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|
|Date of publication||None|
|Maintainer||Benedikt Zacher <firstname.lastname@example.org>|
|License||GPL (>= 2)|
bdHMM: Create a bdHMM object
bdHMM-class: This class is a generic container for bidirectional Hidden...
binarizeData: Binarize Sequencing data with the default ChromHMM...
c2optimize: Optimize transitions
call_dpoilog: Calculate density of the Poisson-Log-Normal distribution.
data2Gviz: Convert data for plotting with Gviz
DimNames: Get dimNames of a (bd)HMM
DirScore: Get directionality score of a bdHMM
Emission: Get Emission functions of a (bd)HMM
EmissionParams: Get Emission parameters of a (bd)HMM.
example: The data for the bdHMM example in the vignette and examples...
fitHMM: Fit a Hidden Markov Model
flags: Pre-computed flag sequence for the 'example' data.
getAvgSignal: Compute average signal in state segmentation
getLogLik: Calculate log likelihood state distribution.
getPosterior: Calculate posterior state distribution.
getSizeFactors: Compute size factors
getViterbi: Calculate the most likely state path
HMM: Create a HMM object
HMM-class: This class is a generic container for Hidden Markov Models.
HMMEmission: Create a HMMEmission object
HMMEmission-class: This class is a generic container for different emission...
initBdHMM: Initialization of bidirectional hidden Markov models
initHMM: Initialization of hidden Markov models
InitProb: Get initial state probabilities of a (bd)HMM
LogLik: Get stateNames of a (bd)HMM
observations: Observation sequence for the 'example' data.
pilot.hg19: Genomic positions of processed signal for the Roadmap...
runningMean: Smooth data with running mean
STAN-package: The genomic STate ANnotation package
StateNames: Get stateNames of a (bd)HMM
sub-bdHMM-ANY-ANY-method: This function subsets a bdHMM object. Rows are interpreted as...
sub-HMM-ANY-ANY-method: This function subsets an HMM object. Rows are interpreted as...
trainRegions: Training regions for the Roadmap Epigenomics data set. Three...
Transitions: Get transitions of a (bd)HMM
ucscGenes: UCSC gene annotation for the Roadmap Epigenomics data set.
viterbi2GRanges: Convert the viterbi path to a GRanges object
viterbi2Gviz: Convert state segmentation for plotting with Gviz
yeastTF_databychrom_ex: Processed ChIP-on-chip data for yeast TF example
yeastTF_SGDGenes: SGD annotation for the yeast TF example