biotools:Epigenomics_Workflow_on_Galaxy_and_Jupyter

Epigenomics Workflow on Galaxy and Jupyter, RRID:SCR_017544

Epigenomics Workflow on Galaxy and Jupyter

Over the last decade, extensive epigenomics data is being generated. Data analysis may be challenging, and usually requires bioinformatics knowledge. Here, we present a 2-step full pipeline for combined ChIP-Seq and RNA-Seq data analysis.

Contents

Two Docker images were prepared to run the analysis in a coordinated way.

• First, a container running Galaxy will run the bulk analysis of ChIP-Seq and RNA-Seq data. The workflows are designed to use local read data or from SRA and to 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
• The second container running Jupyter will use the files generated by Galaxy and finish data analysis. Two notebooks are provided with a preview of results from each command cell. They run:
  • ChIP-Seq:
    • Comparison of ChIP-Seq samples with MAnorm
    • Peak annotation
    • Metagene/heatmap plots of read distribution on genes
  • Complete dataset:
    • Functional annotation of results
    • Combination of ChIP-Seq and RNA-Seq results
    • Generation of tables and figures

These containers were prepared during the study of the epigenome of Brassica rapa to analyze ChIP-Seq (broad histone mark H3K27me3) and RNA-Seq data. For reproducibility, steps to repeat the original analysis described in our paper are included (instructions on container descriptions).

• 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

Usage

Docker commands

To use the images, Docker needs to be installed in the system (link to documentation). Basic docker commands are:

• docker images: show all downloaded/built images.
• docker run: download (if needed) and run a docker image. A container is launched as an instance of that image. Multiple options are available to handle the interaction between local system and container.
• docker ps -a: list all containers.
• docker stop <my_container>: stop running container.
• docker start <my_container>: start a stopped container.
• docker exec -it <my_container> bash: access a container from a terminal.
• docker rm <my_container>: delete a stopped container.
• docker rmi <image_id>: delete a docker image.

Galaxy in Docker

The epigenomics Galaxy image is based on bgruening/galaxy-stable (link). The key additions are:

• The default user has administrative privileges
• Tools to run the epigenomics analysis are pre-installed
• Workflows are provided to run ChIP-Seq and RNA-Seq data analysis
• Accessory files to run Brassica rapa data analysis

The workflows are designed to start from .fastq files or two-column text files indicating SRA accession numbers on the first column and file names on the second column. The default workflows use paired-end reads for ChIP-Seq data and single-end reads for RNA-Seq data. They can be customized to modify this behavior.

Quick start

Initialize the container.

## prepare local directory to contain galaxy
local_path=~/DockerFolders/run_v1  # name for the export directory
mkdir -p "${local_path}"

## run the container
cont_name=run1
port=8080

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

## after download, open web browser
xdg-open http://localhost:$port/

Run test (may take 10-15 min)

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

Or create the filesystem tree for the Galaxy export tool before running any workflows.

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

Jupyter in Docker

The epigenomics Jupyter image is based on jupyter/datascience-notebook (link). It contains:

• Kernels
  • Python
  • R
  • bash
  • Julia
• Software
  • R and Python libraries for data analysis
  • MAnorm
  • ngs.plot
  • Miniconda 2 and 3
• Notebooks
  • Two bash notebooks for differential binding analysis and ChIP-Seq data plotting
  • An R notebook for the annotation and visualization of results

Quick start

Running this container for the first time on a local machine automatically opens Jupyter in a web browser. In Jupyter, the export folder is ~/work. To continue with the data analysis from Galaxy, ~/work is mapped to the analysis folder where the workflows have exported the results.

local_path=~/DockerFolders/run_v1
analysis_dir="${local_path}"/analysis
jup_name=nb1
jup_port=8888

docker run \
-p $jup_port:8888 \
--name $jup_name \
-v "${analysis_dir}":/home/jovyan/work \
mpaya/epigenomics_jupyter:2.5

After running the three notebooks in consecutive order, results will be available at $analysis_dir/jupyter-res.

Output

Results are stored on the folder first created when running Galaxy, in this example ~/DockerFolders/run_v1/analysis. In summary, results consist of:

• Galaxy
  • Basic read statistics (MultiQC)
  • Alignment files (.bam)
  • Track files (.bigwig)
  • ChIP-Seq peaks (.bed)
  • RNA-Seq results (counts and DEGs)
• Jupyter
  • Differentially bound peaks (table from MAnorm)
  • Annotated peaks
  • Metagene plots and heatmaps
  • Other figures and tables