STAN: The Genomic STate ANnotation Package
Version 2.6.0

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).

Package details

AuthorBenedikt Zacher, Julia Ertl, Julien Gagneur, Achim Tresch
Bioconductor views ChIPSeq ChipOnChip GenomeAnnotation HiddenMarkovModel Microarray RNASeq Sequencing Transcription
MaintainerRafael Campos-Martin <[email protected]>
LicenseGPL (>= 2)
Version2.6.0
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("STAN")

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STAN documentation built on Nov. 17, 2017, 9 a.m.