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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  1. Step 5: Configure your samples information in the tracking database

5.3 Populate the tracking database with the designated result folder for each sample to be processed

Besides populating the input file S3 path, users will also need to set the results folder using the API command VCPA provides, so that this information will be effectively imported to the tracking database:

function rawurlencode(){
echo -n "$1" | perl -pe 's/([^a-zA-Z0-9_.!~*()'\''-])/sprintf("%%%02X", ord($1))/ge' | perl -pe 's/(\W)/sprintf("%%%02X", ord($1))/ge'
}

$  s3='s3://YOUR/RESULTS/FOLDER'    
### S3 path for the results folder for each sample 
$  results_s3_path=$(rawurlencode $s3)
$ curl -sS "http://IP/v1/sample/set-attr/results_s3_uri/${project_id}/${sample_name}/${results_s3_path}"

GET http://IP/v1/sample/set-attr/results_s3_uri/${project_id}/${sample_name}/${results_s3_path}

Add the path of result files.

Path Parameters

Name
Type
Description

project_id

string

This is the project ID outputted by Section 3.2.

sample_name

string

Sample name (note this needs to match the sample name of the input file in the S3 bucket).

results_s3_path

string

S3 path of where the results will be written to.

{
    "status":"success",
    "id":"8"
}

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Last updated 6 years ago