STAN

DOI: 10.18129/B9.bioc.STAN  

This package is deprecated. It will probably be removed from Bioconductor. Please refer to the package end-of-life guidelines for more information.

This package is for version 3.17 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see STAN.

The Genomic STate ANnotation Package

Bioconductor version: Release (3.17)

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 "4.3") and enter:

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

BiocManager::install("STAN")

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

Documentation

PDF		Reference Manual

Details

biocViews	ChIPSeq, ChipOnChip, GenomeAnnotation, HiddenMarkovModel, ImmunoOncology, Microarray, RNASeq, Sequencing, Software, Transcription
Version	2.28.0
In Bioconductor since	BioC 3.0 (R-3.1) (9 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
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Build Report

Package Archives

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

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
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
Old Source Packages for BioC 3.17	Source Archive