License Tests ChIPSeq Pipeline Tests
These pipelines use qsub
and pure bash
cpu level parallelism.
Please have a look at the updated snakemake pipeline chipseq-smk-pipeline.
Scalable and reproducible technical pipelines for ChIP-Seq and RNA-Seq processing.
Parallel execution is supported with zero configuration on Portable Batch System (qsub
) and local machines.
Reproducibility is guaranteed by automated testing of all the steps in Docker using Continuous Integration.
ChIP-Seq pipeline was used for Epigenetic changes in aging human monocytes ChIP-Seq data analysis.
pipeline_chipseq.py
- Pipeline for batch ChIP-Seq processing, including QC, alignment, peak callingpipeline_tf.py
- Pipeline for batch Transcription Factor ChIP-Seq processingpipeline_rnaseq.py
- Pipeline for batch RNA-Seq processing, including QC, alignment, quantification
Follow these instructions to launch ChIP-Seq pipeline:
- Configure environment, see Requirements section
- Place all the
.fastq
files to a single<FASTQ_FOLDER>
- Create
<INDEXES>
folder to store all the indexes required - Launch the pipeline with desired
<genome>
, e.g.mm9
orhg19
python3 pipeline_chipseq.py <FASTQ_FOLDER> <INDEXES> <genome>
Follow these instructions to launch RNA-Seq pipeline:
- Configure environment, see Requirements section
- Place all the
.fastq
files to a single<FASTQ_FOLDER>
- Create
<INDEXES>
folder to store all the indexes required - Launch the pipeline with desired
<genome>
, e.g.mm9
orhg19
python3 pipeline_rnaseq.py <FASTQ_FOLDER> <INDEXES> <genome>
- Ensure you have Python 3 installed as default interpreter
- Add the following to
~/.bashrc
(Linux) or~/.bash_profile
(MacOS):
# Configure project path
export WASHU_ROOT="<PATH_TO_REPOSITORY>"
# Configure correct python code execution
export PYTHONPATH="$WASHU_ROOT:$PYTHONPATH"
# Configure local machine parallelism
export WASHU_PARALLELISM=8
- Install required tools using Conda
conda install --channel bioconda samtools bedtools bowtie bowtie2 fastqc multiqc sra-tools macs2 sicer \
ucsc-bedgraphtobigwig ucsc-bedclip ucsc-bigwigaverageoverbed \
star rseg
For more details see docker/biolabs/washu/Dockerfile
.
- Download Picard tools:
curl --location https://github.com/broadinstitute/picard/releases/download/2.10.7/picard.jar \
--output ~/picard.jar
- Download and extract Phantompeakqualtools:
curl --location https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/phantompeakqualtools/ccQualityControl.v.1.1.tar.gz \
--output ~/phantompeakqualtools.tar.gz
tar xvf ~/phantompeakqualtools.tar.gz
- Download SPAN:
curl --location https://download.jetbrains.com/biolabs/span/span-1.1.5628.jar \
--output ~/span.jar
/bed
- BED files manipulations - intersection, ChromHMM enrichment, closes gene, etc./docker
- Docker configuration files with tools and test data. See Tests section./parallel
- Scripts for parallel execution of Portable Batch System (qsub
) or on local machine.
Parallelism level on local machine can be configured via WASHU_PARALLELISM environment variable./scripts
- QC, Visualization, BAM conversions, Reads In Peaks, etc./test
- Tests for pipelines.
Explore preconfigured Continuous Integration configurations on TeamCity:
Fetch Docker image biolabs/washu
with all the necessary tools for pipeline and test data.
docker pull biolabs/washu
Launch tests.
# Change working directory
cd <project_path>
# General tests
docker run -v $(pwd):/washu -t -i biolabs/washu /bin/bash -c \
"source activate py3.5 && cd /washu && bash test.sh"
# ChIP-Seq Pipeline tests
docker run -v $(pwd):/washu -t -m 2G -e JAVA_OPTIONS="-Xmx1G" -i biolabs/washu /bin/bash -c \
"source activate py3.5 && cd /washu && bash test_pipeline_chipseq.sh"
Explore the results of ChIP-Seq pipeline in out
folder after executing these tests.
Bedtools, Bowtie, Bowtie2, FastQC, MACS2, MANorm, MultiQC, Phantompeakqualtools, Picardtools, RSeg, Samtools, SICER, SPAN