Name Mode Size
RcppExports.R 100644 4 kb
addOffsets.R 100644 1 kb
base-EM.R 100644 16 kb
base-checkers.R 100644 7 kb
base-core.R 100644 11 kb
base-loglikelihood.R 100644 7 kb
base-optim.R 100644 18 kb
base-utils.R 100644 19 kb
base-wrappers.R 100644 11 kb
base-writers.R 100644 6 kb
callPaterns.R 100644 5 kb
callPeaks.R 100644 3 kb
cleanCounts.R 100644 4 kb
controlEM.R 100644 7 kb
data-helas3.R 100644 2 kb
epigraHMM-package.R 100644 0 kb
epigraHMM.R 100644 3 kb
epigraHMMDataSetFromBam.R 100644 8 kb
epigraHMMDataSetFromMatrix.R 100644 4 kb
estimateTransitionProb.R 100644 1 kb
info.R 100644 2 kb
initializer.R 100644 2 kb
normalizeCounts.R 100644 3 kb
plotCounts.R 100644 4 kb
plotPatterns.R 100644 5 kb
segmentGenome.R 100644 1 kb
utils-data-table.R 100644 1 kb
utils-pipe.R 100644 0 kb
<!-- is generated from README.Rmd. Please edit that file --> # The `epigraHMM` package <!-- badges: start --> <!-- badges: end --> [**epigraHMM**]( is a Bioconductor package that provides set of tools to flexibly analyze data from a wide range of high-throughput epigenomic assays (ChIP-seq, ATAC-seq, DNase-seq, etc.) in an end-to-end pipeline. The official page of `epigraHMM` is the Bioconductor landing page of its [release]( (or [devel]( version. This [github page]( is simply used for issue tracking and development. ## Background A fundamental task in the analysis of data resulting from epigenomic sequencing assays is the detection of genomic regions with significant or differential sequencing read enrichment. `epigraHMM` provides set of tools to flexibly analyze data from a wide range of high-throughput epigenomic assays (ChIP-seq, ATAC-seq, DNase-seq, etc.) in an end-to-end pipeline. It includes functionalities for data pre-processing, normalization, consensus and differential peak detection, as well as data visualization. In differential analyses, `epigraHMM` can detect differential peaks across either multiple conditions of a single epigenomic mark (differential peak calling) or across multiple epigenomic marks from a single condition (genomic segmentation). The data pre-processing steps are heavily integrated with other Bioconductor packages and allow the user to transform sequencing/alignment files into count matrices that are suitable for the final analysis of the data. The current implementation is optimized for genome-wide analyses of epigenomic data and is efficient for the analysis under multi-sample multiple-condition settings, as well as consensus peak calling in multi-sample single-condition settings. `epigraHMM` uses two modified versions of hidden Markov models (HMM) that are robust to the diversity of peak profiles found in epigenomic data and are particularly useful for epigenomic marks that exhibit short and broad peaks. Analyses can be adjusted for possible technical artifacts present in the data and for input control experiments, if available. Results from the peak calling algorithms can be assessed using some of the available tools for visualization that allow the inspection of detected peaks and read counts. ## Installation You can install the official release version of `epigraHMM` from [Bioconductor]( with: ``` r install.packages("BiocManager") BiocManager::install("epigraHMM") ``` A [vignette]( of `epigraHMM` with an overview of the package and its functionalities is available. Users can build the vignette during the installation process with the option `build_vignettes = TRUE` in the command above.