recipe bioconductor-stan

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).



GPL (>= 2)




biotools: stan

package bioconductor-stan

(downloads) docker_bioconductor-stan


2.10.0-0, 2.8.0-0, 2.6.0-0

Required By


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-stan

and update with:

conda update bioconductor-stan

or use the docker container:

docker pull<tag>

(see bioconductor-stan/tags for valid values for <tag>)