STAN

DOI: 10.18129/B9.bioc.STAN    

The Genomic STate ANnotation Package

Bioconductor version: Release (3.13)

Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP).

Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch

Maintainer: Rafael Campos-Martin <campos at mpipz.mpg.de>

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("STAN")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("STAN")

 

PDF	R Script	The genomic STate ANnotation package
PDF		Reference Manual

Details

biocViews	ChIPSeq, ChipOnChip, GenomeAnnotation, HiddenMarkovModel, ImmunoOncology, Microarray, RNASeq, Sequencing, Software, Transcription
Version	2.20.0
In Bioconductor since	BioC 3.0 (R-3.1) (7 years)
License	GPL (>= 2)
Depends	methods, poilog, parallel
Imports	GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp
LinkingTo
Suggests	BiocStyle, gplots, knitr
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
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Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	STAN_2.20.0.tar.gz
Windows Binary	STAN_2.20.0.zip
macOS 10.13 (High Sierra)	STAN_2.20.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/STAN
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/STAN
Package Short Url	https://bioconductor.org/packages/STAN/
Package Downloads Report	Download Stats