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

Joshi A, Gottgens B - Mol Biosyst (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK. anagha.joshi@roslin.ed.ac.uk.

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.

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Related in: MedlinePlus

(A, B) Hierarchical clustering of pair-wise peak overlap of all promoters and enhancers across all cell types, red representing positive Pearson's correlation coefficient values and blue representing negative correlation coefficients. (C, D) 5-way Venn diagram of Pu.1 ChIP sequencing data from 5 cell types in promoters and enhancers representing higher overlap in promoters compared to enhancers.
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fig1: (A, B) Hierarchical clustering of pair-wise peak overlap of all promoters and enhancers across all cell types, red representing positive Pearson's correlation coefficient values and blue representing negative correlation coefficients. (C, D) 5-way Venn diagram of Pu.1 ChIP sequencing data from 5 cell types in promoters and enhancers representing higher overlap in promoters compared to enhancers.

Mentions: We collected genome-wide binding patterns (peaks) of 144 publicly available murine ChIP-sequencing datasets for 53 transcription factors in 15 major blood lineages and leukemia15 to obtain 270 261 regulatory regions with at least one factor binding. We classified peaks into two groups: promoter and enhancer peaks by defining the peaks within 1 kb of TSS as promoter peaks. 7.5% of the total peaks belonged to promoters and all non-promoter peaks were classified as putative enhancers. The hierarchical clustering of enhancers clustered them according to the cell type (Fig. 1B and Fig. S2, ESI†) irrespective of the factors such as Fli1 in hematopoietic progenitor cells (HPC) clustered with other samples in HPCs and Fli1 in T cells clustered with T cell samples. There was an exception of one transcription factor, Pu.1. Pu.1 samples across multiple cell types clustered together.14


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

Joshi A, Gottgens B - Mol Biosyst (2014)

(A, B) Hierarchical clustering of pair-wise peak overlap of all promoters and enhancers across all cell types, red representing positive Pearson's correlation coefficient values and blue representing negative correlation coefficients. (C, D) 5-way Venn diagram of Pu.1 ChIP sequencing data from 5 cell types in promoters and enhancers representing higher overlap in promoters compared to enhancers.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263230&req=5

fig1: (A, B) Hierarchical clustering of pair-wise peak overlap of all promoters and enhancers across all cell types, red representing positive Pearson's correlation coefficient values and blue representing negative correlation coefficients. (C, D) 5-way Venn diagram of Pu.1 ChIP sequencing data from 5 cell types in promoters and enhancers representing higher overlap in promoters compared to enhancers.
Mentions: We collected genome-wide binding patterns (peaks) of 144 publicly available murine ChIP-sequencing datasets for 53 transcription factors in 15 major blood lineages and leukemia15 to obtain 270 261 regulatory regions with at least one factor binding. We classified peaks into two groups: promoter and enhancer peaks by defining the peaks within 1 kb of TSS as promoter peaks. 7.5% of the total peaks belonged to promoters and all non-promoter peaks were classified as putative enhancers. The hierarchical clustering of enhancers clustered them according to the cell type (Fig. 1B and Fig. S2, ESI†) irrespective of the factors such as Fli1 in hematopoietic progenitor cells (HPC) clustered with other samples in HPCs and Fli1 in T cells clustered with T cell samples. There was an exception of one transcription factor, Pu.1. Pu.1 samples across multiple cell types clustered together.14

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK. anagha.joshi@roslin.ed.ac.uk.

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.

Show MeSH
Related in: MedlinePlus