trainRegions


Training regions for the Roadmap Epigenomics data set. Three ENCODE pilot regions with data from two cell lines.


data

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).

Benedikt Zacher

STAN

The genomic STate ANnotation package

trainRegions function

Training regions for the Roadmap Epigenomics data set. Three ENCODE pilot regions with data from two cell lines.

trainRegions: Training regions for the Roadmap Epigenomics data set. Three ENCODE pilot regions with data from two cell lines.

Description

Arguments