AWS HealthOmics provides powerful tools for running genomic analysis pipelines at scale. This guide explores how to effectively use AWS HealthOmics for genomic data processing, with a focus on practical implementation, step-by-step instructions, and best practices.
Prerequisites
Before getting started, ensure you have:
- AWS Account Setup:
# Install AWS CLI curl "https://awscli.amazonaws.com/AWSCLIV2.pkg" -o "AWSCLIV2.pkg" sudo installer -pkg AWSCLIV2.pkg -target / # Configure AWS CLI aws configure # Enter your AWS Access Key ID # Enter your AWS Secret Access Key # Enter your default region (e.g., us-east-1) # Enter your output format (json)
- Required Tools:
# Install Python 3.8+ brew install python@3.8 # Create virtual environment python3.8 -m venv healthomics-env source healthomics-env/bin/activate # Install required packages pip install boto3 aws-healthomics-sdk pandas numpy
Initial Setup
1. AWS HealthOmics Configuration
# healthomics_config.py
import boto3
from aws_healthomics_sdk import HealthOmicsClient
class HealthOmicsConfig:
def __init__(self):
self.client = HealthOmicsClient(
region_name='us-east-1',
aws_access_key_id='YOUR_ACCESS_KEY',
aws_secret_access_key='YOUR_SECRET_KEY'
)
def create_workflow(self, name, definition):
"""Create a new workflow"""
try:
response = self.client.create_workflow(
name=name,
definition=definition,
description=f"Workflow for {name}"
)
return response['workflowId']
except Exception as e:
print(f"Error creating workflow: {e}")
return None
def list_workflows(self):
"""List all workflows"""
try:
response = self.client.list_workflows()
return response['workflows']
except Exception as e:
print(f"Error listing workflows: {e}")
return []
2. S3 Data Organization
# s3_manager.py
import boto3
from pathlib import Path
class S3Manager:
def __init__(self):
self.s3 = boto3.client('s3')
self.bucket = 'your-genomics-bucket'
def upload_reference_genome(self, local_path, s3_key):
"""Upload reference genome to S3"""
try:
self.s3.upload_file(
local_path,
self.bucket,
f"reference_genomes/{s3_key}"
)
return f"s3://{self.bucket}/reference_genomes/{s3_key}"
except Exception as e:
print(f"Error uploading reference genome: {e}")
return None
def upload_sequencing_data(self, local_path, s3_key):
"""Upload sequencing data to S3"""
try:
self.s3.upload_file(
local_path,
self.bucket,
f"sequencing_data/{s3_key}"
)
return f"s3://{self.bucket}/sequencing_data/{s3_key}"
except Exception as e:
print(f"Error uploading sequencing data: {e}")
return None
Pipeline Implementation
1. Workflow Definition
# workflow_definition.py
class WorkflowDefinition:
def __init__(self):
self.definition = {
"name": "Genomic Analysis Pipeline",
"version": "1.0",
"steps": [
{
"name": "Quality Control",
"tool": "FastQC",
"inputs": {
"reads": "${input.reads}"
},
"outputs": {
"report": "qc_report.html"
}
},
{
"name": "Alignment",
"tool": "BWA",
"inputs": {
"reads": "${input.reads}",
"reference": "${input.reference}"
},
"outputs": {
"bam": "aligned.bam"
}
},
{
"name": "Variant Calling",
"tool": "GATK",
"inputs": {
"bam": "${steps.alignment.outputs.bam}",
"reference": "${input.reference}"
},
"outputs": {
"vcf": "variants.vcf"
}
}
]
}
def get_definition(self):
return self.definition
2. Pipeline Execution
# pipeline_executor.py
from healthomics_config import HealthOmicsConfig
from s3_manager import S3Manager
class PipelineExecutor:
def __init__(self):
self.healthomics = HealthOmicsConfig()
self.s3 = S3Manager()
def run_pipeline(self, workflow_id, input_data):
"""Run a genomic pipeline"""
try:
response = self.healthomics.client.start_run(
workflowId=workflow_id,
input=input_data,
name=f"Run_{workflow_id}_{input_data['sample_id']}"
)
return response['runId']
except Exception as e:
print(f"Error running pipeline: {e}")
return None
def monitor_run(self, run_id):
"""Monitor pipeline execution"""
try:
response = self.healthomics.client.get_run(runId=run_id)
return {
'status': response['status'],
'progress': response['progress'],
'startTime': response['startTime'],
'endTime': response.get('endTime')
}
except Exception as e:
print(f"Error monitoring run: {e}")
return None
Data Management
1. Input Data Preparation
# data_preparation.py
import pandas as pd
from pathlib import Path
class DataPreparation:
def __init__(self):
self.metadata = pd.DataFrame()
def prepare_sequencing_data(self, data_dir):
"""Prepare sequencing data for pipeline"""
try:
# Read metadata
self.metadata = pd.read_csv(data_dir / 'metadata.csv')
# Validate data
for _, row in self.metadata.iterrows():
self._validate_sample(row)
return True
except Exception as e:
print(f"Error preparing data: {e}")
return False
def _validate_sample(self, sample):
"""Validate individual sample"""
required_files = ['R1.fastq.gz', 'R2.fastq.gz']
sample_dir = Path(sample['sample_dir'])
for file in required_files:
if not (sample_dir / file).exists():
raise ValueError(f"Missing required file: {file}")
2. Output Management
