To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("MEDIPS")
In most cases, you don't need to download the package archive at all.
MEDIPS
(MeD)IP-seq data analysis
Bioconductor version: 2.14
MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples as well as saturation and correlation analyses.
Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich
Maintainer: Lukas Chavez <lchavez at liai.org>
Citation (from within R,
enter citation("MEDIPS")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("MEDIPS")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MEDIPS")
| R Script | MEDIPS | |
| Reference Manual |
Details
| biocViews | ChIPSeq, CpGIsland, DNAMethylation, DifferentialExpression, Preprocessing, QualityControl, Sequencing, Software, Visualization |
| Version | 1.14.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | GPL (>=2) |
| Depends | R (>= 3.0), BSgenome, DNAcopy |
| Imports | Biostrings, BSgenome, Rsamtools, graphics, gtools, IRanges, methods, stats, utils, GenomicRanges, edgeR, GenomicFeatures, DNAcopy, biomaRt, rtracklayer |
| Suggests | BSgenome, BSgenome.Hsapiens.UCSC.hg19, MEDIPSData |
| 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 | MEDIPS_1.14.0.tar.gz |
| Windows Binary | MEDIPS_1.14.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | MEDIPS_1.14.0.tgz |
| Mac OS X 10.9 (Mavericks) | MEDIPS_1.14.0.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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