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 or simply
through Bioconductor.
If GitHub source installation is selected, make sure dependency
R packages are pre-installed as shown in the
DESCRIPTION file.
Then, install gcapc with following code.
library(devtools)
install_github("tengmx/gcapc")
Alternatively, installation through Bioconductor is as simple as follows.
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 or simply through Bioconductor.
If GitHub source installation is selected, make sure dependency R packages are pre-installed as shown in the DESCRIPTION file. Then, install gcapc with following code.
Alternatively, installation through Bioconductor is as simple as follows.
Using gcapc
First, load the package into R.
Then, follow the steps introduced in the package vignette to estimate GC-bias or peak calling.
Help
You are very welcome to leave any questions/bug messages at GitHub issues.