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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GenomicFiles")
In most cases, you don't need to download the package archive at all.
GenomicFiles
Distributed computing by file or by range
Bioconductor version: 3.0
This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation.
Author: Valerie Obenchain, Michael Love, Martin Morgan
Maintainer: Bioconductor Package Maintainer <maintainer at bioconductor.org>
Citation (from within R,
enter citation("GenomicFiles")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GenomicFiles")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GenomicFiles")
| R Script | Introduction to GenomicFiles | |
| Reference Manual | ||
| Text | NEWS | |
| Video | Reading from a BAM file - Part 2 |
Details
| biocViews | DataImport, Infrastructure, Sequencing, Software |
| Version | 1.2.1 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | Artistic-2.0 |
| Depends | R (>= 3.1.0), methods, BiocGenerics(>= 0.11.2), Rsamtools(>= 1.17.29), GenomicRanges(>= 1.17.16), rtracklayer(>= 1.25.3), BiocParallel(>= 0.7.0) |
| Imports | GenomicAlignments, IRanges |
| LinkingTo | |
| Suggests | BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14 |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | derfinder |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GenomicFiles_1.2.1.tar.gz |
| Windows Binary | GenomicFiles_1.2.1.zip |
| Mac OS X 10.6 (Snow Leopard) | GenomicFiles_1.2.1.tgz |
| Mac OS X 10.9 (Mavericks) | GenomicFiles_1.2.1.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
- 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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