Package: crupR 1.5.0

Persia Akbari Omgba

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

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.

Authors:Persia Akbari Omgba [cre], Verena Laupert [aut], Martin Vingron [aut]

crupR_1.5.0.tar.gz
crupR_1.5.0.zip(r-4.7)crupR_1.5.0.zip(r-4.6)crupR_1.5.0.zip(r-4.5)
crupR_1.5.0.tgz(r-4.6-any)crupR_1.5.0.tgz(r-4.5-any)
crupR_1.5.0.tar.gz(r-4.7-any)crupR_1.5.0.tar.gz(r-4.6-any)
crupR_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
crupR/json (API)

# Install 'crupR' in R:

install.packages('crupR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/akbariomgba/crupr/issues

On BioConductor:crupR-1.5.0(bioc 3.24)crupR-1.4.0(bioc 3.23)

differentialpeakcalling genetarget functionalprediction histonemodification peakdetection

4.18 score 3 scripts 242 downloads 7 exports 96 dependencies

Last updated from:141adc465a. Checks:1 NOTE, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	312
linux-devel-x86_64	OK	684
source / vignettes	OK	849
linux-release-x86_64	OK	690
macos-release-arm64	OK	702
macos-oldrel-arm64	OK	699
windows-devel	OK	508
windows-release	OK	507
windows-oldrel	OK	487
wasm-release	OK	275

Exports:getDynamics getEnhancers getSE getTargets normalize plotSummary saveFiles

Dependencies:abind AnnotationDbi askpass bamsignals BH Biobase BiocBaseUtils BiocGenerics BiocIO BiocParallel Biostrings bit bit64 bitops blob cachem cigarillo cli codetools cpp11 crayon curl DBI DelayedArray dplyr farver fastmap formatR fs futile.logger futile.options generics GenomicAlignments GenomicFeatures GenomicRanges ggplot2 glue gtable httr IRanges isoband jsonlite KEGGREST labeling lambda.r lattice lifecycle magrittr Matrix MatrixGenerics matrixStats memoise mime openssl pillar pkgconfig plyr png preprocessCore R6 randomForest RColorBrewer Rcpp RCurl reshape2 restfulr Rhtslib rjson rlang Rsamtools RSQLite rtracklayer S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray stringi stringr SummarizedExperiment sys tibble tidyselect TxDb.Hsapiens.UCSC.hg19.knownGene TxDb.Hsapiens.UCSC.hg38.knownGene TxDb.Mmusculus.UCSC.mm10.knownGene TxDb.Mmusculus.UCSC.mm9.knownGene utf8 vctrs viridisLite withr XML XVector yaml

crupR Vignette

Rendered fromcrupR-vignette.Rmdusingknitr::rmarkdown

Last update: 2025-07-31
Started: 2022-03-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
crupR: Condition-specific Regulatory Units Prediction	crupR-package crupR
Clusters the active enhancer into condition-specific groups	getDynamics
Predicts the occurence of enhancers using the normalized HMs counts.	getEnhancers
Find enhancer peaks and super enhancers	getSE
Find the target genes of the regulatory, condition-specific clusters	getTargets
Normalization of ChIP-seq counts by input	normalize
plots boxplots of the max. enhancer prediction values of the enhancers in the condition-specific clusters	plotSummary
Function to save the different output objects of each step	saveFiles