/wes_germline_gatk

GATK workflow for WES on germline samples

Primary LanguageWDL

Whole Exome Sequencing Pipeline - Germline Analysis

Using the Scatter-Gather technique provided by Broad Institute

This repository contains an implementation of the Whole Exome Sequencing (WES) pipeline based on GATK best practices workflows using WDL scripts (Workflow Description Language).

  1. Optimized to run samples in parallel
  2. The Docker version allow users to chose the number of samples to run in parallel based on available resources (threads and memory; available upon request)
  3. WDL and JSON made easy by removing "unecessary statements"
  4. Single line command to run the whole pipeline (QC, trimming, mapping, markduplicates, base recalibration, variant calling, annotation)

The diagram below summarizes the germline and somatic analysis (tumor only or tumor/normal).

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The pipelines consist of WDL scripts that run the analysis in addition to shell scripts that act as intermediate steps. The pipelines were tested successfully based on the following dependencies:

Java 8 
Cromwell v36 
FastQC v0.11.5 
BWA 0.7.17-r1194-dirty 
Cutadapt 1.18 
Samtools 1.8 – should be installed in the PATH 
GATK-4.0.11.0 
Tabix 0.2.5

Also, you should download the human reference genome and index it using BWA. In addition, some databases should be downloaded too:

dbsnp
phase1snps 
Mills_and_1000G_gold_standard 
HapMap 
Omni 
Axiom

You can download the reference genome and its index, the intervals and the databases listed above from resources directory provided by Broad Institute from the following link:

https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0/?pli=1

Each one of the WDL and shell scripts can be invoked independently by providing the project directory as argument.

the projectDir should have the following structure:

1- A directory named "fastq" which contains FASTQ files. FASTQ files should have the following naming style:

    sampleName_R1.fastq.gz and sampleName_R2.fastq.gz

2- A directory named "lists" containing three files:

    1) fastq_list.txt: A tab separated file listing samples in the following format:

    sampleName1    sampleName1_R1.fastq.gz    sampleName1_R2.fastq.gz
    sampleName2    sampleName2_R1.fastq.gz    sampleName2_R2.fastq.gz

    2) intervals.txt Contains a list of full path of all intervals in BED format:

    path/to/intervals/scattered_calling_intervals/temp_0001_of_50/scattered.interval_list
    path/to/intervals/scattered_calling_intervals/temp_0002_of_50/scattered.interval_list
    path/to/intervals/scattered_calling_intervals/temp_0003_of_50/scattered.interval_list
    path/to/intervals/scattered_calling_intervals/temp_0004_of_50/scattered.interval_list
    path/to/intervals/scattered_calling_intervals/temp_0005_of_50/scattered.interval_list

    3) adapters.txt Contains adapters to be trimmed:

    The first line should contain first read adapter (forward) and the second
    line should contain second read adapter (reverse):

    CTGTCTCTTGATCACA
    TGTGATCAAGAGACAG

To run the pipeline, you must specify full paths for each tool and database in the JSON file. Once done, you can invoke the pipeline using the following command:

/path/to/run.sh /path/to/project/directory /path/to/cromwell.jar

To use the Docker image (available upon request), you must prepare the ‘project directory’ as mentioned above and invoke the Docker image using the following command:

docker run -it -v /path/to/project/directory/:/data/ pklab/wes_pipelines

We can invoke each WDL and shell scripts separately.
If we use the Docker, all you need is to use fastq_list.txt, intervals.txt and adapters.txt from the lists directory.