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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) Frequency of the distance between the Scl motif and the GATA motif in peaks occupied by both Gata1/Gata2 and Scl, plotted such that the GATA motif is at position zero. A peak with a 8–10 bps gap between the two sequence motifs is over-represented. (B) Similarly there is a preferred gap of 20 bps between the CTCF and Pu.1 motifs (C). A gap of –1 bp between the Pu.1 and Scl motifs is significantly enriched. Each motif pair was validated by at least two independent ChIP-seq experiments.
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fig3: (A) Frequency of the distance between the Scl motif and the GATA motif in peaks occupied by both Gata1/Gata2 and Scl, plotted such that the GATA motif is at position zero. A peak with a 8–10 bps gap between the two sequence motifs is over-represented. (B) Similarly there is a preferred gap of 20 bps between the CTCF and Pu.1 motifs (C). A gap of –1 bp between the Pu.1 and Scl motifs is significantly enriched. Each motif pair was validated by at least two independent ChIP-seq experiments.

Mentions: To investigate any new motifs showing distance specificity with respect to TF binding sites from our compendium, we calculated distances between each sample and all possible 3 mers (43 = 64 patterns). We found 3 binding distance preferences; the first pattern, GATA and GAT, had a 3/4 bp gap consistent with Gata factors binding as homo-dimers validated by the crystal structure (Bates et al., 2008).32 The second pattern, GATA and CTG or GTC, had a 9 bp gap mapping to GATA and a half Ebox binding as a part of the Ldb1 complex. The final pattern, Gfi1b and (A/T)GC, had a 2 bp gap (Fig. 3).


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

Joshi A, Gottgens B - Mol Biosyst (2014)

(A) Frequency of the distance between the Scl motif and the GATA motif in peaks occupied by both Gata1/Gata2 and Scl, plotted such that the GATA motif is at position zero. A peak with a 8–10 bps gap between the two sequence motifs is over-represented. (B) Similarly there is a preferred gap of 20 bps between the CTCF and Pu.1 motifs (C). A gap of –1 bp between the Pu.1 and Scl motifs is significantly enriched. Each motif pair was validated by at least two independent ChIP-seq experiments.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: (A) Frequency of the distance between the Scl motif and the GATA motif in peaks occupied by both Gata1/Gata2 and Scl, plotted such that the GATA motif is at position zero. A peak with a 8–10 bps gap between the two sequence motifs is over-represented. (B) Similarly there is a preferred gap of 20 bps between the CTCF and Pu.1 motifs (C). A gap of –1 bp between the Pu.1 and Scl motifs is significantly enriched. Each motif pair was validated by at least two independent ChIP-seq experiments.
Mentions: To investigate any new motifs showing distance specificity with respect to TF binding sites from our compendium, we calculated distances between each sample and all possible 3 mers (43 = 64 patterns). We found 3 binding distance preferences; the first pattern, GATA and GAT, had a 3/4 bp gap consistent with Gata factors binding as homo-dimers validated by the crystal structure (Bates et al., 2008).32 The second pattern, GATA and CTG or GTC, had a 9 bp gap mapping to GATA and a half Ebox binding as a part of the Ldb1 complex. The final pattern, Gfi1b and (A/T)GC, had a 2 bp gap (Fig. 3).

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