ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions
<p>Abstract</p> <p>Background</p> <p><it>In vivo </it>detection of protein-bound genomic regions can be achieved by combining chromatin-immunoprecipitation with next-generation sequencing technology (ChIP-seq). The large amount of sequence data produced by t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-05-01
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| Series: | Plant Methods |
| Online Access: | http://www.plantmethods.com/content/7/1/11 |
| _version_ | 1818472656798744576 |
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| author | van Ham Roeland CHJ Kaufmann Kerstin Muiño Jose M Angenent Gerco C Krajewski Pawel |
| author_facet | van Ham Roeland CHJ Kaufmann Kerstin Muiño Jose M Angenent Gerco C Krajewski Pawel |
| author_sort | van Ham Roeland CHJ |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p><it>In vivo </it>detection of protein-bound genomic regions can be achieved by combining chromatin-immunoprecipitation with next-generation sequencing technology (ChIP-seq). The large amount of sequence data produced by this method needs to be analyzed in a statistically proper and computationally efficient manner. The generation of high copy numbers of DNA fragments as an artifact of the PCR step in ChIP-seq is an important source of bias of this methodology.</p> <p>Results</p> <p>We present here an R package for the statistical analysis of ChIP-seq experiments. Taking the average size of DNA fragments subjected to sequencing into account, the software calculates single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package. An analysis of simulated and experimental ChIP-seq data is presented to demonstrate the robustness of our method against PCR-artefacts and its adequate control of the error rate.</p> <p>Conclusions</p> <p>The software <it>ChIP-seq Analysis in R </it>(CSAR) enables fast and accurate detection of protein-bound genomic regions through the analysis of ChIP-seq experiments. Compared to existing methods, we found that our package shows greater robustness against PCR-artefacts and better control of the error rate.</p> |
| first_indexed | 2024-04-14T04:11:36Z |
| format | Article |
| id | doaj.art-42b71f04069d492cb488d15cd76f5db3 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-04-14T04:11:36Z |
| publishDate | 2011-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-42b71f04069d492cb488d15cd76f5db32022-12-22T02:13:08ZengBMCPlant Methods1746-48112011-05-01711110.1186/1746-4811-7-11ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regionsvan Ham Roeland CHJKaufmann KerstinMuiño Jose MAngenent Gerco CKrajewski Pawel<p>Abstract</p> <p>Background</p> <p><it>In vivo </it>detection of protein-bound genomic regions can be achieved by combining chromatin-immunoprecipitation with next-generation sequencing technology (ChIP-seq). The large amount of sequence data produced by this method needs to be analyzed in a statistically proper and computationally efficient manner. The generation of high copy numbers of DNA fragments as an artifact of the PCR step in ChIP-seq is an important source of bias of this methodology.</p> <p>Results</p> <p>We present here an R package for the statistical analysis of ChIP-seq experiments. Taking the average size of DNA fragments subjected to sequencing into account, the software calculates single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package. An analysis of simulated and experimental ChIP-seq data is presented to demonstrate the robustness of our method against PCR-artefacts and its adequate control of the error rate.</p> <p>Conclusions</p> <p>The software <it>ChIP-seq Analysis in R </it>(CSAR) enables fast and accurate detection of protein-bound genomic regions through the analysis of ChIP-seq experiments. Compared to existing methods, we found that our package shows greater robustness against PCR-artefacts and better control of the error rate.</p>http://www.plantmethods.com/content/7/1/11 |
| spellingShingle | van Ham Roeland CHJ Kaufmann Kerstin Muiño Jose M Angenent Gerco C Krajewski Pawel ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions Plant Methods |
| title | ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions |
| title_full | ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions |
| title_fullStr | ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions |
| title_full_unstemmed | ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions |
| title_short | ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions |
| title_sort | chip seq analysis in r csar an r package for the statistical detection of protein bound genomic regions |
| url | http://www.plantmethods.com/content/7/1/11 |
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