# gcapc: GC effects aware peak caller ### Introduction ChIP-seq has been widely utilized as the standard technology to detect protein binding regions, where peak calling algorithms were developed particularly to serve the analysis. Existing peak callers lack of power on ranking peaks' significance due to sequencing technology might undergo sequence context biases, *e.g.* GC bias. *gcapc* is designed to address this deficiency by modeling GC effects into peak calling. ### Installation *gcapc* is an R/Bioconductor package, which can be installed with source code documented in [GitHub](https://github.com/tengmx/gcapc) or simply through [Bioconductor](https://bioconductor.org/packages/gcapc). If GitHub source installation is selected, make sure dependency R packages are pre-installed as shown in the [DESCRIPTION](https://github.com/tengmx/gcapc/blob/master/DESCRIPTION) file. Then, install *gcapc* with following code. ```s library(devtools) install_github("tengmx/gcapc") ``` Alternatively, installation through Bioconductor is as simple as follows. ```s if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("gcapc") ``` ### Using *gcapc* First, load the package into R. ```s library(gcapc) ``` Then, follow the steps introduced in the package [vignette](https://bioconductor.org/packages/devel/bioc/vignettes/gcapc/inst/doc/gcapc.html) to estimate GC-bias or peak calling. ### Help You are very welcome to leave any questions/bug messages at [GitHub issues](https://github.com/tengmx/gcapc/issues).