To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("BiSeq")
In most cases, you don't need to download the package archive at all.
BiSeq
Processing and analyzing bisulfite sequencing data
Bioconductor version: 3.0
The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples.
Author: Katja Hebestreit, Hans-Ulrich Klein
Maintainer: Katja Hebestreit <katjah at stanford.edu>
Citation (from within R,
enter citation("BiSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("BiSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("BiSeq")
| R Script | An Introduction to BiSeq | |
| Reference Manual |
Details
| biocViews | DNAMethylation, Genetics, MethylSeq, Sequencing, Software |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | LGPL-3 |
| Depends | R (>= 2.15.2), methods, S4Vectors, IRanges(>= 1.17.24), GenomicRanges, Formula |
| Imports | methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomicRanges, rtracklayer, parallel, betareg, lokern, Formula, globaltest |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | RRBSdata |
| Imports Me | M3D |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | BiSeq_1.6.0.tar.gz |
| Windows Binary | BiSeq_1.6.0.zip |
| Mac OS X 10.6 (Snow Leopard) | BiSeq_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | BiSeq_1.6.0.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
- 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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