To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SGSeq")
In most cases, you don't need to download the package archive at all.
SGSeq
Prediction, quantification and visualization of alternative transcript events from RNA-seq data
Bioconductor version: 3.0
RNA-seq data are informative for the analysis of known and novel transcript isoforms. While the short length of RNA-seq reads limits the ability to predict and quantify full-length transcripts, short read data are well suited for the analysis of individual alternative transcripts events (e.g. inclusion or skipping of a cassette exon). The SGSeq package enables the prediction, quantification and visualization of alternative transcript events from BAM files.
Author: Leonard Goldstein
Maintainer: Leonard Goldstein <goldstel at gene.com>
Citation (from within R,
enter citation("SGSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SGSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SGSeq")
| R Script | SGSeq | |
| Reference Manual |
Details
| biocViews | AlternativeSplicing, RNASeq, Software, Transcription |
| Version | 1.0.6 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | Artistic-2.0 |
| Depends | BiocParallel, GenomicRanges, IRanges, methods |
| Imports | AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, igraph, parallel, Rsamtools, rtracklayer, S4Vectors(>= 0.2.3) |
| LinkingTo | |
| Suggests | BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene |
| 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 | SGSeq_1.0.6.tar.gz |
| Windows Binary | SGSeq_1.0.6.zip |
| Mac OS X 10.6 (Snow Leopard) | SGSeq_1.0.6.tgz |
| Mac OS X 10.9 (Mavericks) | SGSeq_1.0.6.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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