To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("casper")
In most cases, you don't need to download the package archive at all.
casper
Characterization of Alternative Splicing based on Paired-End Reads
Bioconductor version: 3.0
Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision.
Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena
Maintainer: David Rossell <rosselldavid at gmail.com>
Citation (from within R,
enter citation("casper")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("casper")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("casper")
| DesignRNASeq.pdf | ||
| R Script | Manual for the casper library | |
| Reference Manual |
Details
| biocViews | DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Transcription |
| Version | 2.0.2 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>=2) |
| Depends | R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges |
| Imports | BiocGenerics, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors, sqldf, survival, VGAM |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | parallel |
| 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 | casper_2.0.2.tar.gz |
| Windows Binary | casper_2.0.2.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | casper_2.0.2.tgz |
| Mac OS X 10.9 (Mavericks) | casper_2.0.2.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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