gcapc
GC Aware Peak Caller
Bioconductor version: 3.23 · Package version: 1.36.0
Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("gcapc") Details
| Maintainer | Mingxiang Teng <tengmx@gmail.com> |
| Author | Mingxiang Teng and Rafael A. Irizarry |
| License | GPL-3 |
| URL | https://github.com/tengmx/gcapc |
| Downloads rank | 646 |
| Source branch | RELEASE_3_23 |
| biocViews | BatchEffect, ChIPSeq, PeakDetection, Sequencing, Software |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | gcapc_1.36.0.tar.gz |
| Windows binary (x86_64) | gcapc_1.36.0.zip |
| macOS binary (arm64) | gcapc_1.36.0.tgz |
| macOS binary (x86_64) | gcapc_1.36.0.tgz |
Dependencies
Depends: R (>= 3.4)
Imports: BiocGenerics, Seqinfo, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10
Reverse dependencies
Suggests Me (1): epigraHMM