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

Han Z, Tian L, Pécot T, Huang T, Machiraju R, Huang K - BMC Bioinformatics (2012)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Software, Nankai University, Tianjin, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

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Denoising on PolII ChIP-seq data for MCF7 cell line using NL-means algorithm. This region is over the gene ELAC2. Different parameters for the NL-means algorithm are tested on the ChIP-seq data. The second panel uses the parameter sets used in the rest of this paper (R=10(bins),L=15, and σ=10)
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Figure 2: Denoising on PolII ChIP-seq data for MCF7 cell line using NL-means algorithm. This region is over the gene ELAC2. Different parameters for the NL-means algorithm are tested on the ChIP-seq data. The second panel uses the parameter sets used in the rest of this paper (R=10(bins),L=15, and σ=10)

Mentions: Formally, given a signal with data points X={xi,i = 1,...,N}, the filtered value at xi is defined as , where is a difference measure between xi and a neighbouring point xj under the constraints w(i,j)≥0 and Specifically, , where is the normalizing factor, , and denote a fixed-size neighbourhood centred at the position i. In practice, searching for similar neighbourhood patterns over the entire genome is not feasible. Instead, a parameter specifying the range of search is needed. In summary, the NL-means algorithm requires three parameters, the size of neighbourhood the range of search and the weight parameter We evaluated a series of parameter combinations (Figure 2) and in this study we use R = 10(bins) L = 15, and σ = 10


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

Han Z, Tian L, Pécot T, Huang T, Machiraju R, Huang K - BMC Bioinformatics (2012)

Denoising on PolII ChIP-seq data for MCF7 cell line using NL-means algorithm. This region is over the gene ELAC2. Different parameters for the NL-means algorithm are tested on the ChIP-seq data. The second panel uses the parameter sets used in the rest of this paper (R=10(bins),L=15, and σ=10)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3375632&req=5

Figure 2: Denoising on PolII ChIP-seq data for MCF7 cell line using NL-means algorithm. This region is over the gene ELAC2. Different parameters for the NL-means algorithm are tested on the ChIP-seq data. The second panel uses the parameter sets used in the rest of this paper (R=10(bins),L=15, and σ=10)
Mentions: Formally, given a signal with data points X={xi,i = 1,...,N}, the filtered value at xi is defined as , where is a difference measure between xi and a neighbouring point xj under the constraints w(i,j)≥0 and Specifically, , where is the normalizing factor, , and denote a fixed-size neighbourhood centred at the position i. In practice, searching for similar neighbourhood patterns over the entire genome is not feasible. Instead, a parameter specifying the range of search is needed. In summary, the NL-means algorithm requires three parameters, the size of neighbourhood the range of search and the weight parameter We evaluated a series of parameter combinations (Figure 2) and in this study we use R = 10(bins) L = 15, and σ = 10

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Software, Nankai University, Tianjin, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Show MeSH
Related in: MedlinePlus