NIAGADS
  • VCPA
  • 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 a 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 using Public AMI (recommended)
    • Option 2: Setup sample tracking database using 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 to represent 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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  • Overview of VCPA
  • The figure below outlines the VCPA

Introduction

PreviousVCPANextStep 1: Set up the Amazon Web Services (AWS) environment

Last updated 6 years ago

Overview of VCPA

VCPA consists of two independent but linkable components: pipeline and database. The pipeline implements are coded in Workflow Description Language (WDL) and are fully optimized for the Amazon elastic compute cloud environment. This includes steps for processing raw sequence reads including read alignment and variant calling using GATK. The tracking database allows users to dynamically view the statuses of jobs running and the quality metrics reported by the pipeline. Users can thus monitor the production process and diagnose if any problem arises during the procedure. All quality metrics (>100 collected per processed genome) are stored in the database, thus facilitating users to compare, share and visualize the results.

The figure below outlines the VCPA

To summarize, VCPA consists of a CCDG/TOPMed functional equivalent pipeline. Together with the public Amazon Machine Image (AMI) or dockerized database, users can easily process any WGS/WES data on Amazon cloud with minimal installation.

AMI availability

Pipeline AMI: ami-2cb8cd53

Database AMI: ami-acc840d3

Overview of this documentation

Step 1: Configuration of tools required for VCPA: AWS accounts, keys, command line interface for S3 and Starcluster.

Steps 2-5: Preparation steps required for setting up the tracking database

Step 6: Job submission process

Step 7: Review quality of processed data

Step 8: Generating project level pVCF (i.e. joint-genotype gVCFs)

Availability: VCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the , as well as from this documentation.

National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site
Figure 1: A) VCPA architecture; B) Dynamic view of job status; C) Pipeline overview.