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|
|Bioconductor views||ChIPSeq ChipOnChip GenomeAnnotation HiddenMarkovModel ImmunoOncology Microarray RNASeq Sequencing Transcription|
|Maintainer||Rafael Campos-Martin <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on Bioconductor|
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