compEpiTools statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.

Subscribe
info

Citations per year

Citations chart
info

Popular tool citations

chevron_left Data integration chevron_right
Popular tools chart
info

Tool usage distribution map

Tool usage distribution map
info

Associated diseases

Associated diseases

compEpiTools specifications

Information


Unique identifier OMICS_15432
Name compEpiTools
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Epigenomics data (ChiP-seq, DNAse-seq, RNA-seq)
Output data Integrative heatmaps, reads counting, signal enrichment, annotation, RNAPII Stalling index, GO / simplify-GO, IncRNAs, Enhancers, Enhancers-TFs
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.14.1
Stability Stable
Requirements
AnnotationDbi, methods, gplots, parallel, BiocGenerics, IRanges, GenomicRanges, Rsamtools, S4Vectors, GenomeInfoDb, Biostrings, rtracklayer, grDevices, XVector, GenomicFeatures, knitr, BSgenome.Mmusculus.UCSC.mm9, GO.db, R(>=3.1.1), org.Mm.eg.db, topGO, TxDb.Mmusculus.UCSC.mm9.knownGene, methylPipe
Maintained Yes

Download


Versioning


Add your version

Documentation


Maintainer


  • person_outline Mattia Pelizzola <>

Publication for compEpiTools

compEpiTools in pipeline

2018
PMCID: 5819258
PMID: 29458328
DOI: 10.1186/s12861-018-0163-7

[…] r creating custom coverage files with the grcoverageinbins function (as object we used the promoter file (32,840 regions) converting it to granges, nnorm = true, snorm = false, nbins = 20) from the compepitools package [] in bioconductor []. for each of the cell types, we subsequently created a combined matrix of histone marks, rnapii, rna-seq coverage (normalized by library size in each cell […]


To access a full list of citations, you will need to upgrade to our premium service.

compEpiTools in publications

 (6)
PMCID: 5819258
PMID: 29458328
DOI: 10.1186/s12861-018-0163-7

[…] ending site (tes) of the gene. using the grcoverage function (as objects we used the previously mentioned promoter and gene body files (22,179 regions), nnorm = false, snorm = true) from the compepitools package [] in bioconductor [] we computed the read coverage at those regions for each cell type and gene in our clusters., we calculated pausing indices for the genes categories: […]

PMCID: 5833453
PMID: 29416007
DOI: 10.1038/s41419-017-0234-x

[…] to fisher’s exact test method., promoters were defined as −1 to 1 kb of the transcription start sites. promoter classes based on cpg density (hcp, icp, and lcp) were obtained by bioconductor package compepitools v1.6.4 using r v3.3.0. hcp, icp, and lcp were defined as previously published. genomic locations of cpg islands and interspersed repeat families (lines, sines, ltrs) were downloaded […]

PMCID: 5665917
PMID: 29093577
DOI: 10.1038/s41598-017-14942-4

[…] biomart was taken as the representative for each gene. the genes filtered against ensembl protein-coding genes were used in further analyses. in the histone modification comparative analysis, the compepitools package was used to normalize the numbers and lengths of the reads, and count the aligned reads in the promoter regions. chip-seq datasets for h3k4me3, h3k27me3, h3k36me3, ctcf, and dna […]

PMCID: 5310924
PMID: 27443262
DOI: 10.1038/leu.2016.182

[…] as reference. normalized reads count within a genomic region was determined as the number of reads per million of library aligned reads (r.p.m.), that were subtracted by the input normalized reads, ‘compepitools' bioconductor r package. peak read density (reads per million of reads per base pair) for a particular region was determined as the ratio between the normalized reads count […]

PMCID: 5161449
PMID: 27855777
DOI: 10.7554/eLife.20235.034

[…] was plotted using ggplot2 (). the correlation between samples was calculated using pearson’s correlation., functional annotation for genomic regions were obtained using the r/bioconductor package compepitools function grannotatesimple ()., pileup bedgraph files normalised to reads per million as generated by macs2 were used to plot chip-seq intensity heatmaps. bigwig files for chip and input […]


To access a full list of publications, you will need to upgrade to our premium service.

compEpiTools funding source(s)
This work was supported by the European Community’s Seventh Framework (FP7/2007-2013) project RADIANT [grant number 305626]; a grant from the Italian Association for Cancer Research (AIRC); and by the Mary K. Chapman Foundation and NIH Roadmap Epigenomics project [grant number U01ES017166 and 1U01MH105985].

compEpiTools reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review compEpiTools