# output_manager.py
import json
from datetime import datetime
class OutputManager:
def __init__(self, s3_manager):
self.s3 = s3_manager
def organize_outputs(self, run_id, outputs):
"""Organize pipeline outputs"""
try:
# Create output structure
output_structure = {
'run_id': run_id,
'timestamp': datetime.now().isoformat(),
'outputs': outputs
}
# Save to S3
self.s3.upload_json(
output_structure,
f"pipeline_outputs/{run_id}/summary.json"
)
return True
except Exception as e:
print(f"Error organizing outputs: {e}")
return False
Quality Control
1. Data Quality Checks
# quality_control.py
import subprocess
from pathlib import Path
class QualityControl:
def __init__(self):
self.tools = {
'fastqc': 'fastqc',
'multiqc': 'multiqc'
}
def run_quality_checks(self, input_dir, output_dir):
"""Run quality control checks"""
try:
# Run FastQC
subprocess.run([
self.tools['fastqc'],
str(input_dir),
'-o', str(output_dir)
], check=True)
# Run MultiQC
subprocess.run([
self.tools['multiqc'],
str(output_dir),
'-o', str(output_dir)
], check=True)
return True
except Exception as e:
print(f"Error running quality checks: {e}")
return False
2. Pipeline Validation
# pipeline_validator.py
class PipelineValidator:
def __init__(self):
self.required_outputs = {
'alignment': ['bam', 'bai'],
'variant_calling': ['vcf', 'tbi']
}
def validate_pipeline_outputs(self, run_id, outputs):
"""Validate pipeline outputs"""
try:
for step, required_files in self.required_outputs.items():
if step not in outputs:
raise ValueError(f"Missing step: {step}")
for file in required_files:
if file not in outputs[step]:
raise ValueError(f"Missing output file: {file}")
return True
except Exception as e:
print(f"Error validating outputs: {e}")
return False
Cost Management
1. Resource Optimization
# resource_optimizer.py
class ResourceOptimizer:
def __init__(self):
self.instance_types = {
'small': {'vcpu': 2, 'memory': 4},
'medium': {'vcpu': 4, 'memory': 8},
'large': {'vcpu': 8, 'memory': 16}
}
def optimize_resources(self, input_size, complexity):
"""Optimize resource allocation"""
try:
if input_size < 10 and complexity == 'low':
return self.instance_types['small']
elif input_size < 50 and complexity == 'medium':
return self.instance_types['medium']
else:
return self.instance_types['large']
except Exception as e:
print(f"Error optimizing resources: {e}")
return self.instance_types['medium']
2. Cost Tracking
# cost_tracker.py
import boto3
from datetime import datetime, timedelta
class CostTracker:
def __init__(self):
self.ce = boto3.client('ce')
def track_pipeline_costs(self, run_id, start_time, end_time):
"""Track pipeline costs"""
try:
response = self.ce.get_cost_and_usage(
TimePeriod={
'Start': start_time.isoformat(),
'End': end_time.isoformat()
},
Granularity='DAILY',
Filter={
'Tags': {
'Key': 'RunId',
'Values': [run_id]
}
}
)
return response['ResultsByTime']
except Exception as e:
print(f"Error tracking costs: {e}")
return []
Best Practices
1. Pipeline Design
# pipeline_design.py
class PipelineDesign:
def __init__(self):
self.best_practices = {
'modularity': True,
'error_handling': True,
'logging': True,
'checkpointing': True
}
def implement_best_practices(self, workflow_definition):
"""Implement best practices in workflow"""
try:
# Add error handling
for step in workflow_definition['steps']:
step['error_handling'] = {
'retry_count': 3,
'retry_delay': 300
}
# Add logging
workflow_definition['logging'] = {
'level': 'INFO',
'destination': 's3://your-bucket/logs'
}
# Add checkpointing
workflow_definition['checkpointing'] = {
'enabled': True,
'interval': 3600
}
return workflow_definition
except Exception as e:
print(f"Error implementing best practices: {e}")
return workflow_definition
2. Execution Management
# execution_manager.py
class ExecutionManager:
def __init__(self):
self.max_concurrent_runs = 5
self.active_runs = set()
def manage_execution(self, run_id, workflow_id, input_data):
"""Manage pipeline execution"""
try:
# Check concurrent runs
if len(self.active_runs) >= self.max_concurrent_runs:
raise Exception("Maximum concurrent runs reached")
# Start run
self.active_runs.add(run_id)
# Monitor run
while True:
status = self.monitor_run(run_id)
if status['status'] in ['COMPLETED', 'FAILED']:
self.active_runs.remove(run_id)
break
return status
except Exception as e:
print(f"Error managing execution: {e}")
return None
Conclusion
Running genomic pipelines with AWS HealthOmics requires careful planning and implementation. By following this guide, you can:
- Set up efficient pipelines
- Optimize resource usage
- Ensure data quality
- Maintain security
- Control costs
Remember to:
- Regularly review pipeline performance
- Optimize resource allocation
- Monitor costs
- Update security measures
- Follow best practices
With proper implementation and maintenance, AWS HealthOmics can provide powerful tools for running genomic analysis pipelines at scale while maintaining efficiency and security.