mpaya/epigenomics_galaxy repository overview

==> Pipeline for Epigenomics data analysis

For more details, visit GitHub⁠

This galaxy instance contains tools and workflows aimed at the initial analysis of epigenomics data, both ChIP-Seq and RNA-Seq, which is continued at the notebooks in mpaya/epigenomics_jupyter⁠. These containers were prepared to document and reproduce the data analysis performed on Brassica rapa from our paper.

• Payá-Milans, M., Poza-Viejo, L., Martín-Uriz, P. S., Lara-Astiaso, D., Wilkinson, M. D., & Crevillén, P. (2019). Genome-wide analysis of the H3K27me3 epigenome and transcriptome in Brassica rapa.⁠ GigaScience, 8(12). doi: 10.1093/gigascience/giz147⁠

⁠Contents

This container with Galaxy will analyze raw ChIP-Seq and RNA-Seq data. The workflows are designed to use user input as fastq files or SRA identifiers, and export results locally. Major steps in these workflows are:

• Trimming with Trimmomatic
• Mapping with Bowtie2
• ChIP-Seq:
  • Alignment filtering and deduplication
  • Generation of BigWig files
  • Peak calling with MACS2 and epic2
• RNA-Seq:
  • Read counting
  • Differential expression analysis with DESeq2

⁠Instructions

This container, based on bgruening/galaxy-stable contains all dependencies to launch a fully running Galaxy instance with full privileges.

⁠Prepare local environment

This container may be run directly and Docker will create the directory where /export points under root user. Some steps require access to write to this folder, so creating it in advance with the local user is recommended.

local_path=~/DockerFolders/run_v1
mkdir -p "${local_path}"

⁠Download and activate the container

The docker-galaxy container will be run on daemon mode, mapping an export directory to a local path where results can be easily accessed, and mapping the web page to a local port where the galaxy instance can be viewed.

# name for container and port for local web browser
cont_name=run1   
port=8080

docker run \
-d \
-v "${local_path}":/export/ \
-p $port:80 \
--name "${cont_name}" \
mpaya/epigenomics_galaxy:2.5

## track download on destination
sudo du -sh $local_path 

After download of ~16GB, galaxy will be available at http://localhost:$port/}. Credentials to log in are:

• Default user: [email protected]
• Password: admin

⁠Test

Test code is provided to prepare the file system to save the outputs of Galaxy workflows, install a test genomic file and run the analysis with the test data. Test data consists of ChIP-Seq and RNA-Seq data from Arabidopsis. It takes ~10 min on 8 cores and uses 400 Mb. If not specified, the default port used is 8080.

bash ${local_path}/galaxy-central/lib/image_data/run_test.sh $port

⁠Reproducible Brassica rapa data analysis

Included is a script that will prepare the environment, install B. rapa chiifu v3.0 genome and conduct the data analysis indicated on the paper above.

bash ${local_path}/galaxy-central/lib/image_data/brassica_data/run_analysis.sh $port

⁠Cleanup

Running Galaxy workflows will generate a large load of intermediate files, such as alignment files. These may be deleted from the Galaxy web interface. In addition, the local folder with exported results may be copied elsewhere and the file system refreshed for a new analysis.

## preparation for next analysis
# copy results to a diferent destination
my_dest="${local_path}"/test_run
mkdir my_dest
cp -r "${local_path}"/analysis $my_dest

# delete current results after inspection
rm -fr "${local_path}"/analysis/*res

# prepare fresh file system 
galaxy_res="${local_path}"/analysis/galaxy-res
mkdir -m 777 -p $galaxy_res/{chipseq1,chipseq2,rnaseq}

⁠Container cleanup

Once finished with the analysis, the container and related files may be deleted.

## full cleanup (with sudo)
rm -fr "${local_path}"

# delete galaxy container
docker stop ${cont_name}
docker rm ${cont_name}
docker rmi mpaya/epigenomics_galaxy:2.5