To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("STAN")
In most cases, you don't need to download the package archive at all.
STAN
STrand-specific ANnotation of genomic data
Bioconductor version: 3.0
STAN (STrand-specic ANnotation of genomic data) implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription, DNA replication, recombination or DNA repair by integrating genomic data. bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP).
Author: Benedikt Zacher, Julien Gagneur, Achim Tresch
Maintainer: Benedikt Zacher <zacher at lmb.uni-muenchen.de>
Citation (from within R,
enter citation("STAN")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("STAN")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("STAN")
| R Script | STrand-specific ANnotation of genomic data | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GenomeAnnotation, HiddenMarkovModel, Microarray, Sequencing, Software |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL (>= 2) |
| Depends | Rsolnp, methods |
| Imports | |
| LinkingTo | |
| Suggests | BiocStyle, Gviz, GenomicRanges, IRanges, gplots, knitr |
| 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 | STAN_1.0.0.tar.gz |
| Windows Binary | STAN_1.0.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | STAN_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | STAN_1.0.0.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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