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crupR

An R package to predict condition-specific enhancers from ChIP-seq data

Bioconductor version: 3.23 · Package version: 1.4.0

An R package that offers a workflow to predict condition-specific enhancers from ChIP-seq data. The prediction of regulatory units is done in four main steps: Step 1 - the normalization of the ChIP-seq counts. Step 2 - the prediction of active enhancers binwise on the whole genome. Step 3 - the condition-specific clustering of the putative active enhancers. Step 4 - the detection of possible target genes of the condition-specific clusters using RNA-seq counts.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("crupR")

Details

MaintainerPersia Akbari Omgba <omgba@molgen.mpg.de>
AuthorPersia Akbari Omgba [cre], Verena Laupert [aut], Martin Vingron [aut]
LicenseGPL-3
URLhttps://github.com/akbariomgba/crupR
Bug Reportshttps://github.com/akbariomgba/crupR/issues
Downloads rank166
Source branchRELEASE_3_23
biocViewsDifferentialPeakCalling, FunctionalPrediction, GeneTarget, HistoneModification, PeakDetection, Software

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Dependencies

Depends: R (>= 4.4.0)

Imports: bamsignals, Rsamtools, GenomicRanges, preprocessCore, randomForest, rtracklayer, Seqinfo, S4Vectors, ggplot2, matrixStats, dplyr, IRanges, GenomicAlignments, GenomicFeatures, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, reshape2, magrittr, stats, utils, grDevices, SummarizedExperiment, BiocParallel, fs, methods

Suggests: GenomeInfoDb, testthat, BiocStyle, knitr, rmarkdown