This ChIP-Seq pipeline is based off the ENCODE (phase-3) transcription factor and histone ChIP-seq pipeline specifications (by Anshul Kundaje) in this google doc.

• Portability: The pipeline run can be performed across different cloud platforms such as Google, AWS and DNAnexus, as well as on cluster engines such as SLURM, SGE and PBS.
• User-friendly HTML report: In addition to the standard outputs, the pipeline generates an HTML report that consists of a tabular representation of quality metrics including alignment/peak statistics and FRiP along with many useful plots (IDR/cross-correlation measures). An example of the HTML report. The json file used in generating this report.
• Supported genomes: Pipeline needs genome specific data such as aligner indices, chromosome sizes file and blacklist. We provide a genome database downloader/builder for hg38, hg19, mm10, mm9. You can also use this builder to build genome database from FASTA for your custom genome.

1. Git clone this pipeline.

   IMPORTANT: use ~/chip-seq-pipeline2/chip.wdl as [WDL] in Caper's documentation.

   $ cd
   $ git clone https://github.com/ENCODE-DCC/chip-seq-pipeline2

2. Install pipeline's Conda environment if you want to use Conda instead of Docker/Singularity. Conda is recommneded on local computer and HPCs (e.g. Stanford Sherlock/SCG). Use

   IMPORTANT: use encode-chip-seq-pipeline as [PIPELINE_CONDA_ENV] in Caper's documentation.

3. Skip this step if you have installed pipeline's Conda environment. Caper is already included in the Conda environment. Install Caper. Caper is a python wrapper for Cromwell.

   IMPORTANT: Make sure that you have python3(>= 3.6.0) installed on your system.

   $ pip install caper  # use pip3 if it doesn't work

4. Follow Caper's README carefully. Find an instruction for your platform.

   IMPORTANT: Configure your Caper configuration file ~/.caper/default.conf correctly for your platform.

Use https://storage.googleapis.com/encode-pipeline-test-samples/encode-chip-seq-pipeline/ENCSR000DYI_subsampled_chr19_only.json as [INPUT_JSON] in Caper's documentation.

IMPORTANT: DO NOT BLINDLY USE A TEMPLATE/EXAMPLE INPUT JSON. READ THROUGH THE FOLLOWING GUIDE TO MAKE A CORRECT INPUT JSON FILE.

An input JSON file specifies all the input parameters and files that are necessary for successfully running this pipeline. This includes a specification of the path to the genome reference files and the raw data fastq file. Please make sure to specify absolute paths rather than relative paths in your input JSON files.

1. Input JSON file specification (short)
2. Input JSON file specification (long)

You can also run this pipeline on DNAnexus without using Caper or Cromwell. There are two ways to build a workflow on DNAnexus based on our WDL.

1. dxWDL CLI
2. DNAnexus Web UI

Install Croo. You can skip this installation if you have installed pipeline's Conda environment and activated it. Make sure that you have python3(> 3.4.1) installed on your system. Find a metadata.json on Caper's output directory.

$ pip install croo
$ croo [METADATA_JSON_FILE]

Install qc2tsv. Make sure that you have python3(> 3.4.1) installed on your system.

Once you have organized output with Croo, you will be able to find pipeline's final output file qc/qc.json which has all QC metrics in it. Simply feed qc2tsv with multiple qc.json files. It can take various URIs like local path, gs:// and s3://.

$ pip install qc2tsv
$ qc2tsv /sample1/qc.json gs://sample2/qc.json s3://sample3/qc.json ... > spreadsheet.tsv

QC metrics for each experiment (qc.json) will be split into multiple rows (1 for overall experiment + 1 for each bio replicate) in a spreadsheet.