wgs_wdl_upenn
Introduction
Step 1: Set up the Amazon Web Services (AWS) environment
1.1 Create AWS account
1.2 Configure your computing environment and login to AWS
1.3 Setup S3 bucket (simple storage solution for AWS) for hosting sequencing data
1.4 Install AWS command line software for accessing S3 bucket via command line interface
1.5 Install StarCluster for AWS instance provisioning (optional)
Step 2: Create your tracking database instance
Option 1: Setup sample tracking database with Public AMI (recommended)
Option 2: Setup sample tracking database with Docker
Step 3: Configure your project information in the tracking database
3.1 List all projects in the tracking database
3.2 Create the project in the tracking database
Step 4: Upload sequencing data to your S3 bucket
Step 5: Configure your samples information in the tracking database
5.1 Input the sample information to the tracking database
5.2 Populate the tracking database with the S3 paths for the samples to be processed
5.3 Populate the tracking database with the designated result folder for each sample to be processed
5.4 Input PCR protocol information into the tracking database
5.5 Add the capture kit information (WES sample only) into the tracking database
5.6 Generate an ID for the capture kit information (WES sample only)
Step 6: Submit a job to process one whole genome (WGS) / whole exome (WES) sample
6.1 Update vcpa-pipeline bitbucket contents
6.2 Choose which workflow to use
6.3 Enter your AWS credentials into the workflow script
6.4 Launch Amazon EC2 Spot Instances via starcluster
Step 7: Review quality metrics of processed data
Step 8: Generating Project-level VCF via joint genotyping
Optional: Change software versions and dependencies of the VCPA workflow
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3.1 List all projects in the tracking database
3.1 List all projects in the tracking database
Users can list all projects by typing "
http://IP/v1/projects
" in any browser, where IP is the public IP address of the tracking database instance.
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