To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ChIPQC")
In most cases, you don't need to download the package archive at all.
ChIPQC
Quality metrics for ChIPseq data
Bioconductor version: 2.14
Quality metrics for ChIPseq data
Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark
Maintainer: Tom Carroll <tc.infomatics at gmail.com>, Rory Stark <rory.stark at cruk.cam.ac.uk>
Citation (from within R,
enter citation("ChIPQC")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ChIPQC")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ChIPQC")
| R Script | Assessing ChIP-seq sample quality with ChIPQC | |
| ChIPQCSampleReport.pdf | ||
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | ChIPSeq, QualityControl, ReportWriting, Sequencing, Software |
| Version | 1.0.9 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | GPL (>= 3) |
| Depends | R (>= 3.0.0), ggplot2, DiffBind |
| Imports | BiocGenerics(>= 0.8.0), Rsamtools(>= 1.14.2), GenomicRanges(>= 1.14.4), chipseq(>= 1.12.0), GenomicAlignments(>= 0.99.2), gtools, BiocParallel, methods, IRanges, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils |
| Suggests | BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | ChIPQC_1.0.9.tar.gz |
| Windows Binary | ChIPQC_1.0.9.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | ChIPQC_1.0.9.tgz |
| Mac OS X 10.9 (Mavericks) | ChIPQC_1.0.9.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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