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Deciphering transcription factor binding patterns from genome-wide high density ChIP-chip tiling array data.

Li J, Zhu L, Eshaghi M, Liu J, Karuturi KM - BMC Proc (2011)

Bottom Line: Our analysis revealed the variation of binding patterns within and across different DNA interacting proteins.We present their utility in understanding transcriptional regulation from ChIP-chip data.Our method can successfully detect the signal regions and characterize the binding patterns in ChIP-chip data which help appropriate analysis of the ChIP-chip data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational & Systems Biology, Genome Institute of Singapore, 60 Biopolis Street, (S)138672, Singapore. karuturikm@gis.a-star.edu.sg.

ABSTRACT

Background: The binding events of DNA-interacting proteins and their patterns can be extensively characterized by high density ChIP-chip tiling array data. The characteristics of the binding events could be different for different transcription factors. They may even vary for a given transcription factor among different interaction loci. The knowledge of binding sites and binding occupancy patterns are all very useful to understand the DNA-protein interaction and its role in the transcriptional regulation of genes.

Results: In the view of the complexity of the DNA-protein interaction and the opportunity offered by high density tiled ChIP-chip data, we present a statistical procedure which focuses on identifying the interaction signal regions instead of signal peaks using moving window binomial testing method and deconvolving the patterns of interaction using peakedness and skewness scores. We analyzed ChIP-chip data of 4 different DNA interacting proteins including transcription factors and RNA polymerase in fission yeast using our procedure. Our analysis revealed the variation of binding patterns within and across different DNA interacting proteins. We present their utility in understanding transcriptional regulation from ChIP-chip data.

Conclusions: Our method can successfully detect the signal regions and characterize the binding patterns in ChIP-chip data which help appropriate analysis of the ChIP-chip data.

No MeSH data available.


Four examples of Atf1 and Pcr1 binding patterns and Rpb1 (with and without H2O2 treatment) occupancies with two repeats. The blue boxes in first track indicate the gene ORF regions, and the vertical light blue bars indicate the stress response gene ORF regions.
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Figure 8: Four examples of Atf1 and Pcr1 binding patterns and Rpb1 (with and without H2O2 treatment) occupancies with two repeats. The blue boxes in first track indicate the gene ORF regions, and the vertical light blue bars indicate the stress response gene ORF regions.

Mentions: Atf1 and Pcr1 bindings also display more skewed patterns at promoters of the stress response genes (more than 2 fold Rpb1 change after H2O2 treatment). As shown in Figure 7, the average skewness score of stress response gene bindings are positive (the sign of skewness score for negative strand gene bindings are changed) and the average skewness score of other genes bindings equal to 0 (p-value=0.007204). Four genes selected in top 20 Atf1/Pcr1-bound genes list [9] as examples are shown in Figure 8.


Deciphering transcription factor binding patterns from genome-wide high density ChIP-chip tiling array data.

Li J, Zhu L, Eshaghi M, Liu J, Karuturi KM - BMC Proc (2011)

Four examples of Atf1 and Pcr1 binding patterns and Rpb1 (with and without H2O2 treatment) occupancies with two repeats. The blue boxes in first track indicate the gene ORF regions, and the vertical light blue bars indicate the stress response gene ORF regions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3090766&req=5

Figure 8: Four examples of Atf1 and Pcr1 binding patterns and Rpb1 (with and without H2O2 treatment) occupancies with two repeats. The blue boxes in first track indicate the gene ORF regions, and the vertical light blue bars indicate the stress response gene ORF regions.
Mentions: Atf1 and Pcr1 bindings also display more skewed patterns at promoters of the stress response genes (more than 2 fold Rpb1 change after H2O2 treatment). As shown in Figure 7, the average skewness score of stress response gene bindings are positive (the sign of skewness score for negative strand gene bindings are changed) and the average skewness score of other genes bindings equal to 0 (p-value=0.007204). Four genes selected in top 20 Atf1/Pcr1-bound genes list [9] as examples are shown in Figure 8.

Bottom Line: Our analysis revealed the variation of binding patterns within and across different DNA interacting proteins.We present their utility in understanding transcriptional regulation from ChIP-chip data.Our method can successfully detect the signal regions and characterize the binding patterns in ChIP-chip data which help appropriate analysis of the ChIP-chip data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational & Systems Biology, Genome Institute of Singapore, 60 Biopolis Street, (S)138672, Singapore. karuturikm@gis.a-star.edu.sg.

ABSTRACT

Background: The binding events of DNA-interacting proteins and their patterns can be extensively characterized by high density ChIP-chip tiling array data. The characteristics of the binding events could be different for different transcription factors. They may even vary for a given transcription factor among different interaction loci. The knowledge of binding sites and binding occupancy patterns are all very useful to understand the DNA-protein interaction and its role in the transcriptional regulation of genes.

Results: In the view of the complexity of the DNA-protein interaction and the opportunity offered by high density tiled ChIP-chip data, we present a statistical procedure which focuses on identifying the interaction signal regions instead of signal peaks using moving window binomial testing method and deconvolving the patterns of interaction using peakedness and skewness scores. We analyzed ChIP-chip data of 4 different DNA interacting proteins including transcription factors and RNA polymerase in fission yeast using our procedure. Our analysis revealed the variation of binding patterns within and across different DNA interacting proteins. We present their utility in understanding transcriptional regulation from ChIP-chip data.

Conclusions: Our method can successfully detect the signal regions and characterize the binding patterns in ChIP-chip data which help appropriate analysis of the ChIP-chip data.

No MeSH data available.