Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 408442
Title ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions
Author(s) Muino, J.M.; Kaufmann, K.; Ham, R.C.H.J. van; Angenent, G.C.; Krajewski, P.
Source Plant Methods 7 (2011). - ISSN 1746-4811
DOI http://dx.doi.org/10.1186/1746-4811-7-11
Department(s) EPS-1
PRI BIOS Applied Bioinformatics
PRI Bioscience
Bioinformatics
Laboratory of Molecular Biology
PRI BIOS Plant Development Systems
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) short read alignment - rna-seq - orchestration - ultrafast - software - system - cells
Abstract Background In vivo 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. Results 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. Conclusions The software ChIP-seq Analysis in R (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.
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