DOI: 10.18129/B9.bioc.STAN    

This package is for version 3.7 of Bioconductor; for the stable, up-to-date release version, see STAN.

The Genomic STate ANnotation Package

Bioconductor version: 3.7

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: Rafael Campos-Martin <campos at>

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PDF R Script The genomic STate ANnotation package
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biocViews ChIPSeq, ChipOnChip, GenomeAnnotation, HiddenMarkovModel, Microarray, RNASeq, Sequencing, Software, Transcription
Version 2.8.0
In Bioconductor since BioC 3.0 (R-3.1) (4 years)
License GPL (>= 2)
Depends methods, poilog, parallel
Imports GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp
Suggests BiocStyle, gplots, knitr
Depends On Me
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