Package: STAN
Version: 2.24.0
Date: 2021-11-21
Title: The Genomic STate ANnotation Package
Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch
Maintainer: Rafael Campos-Martin <campos@mpipz.mpg.de>
Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb,
        Gviz, Rsolnp
Depends: R (>= 3.5.0), methods, poilog, parallel
VignetteBuilder: knitr
Suggests: BiocStyle, gplots, knitr
Description: 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).
License: GPL (>= 2)
biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing,
        ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology
LazyLoad: yes
Packaged: 2022-04-27 01:30:21 UTC; biocbuild
RoxygenNote: 6.0.1
git_url: https://git.bioconductor.org/packages/STAN
git_branch: RELEASE_3_15
git_last_commit: bbd31d4
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-26
NeedsCompilation: yes
Built: R 4.2.0; x86_64-w64-mingw32; 2022-04-27 10:10:11 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
