recipe bioconductor-esatac

An Easy-to-use Systematic pipeline for ATACseq data analysis



GPL-3 | file LICENSE



This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly.

package bioconductor-esatac

(downloads) docker_bioconductor-esatac



depends bioconductor-annotationdbi:


depends bioconductor-biocgenerics:


depends bioconductor-biostrings:


depends bioconductor-bsgenome:


depends bioconductor-chipseeker:


depends bioconductor-clusterprofiler:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicalignments:


depends bioconductor-genomicfeatures:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-jaspar2018:


depends bioconductor-motifmatchr:


depends bioconductor-pipeframe:


depends bioconductor-rbowtie2:


depends bioconductor-rsamtools:


depends bioconductor-rtracklayer:


depends bioconductor-s4vectors:


depends bioconductor-shortread:


depends bioconductor-tfbstools:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-biocmanager:

depends r-corrplot:

depends r-digest:

depends r-ggplot2:

depends r-igraph:

depends r-knitr:

depends r-magrittr:

depends r-r.utils:

depends r-rcpp:


depends r-rjava:

depends r-rmarkdown:

depends r-venndiagram:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-esatac

and update with::

   mamba update bioconductor-esatac

To create a new environment, run:

mamba create --name myenvname bioconductor-esatac

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-esatac/tags`_ for valid values for ``<tag>``)

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