Title |
An Integrated Pipeline for the Genome-Wide Analysis of Transcription Factor Binding Sites from ChIP-Seq
|
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Published in |
PLOS ONE, February 2011
|
DOI | 10.1371/journal.pone.0016432 |
Pubmed ID | |
Authors |
Eloi Mercier, Arnaud Droit, Leping Li, Gordon Robertson, Xuekui Zhang, Raphael Gottardo |
Abstract |
ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data. |
X Demographics
Geographical breakdown
Country | Count | As % |
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France | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 5% |
France | 4 | 2% |
Italy | 2 | 1% |
Germany | 1 | <1% |
Austria | 1 | <1% |
Sweden | 1 | <1% |
Turkey | 1 | <1% |
Slovenia | 1 | <1% |
United Kingdom | 1 | <1% |
Other | 2 | 1% |
Unknown | 152 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 55 | 31% |
Student > Ph. D. Student | 53 | 30% |
Student > Master | 16 | 9% |
Student > Bachelor | 11 | 6% |
Professor | 9 | 5% |
Other | 23 | 13% |
Unknown | 8 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 95 | 54% |
Biochemistry, Genetics and Molecular Biology | 38 | 22% |
Computer Science | 14 | 8% |
Medicine and Dentistry | 8 | 5% |
Engineering | 3 | 2% |
Other | 7 | 4% |
Unknown | 10 | 6% |