AWS Migration Guide

Introduction

Migration of bioinformatics pipelines to Amazon Web Services (AWS) can achieve significant scalability for next-generation sequencing (NGS) analysis, machine learning (ML) training, digital pathology, etc., with a cost reduction of 40–70% for compute resources. This document presents a clear step-by-step path for the migration of high-performance computing (HPC) environments to the cloud.

Why Migrate Bioinformatics Workflows to AWS?

Current challenges with traditional HPC:

• Manual resource provisioning for variable workloads
• Long queue times for peak analysis periods
• Difficult reproducibility across environments
• Scaling limitations for 10TB+ datasets

AWS Benefits:

• Pay-per-use pricing (no idle cluster costs)
• Auto-scaling for 100 to 100,000 samples
• Native integration with Nextflow/Snakemake
• Managed services (HealthOmics, Batch, EMR)

Step-by-Step Migration Process

Phase 1: Assessment and Planning (Week 1)

1. Inventory Current Workflows:

• List all pipelines (RNA-seq, WGS, ChIP-seq, ML)
• Document tools (GATK, STAR, BWA, FastQC)
• Identify input sizes (samples, file formats)
• Map compute requirements (CPU/GPU cores, RAM)

2. Choose Migration Strategy:

• Small teams (<10 people): AWS Batch + Nextflow
• Medium teams: HealthOmics + CI/CD pipeline
• Enterprise: Full DevOps (CodePipeline + HealthOmics)

3. Set Up AWS Organization:

    # Create accounts via AWS Organizations
    aws organizations create-account --email operations@yourorg.com
    aws organizations create-account --email bioinformatics@yourorg.com
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Phase 2: Foundation Infrastructure (Weeks 2-3)

Networking and Security Baseline:

• VPC: Multi-AZ with public/private subnets
• Security Groups: Restrict to workflow ports (22, 443)
• IAM Roles: Least privilege for EC2/Batch/HealthOmics
• S3 Bucket Policy: Encrypt + versioning enabled

Storage Strategy:

• Raw data → S3 Glacier Deep Archive ($0.00099/GB/month)
• Working data → S3 Standard IA ($0.0125/GB/month)
• Results → S3 Intelligent-Tiering (auto-optimizes)

AWS CLI Setup:

    # Create S3 buckets
    aws s3 mb s3://your-bioinformatics-bucket/raw/
    aws s3 mb s3://your-bioinformatics-bucket/processed/
    # Enable versioning
    aws s3api put-bucket-versioning --bucket your-bioinformatics-bucket --versioning-configuration Status=Enabled

Phase 3: Containerization (Weeks 4-5)

Dockerize All Tools (Example: GATK container):

    FROM ubuntu:20.04
    RUN apt-get update && apt-get install -y openjdk-11-jre-headless
    RUN wget https://github.com/broadinstitute/gatk/releases/latest/download/gatk-4.5.0.0.zip
    ENTRYPOINT ["gatk"]

Push to Amazon ECR:

    # Create ECR repository
    aws ecr create-repository --repository-name bioinformatics/gatk
    # Tag and push Docker image
    docker tag gatk:latest 123456789012.dkr.ecr.us-east-1.amazonaws.com/bioinformatics/gatk:latest
    docker push 123456789012.dkr.ecr.us-east-1.amazonaws.com/bioinformatics/gatk:latest

Phase 4: Workflow Migration (Weeks 6-8)

Nextflow/Snakemake Cloud Configuration (Nextflow nextflow.config):

  profiles {
    aws {
      process.executor = 'awsbatch'
      aws.batch.jobDefinition = 'bioinformatics-job'
      aws.batch.queue = 'bioinformatics-queue'
      workDir = 's3://your-bioinformatics-bucket/work'
    }
  } 
                  

Test Small Workload (Pilot: 10 samples):

nextflow run rnaseq.nf -profile aws --samples sample_list.csv --outdir s3://your-bioinformatics-bucket/pilot/

Phase 5: Production Deployment (Weeks 9-10)

CI/CD Pipeline Setup:

• CodeCommit → CodePipeline → CodeBuild → HealthOmics
• Automatic testing → Manual approval → Production deploy

Scale Testing:

• 100 samples → 1,000 samples → 10,000 samples
• Monitor costs via AWS Cost Explorer
• Optimize instance types (c6i.large → c6g.large ARM)

Cost Optimization Framework

Pro Tips:

• Use Spot Instances (70-90% savings)
• Right-size instances (c6i.large vs m6i.xlarge)
• S3 Lifecycle policies to Glacier
• Reserved Instances for predictable workloads

Common Migration Challenges and Solutions

SyncBio Bioinformatics Migration Results

SyncBio successfully migrated 15 production pipelines processing 25TB NGS data monthly:

Before Migration (On-Prem HPC):

• Monthly compute: $18,500
• Processing time: 28 days for 5,000 samples
• Manual intervention: 40 hours/month

After AWS Migration:

• Monthly compute: $7,200 (61% savings)
• Processing time: 4 days for 5,000 samples
• Hands-free operation: 2 hours/month monitoring
• Pipeline velocity: 3x faster iterations

Key Infrastructure:

• GenomeFlow-Prod: Nextflow + AWS Batch (20,000 samples/month)
• PathoML-Pipeline: GPU Spot + ECR (CNN pathology)
• VariantML-Pipe: HealthOmics + CI/CD

This migration enables SyncBio's CloudBioML expansion and supports EU research collaborations with reproducible, scalable bioinformatics infrastructure.