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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("compEpiTools")
In most cases, you don't need to download the package archive at all.
compEpiTools
Tools for computational epigenomics
Bioconductor version: 3.0
Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples.
Author: http://genomics.iit.it/groups/computational-epigenomics.html
Maintainer: Kamal Kishore <kamal.fartiyal84 at gmail.com>
Citation (from within R,
enter citation("compEpiTools")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("compEpiTools")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("compEpiTools")
| R Script | compEpiTools.pdf | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Coverage, GeneExpression, GenomeAnnotation, Sequencing, Software, Visualization |
| Version | 1.0.3 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL |
| Depends | R (>= 3.1.1), methods, topGO, GenomicRanges |
| Imports | AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb |
| LinkingTo | |
| Suggests | BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | compEpiTools_1.0.3.tar.gz |
| Windows Binary | compEpiTools_1.0.3.zip |
| Mac OS X 10.6 (Snow Leopard) | compEpiTools_1.0.3.tgz |
| Mac OS X 10.9 (Mavericks) | compEpiTools_1.0.3.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
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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