimentally.Conclusions/significanceWe identified reliably functional TF binding sites. This is an essential step towards constructing regulatory networks. The promoter region proximal to the TSS is of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.

Dataset Information

Concerted bioinformatic analysis of the genome-scale blood transcription factor compendium reveals new control mechanisms.

Publications

Concerted bioinformatic analysis of the genome-scale blood transcription factor compendium reveals new control mechanisms.

Similar Datasets

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