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source("http://bioconductor.org/biocLite.R")
biocLite("Basic4Cseq")
In most cases, you don't need to download the package archive at all.
Basic4Cseq
Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data
Bioconductor version: 3.0
Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.
Author: Carolin Walter
Maintainer: Carolin Walter <carolin.walter at uni-muenster.de>
Citation (from within R,
enter citation("Basic4Cseq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Basic4Cseq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Basic4Cseq")
| R Script | Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data | |
| Reference Manual |
Details
| biocViews | QualityControl, Software, Visualization |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | LGPL-3 |
| Depends | R (>= 3.0.0), Biostrings, ShortRead, caTools, GenomicRanges |
| Imports | methods, RCircos, BSgenome.Ecoli.NCBI.20080805 |
| LinkingTo | |
| Suggests | BSgenome.Hsapiens.UCSC.hg19 |
| 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 | Basic4Cseq_1.2.0.tar.gz |
| Windows Binary | Basic4Cseq_1.2.0.zip |
| Mac OS X 10.6 (Snow Leopard) | Basic4Cseq_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | Basic4Cseq_1.2.0.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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