Marat's Notes

AWS HealthOmics is a specialized service designed for processing and analyzing genomic and other biological data at scale. This guide will walk you through the process of setting up and using the AWS HealthOmics API for your genomic data analysis workflows.

Prerequisites

Before getting started, ensure you have:

AWS Account Setup:
- Active AWS account
- Appropriate IAM permissions
- AWS CLI configured
- Python 3.8+ installed
Required AWS Services:
- AWS HealthOmics
- Amazon S3
- AWS IAM
- Amazon CloudWatch

Initial Setup

1. AWS CLI Configuration

First, configure your AWS credentials:

# Configure AWS CLI
aws configure

# Verify HealthOmics access
aws omics list-workflows

2. Python Environment Setup

Set up your Python environment:

# Create virtual environment
python -m venv healthomics-env
source healthomics-env/bin/activate

# Install required packages
pip install boto3 pandas numpy

Working with HealthOmics API

1. Basic API Integration

Here’s a basic example of using the HealthOmics API:

import boto3
from botocore.exceptions import ClientError

class HealthOmicsClient:
    def __init__(self):
        self.client = boto3.client('omics')
        self.s3_client = boto3.client('s3')
    
    def create_workflow(self, name, definition):
        try:
            response = self.client.create_workflow(
                name=name,
                definition=definition,
                description='Genomic analysis workflow'
            )
            return response['id']
        except ClientError as e:
            print(f"Error creating workflow: {e}")
            return None

    def list_workflows(self):
        try:
            response = self.client.list_workflows()
            return response['workflows']
        except ClientError as e:
            print(f"Error listing workflows: {e}")
            return []

2. Workflow Definition

Define a basic genomic analysis workflow:

workflow_definition = {
    "name": "genomic-analysis",
    "version": "1.0",
    "steps": [
        {
            "name": "quality-control",
            "tool": "fastqc",
            "inputs": {
                "reads": "${input.reads}"
            },
            "outputs": {
                "report": "qc_report"
            }
        },
        {
            "name": "alignment",
            "tool": "bwa",
            "inputs": {
                "reads": "${input.reads}",
                "reference": "${input.reference}"
            },
            "outputs": {
                "bam": "aligned.bam"
            }
        }
    ]
}

Data Management

1. S3 Integration

Set up S3 buckets for data storage:

def setup_storage(self):
    try:
        # Create S3 bucket for input data
        self.s3_client.create_bucket(
            Bucket='genomic-input-data',
            CreateBucketConfiguration={
                'LocationConstraint': 'us-west-2'
            }
        )
        
        # Create S3 bucket for output data
        self.s3_client.create_bucket(
            Bucket='genomic-output-data',
            CreateBucketConfiguration={
                'LocationConstraint': 'us-west-2'
            }
        )
    except ClientError as e:
        print(f"Error setting up storage: {e}")

2. Data Upload

Upload genomic data to S3:

def upload_data(self, file_path, bucket, key):
    try:
        self.s3_client.upload_file(
            file_path,
            bucket,
            key,
            ExtraArgs={
                'ServerSideEncryption': 'AES256'
            }
        )
        return True
    except ClientError as e:
        print(f"Error uploading data: {e}")
        return False

Running Analysis

1. Workflow Execution

Execute a genomic analysis workflow:

def run_workflow(self, workflow_id, input_data):
    try:
        response = self.client.start_run(
            workflowId=workflow_id,
            name='genomic-analysis-run',
            roleArn='arn:aws:iam::123456789012:role/HealthOmicsRole',
            parameters={
                'input.reads': input_data['reads'],
                'input.reference': input_data['reference']
            }
        )
        return response['id']
    except ClientError as e:
        print(f"Error running workflow: {e}")
        return None

2. Monitoring Progress

Monitor workflow execution:

def monitor_run(self, run_id):
    try:
        response = self.client.get_run(
            id=run_id
        )
        return {
            'status': response['status'],
            'progress': response.get('progress', 0),
            'startTime': response['startTime'],
            'stopTime': response.get('stopTime')
        }
    except ClientError as e:
        print(f"Error monitoring run: {e}")
        return None

Error Handling and Retries

Implement robust error handling:

from tenacity import retry, stop_after_attempt, wait_exponential

class HealthOmicsErrorHandler:
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    def execute_with_retry(self, func, *args, **kwargs):
        try:
            return func(*args, **kwargs)
        except ClientError as e:
            if e.response['Error']['Code'] == 'ThrottlingException':
                raise
            return None

Best Practices

1. Resource Management

Optimize resource usage:

def optimize_resources(self, workflow_id):
    try:
        response = self.client.update_workflow(
            id=workflow_id,
            resourceConfig={
                'memory': '16GB',
                'vcpus': 4,
                'storage': '100GB'
            }
        )
        return response
    except ClientError as e:
        print(f"Error optimizing resources: {e}")
        return None

2. Cost Optimization

Implement cost-saving measures:

def estimate_cost(self, workflow_id, input_size):
    try:
        response = self.client.get_workflow(
            id=workflow_id
        )
        
        # Calculate estimated cost based on input size and workflow complexity
        estimated_cost = self._calculate_cost(
            input_size,
            response['definition']['steps']
        )
        
        return estimated_cost
    except ClientError as e:
        print(f"Error estimating cost: {e}")
        return None

Security Considerations

1. Data Encryption

Implement data encryption:

def encrypt_data(self, data, key_id):
    try:
        kms_client = boto3.client('kms')
        response = kms_client.encrypt(
            KeyId=key_id,
            Plaintext=data
        )
        return response['CiphertextBlob']
    except ClientError as e:
        print(f"Error encrypting data: {e}")
        return None

2. Access Control

Set up IAM policies:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "omics:CreateWorkflow",
                "omics:StartRun",
                "omics:GetRun"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::genomic-input-data/*",
                "arn:aws:s3:::genomic-output-data/*"
            ]
        }
    ]
}

Conclusion

AWS HealthOmics provides a powerful platform for genomic data analysis. By following this guide, you can:

Set up and configure the HealthOmics API
Manage genomic data effectively
Execute and monitor analysis workflows
Implement security best practices
Optimize costs and resources

Remember to:

Regularly monitor your workflows
Implement proper error handling
Follow security best practices
Optimize resource usage
Keep track of costs

With proper implementation, AWS HealthOmics can significantly streamline your genomic data analysis workflows while maintaining security and cost-effectiveness.

Using AWS HealthOmics API for Genomic Data Analysis