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This article is part of the supplement: Proceedings from the Great Lakes Bioinformatics Conference 2011

Open Access Proceedings

A signal processing approach for enriched region detection in RNA polymerase II ChIP-seq data

Zhi Han12, Lu Tian3, Thierry Pécot45, Tim Huang4, Raghu Machiraju5 and Kun Huang26*

Author Affiliations

1 College of Software, Nankai University, Tianjin, China

2 Department of Biomedical Informatics, The Ohio State University, USA

3 Department of Health Policy and Research - Biostatistics, Stanford University School of Medicine, Stanford, USA

4 Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University, USA

5 Department of Computer Science and Engineering, The Ohio State University, USA

6 The CCC Biomedical Informatics Shared Resource, The Ohio State University, USA

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BMC Bioinformatics 2012, 13(Suppl 2):S2  doi:10.1186/1471-2105-13-S2-S2

Published: 13 March 2012



RNA polymerase II (PolII) is essential in gene transcription and ChIP-seq experiments have been used to study PolII binding patterns over the entire genome. However, since PolII enriched regions in the genome can be very long, existing peak finding algorithms for ChIP-seq data are not adequate for identifying such long regions.


Here we propose an enriched region detection method for ChIP-seq data to identify long enriched regions by combining a signal denoising algorithm with a false discovery rate (FDR) approach. The binned ChIP-seq data for PolII are first processed using a non-local means (NL-means) algorithm for purposes of denoising. Then, a FDR approach is developed to determine the threshold for marking enriched regions in the binned histogram.


We first test our method using a public PolII ChIP-seq dataset and compare our results with published results obtained using the published algorithm HPeak. Our results show a high consistency with the published results (80-100%). Then, we apply our proposed method on PolII ChIP-seq data generated in our own study on the effects of hormone on the breast cancer cell line MCF7. The results demonstrate that our method can effectively identify long enriched regions in ChIP-seq datasets. Specifically, pertaining to MCF7 control samples we identified 5,911 segments with length of at least 4 Kbp (maximum 233,000 bp); and in MCF7 treated with E2 samples, we identified 6,200 such segments (maximum 325,000 bp).


We demonstrated the effectiveness of this method in studying binding patterns of PolII in cancer cells which enables further deep analysis in transcription regulation and epigenetics. Our method complements existing peak detection algorithms for ChIP-seq experiments.