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source("http://bioconductor.org/biocLite.R")
biocLite("Repitools")
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Repitools
Epigenomic tools
Bioconductor version: 2.14
Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc.
Author: Mark Robinson <mark.robinson at imls.uzh.ch>, Dario Strbenac <d.strbenac at garvan.org.au>, Aaron Statham <a.statham at garvan.org.au>, Andrea Riebler <andrea.riebler at math.ntnu.no>
Maintainer: Mark Robinson <mark.robinson at imls.uzh.ch>
Citation (from within R,
enter citation("Repitools")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Repitools")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Repitools")
| R Script | Using Repitools for Epigenomic Sequencing Data | |
| Reference Manual |
Details
| biocViews | DNAMethylation, GeneExpression, MethylSeq, Software |
| Version | 1.10.1 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | LGPL (>= 2) |
| Depends | R (>= 3.0.0), methods, BiocGenerics(>= 0.8.0) |
| Imports | IRanges(>= 1.20.0), GenomicRanges, GenomicAlignments, BSgenome, gplots, grid, MASS, gsmoothr, edgeR(>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, parallel, Biostrings, Rsamtools, cluster |
| Suggests | ShortRead, BSgenome.Hsapiens.UCSC.hg18, rtracklayer |
| 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 | Repitools_1.10.1.tar.gz |
| Windows Binary | Repitools_1.10.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | Repitools_1.10.1.tgz |
| Mac OS X 10.9 (Mavericks) | Repitools_1.10.1.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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