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.