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This article is part of the supplement: Selected Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA'10)

Open Access Proceedings

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

Juntao Li1, Lei Zhu2, Majid Eshaghi2, Jianhua Liu2 and Krishna Murthy R Karuturi1*

Author Affiliations

1 Computational & Systems Biology, Genome Institute of Singapore, 60 Biopolis Street, (S)138672, Singapore

2 Systems Biology, Genome Institute of Singapore, 60 Biopolis Street, (S)138672, Singapore

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BMC Proceedings 2011, 5(Suppl 2):S8  doi:10.1186/1753-6561-5-S2-S8

Published: 28 April 2011

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.