STAN

The genomic STate ANnotation package

Bioconductor version: Release (3.4)

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, Julien Gagneur, Achim Tresch

Maintainer: Benedikt Zacher <zacher at genzentrum.lmu.de>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://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")

Details

Package Archives

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