DNAnexus Integration

DNAnexus Integration

Overview

DNAnexus is a cloud platform for biomedical data analysis and genomics. Build and deploy apps/​applets, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.

When to Use This Skill

This skill should be used when:

• Creating, building, or modifying DNAnexus apps/​applets
• Uploading, downloading, searching, or organizing files and records
• Running analyses, monitoring jobs, creating workflows
• Writing scripts using dxpy to interact with the platform
• Setting up dxapp.json, managing dependencies, using Docker
• Processing FASTQ, BAM, VCF, or other bioinformatics files
• Managing projects, permissions, or platform resources

Core Capabilities

The skill is organized into five main areas, each with detailed reference documentation:

1. App Development

Purpose: Create executable programs (apps/​applets) that run on the DNAnexus platform.

Key Operations:

• Generate app skeleton with dx-app-wizard
• Write Python or Bash apps with proper entry points
• Handle input/​output data objects
• Deploy with dx build or dx build --app
• Test apps on the platform

Common Use Cases:

• Bioinformatics pipelines (alignment, variant calling)
• Data processing workflows
• Quality control and filtering
• Format conversion tools

Reference: See references/​app-development.md for:

• Complete app structure and patterns
• Python entry point decorators
• Input/​output handling with dxpy
• Development best practices
• Common issues and solutions

2. Data Operations

Purpose: Manage files, records, and other data objects on the platform.

Key Operations:

• Upload/​download files with dxpy.upload_local_file() and dxpy.download_dxfile()
• Create and manage records with metadata
• Search for data objects by name, properties, or type
• Clone data between projects
• Manage project folders and permissions

Common Use Cases:

• Uploading sequencing data (FASTQ files)
• Organizing analysis results
• Searching for specific samples or experiments
• Backing up data across projects
• Managing reference genomes and annotations

Reference: See references/​data-operations.md for:

• Complete file and record operations
• Data object lifecycle (open/​closed states)
• Search and discovery patterns
• Project management
• Batch operations

3. Job Execution

Purpose: Run analyses, monitor execution, and orchestrate workflows.

Key Operations:

• Launch jobs with applet.run() or app.run()
• Monitor job status and logs
• Create subjobs for parallel processing
• Build and run multi-step workflows
• Chain jobs with output references

Common Use Cases:

• Running genomics analyses on sequencing data
• Parallel processing of multiple samples
• Multi-step analysis pipelines
• Monitoring long-running computations
• Debugging failed jobs

Reference: See references/​job-execution.md for:

• Complete job lifecycle and states
• Workflow creation and orchestration
• Parallel execution patterns
• Job monitoring and debugging
• Resource management

4. Python SDK (dxpy)

Purpose: Programmatic access to DNAnexus platform through Python.

Key Operations:

• Work with data object handlers (DXFile, DXRecord, DXApplet, etc.)
• Use high-level functions for common tasks
• Make direct API calls for advanced operations
• Create links and references between objects
• Search and discover platform resources

Common Use Cases:

• Automation scripts for data management
• Custom analysis pipelines
• Batch processing workflows
• Integration with external tools
• Data migration and organization

Reference: See references/​python-sdk.md for:

• Complete dxpy class reference
• High-level utility functions
• API method documentation
• Error handling patterns
• Common code patterns

5. Configuration and Dependencies

Purpose: Configure app metadata and manage dependencies.

Key Operations:

• Write dxapp.json with inputs, outputs, and run specs
• Install system packages (execDepends)
• Bundle custom tools and resources
• Use assets for shared dependencies
• Integrate Docker containers
• Configure instance types and timeouts

Common Use Cases:

• Defining app input/​output specifications
• Installing bioinformatics tools (samtools, bwa, etc.)
• Managing Python package dependencies
• Using Docker images for complex environments
• Selecting computational resources

Reference: See references/​configuration.md for:

• Complete dxapp.json specification
• Dependency management strategies
• Docker integration patterns
• Regional and resource configuration
• Example configurations

Quick Start Examples

Upload and Analyze Data

import dxpy

# Upload input file
input_file = dxpy.upload_local_file("sample.fastq", project="project-xxxx")

# Run analysis
job = dxpy.DXApplet("applet-xxxx").run({
    "reads": dxpy.dxlink(input_file.get_id())
})

# Wait for completion
job.wait_on_done()

# Download results
output_id = job.describe()["output"]["aligned_reads"]["$dnanexus_link"]
dxpy.download_dxfile(output_id, "aligned.bam")

Search and Download Files

import dxpy

# Find BAM files from a specific experiment
files = dxpy.find_data_objects(
    classname="file",
    name="*.bam",
    properties={"experiment": "exp001"},
    project="project-xxxx"
)

# Download each file
for file_result in files:
    file_obj = dxpy.DXFile(file_result["id"])
    filename = file_obj.describe()["name"]
    dxpy.download_dxfile(file_result["id"], filename)

Create Simple App

# src/​my-app.py
import dxpy
import subprocess

@dxpy.entry_point('main')
def main(input_file, quality_threshold=30):
    # Download input
    dxpy.download_dxfile(input_file["$dnanexus_link"], "input.fastq")

    # Process
    subprocess.check_call([
        "quality_filter",
        "--input", "input.fastq",
        "--output", "filtered.fastq",
        "--threshold", str(quality_threshold)
    ])

    # Upload output
    output_file = dxpy.upload_local_file("filtered.fastq")

    return {
        "filtered_reads": dxpy.dxlink(output_file)
    }

dxpy.run()

Workflow Decision Tree

When working with DNAnexus, follow this decision tree:

1. Need to create a new executable?

   • Yes → Use App Development (references/​app-development.md)
   • No → Continue to step 2

2. Need to manage files or data?

   • Yes → Use Data Operations (references/​data-operations.md)
   • No → Continue to step 3

3. Need to run an analysis or workflow?

   • Yes → Use Job Execution (references/​job-execution.md)
   • No → Continue to step 4

4. Writing Python scripts for automation?

   • Yes → Use Python SDK (references/​python-sdk.md)
   • No → Continue to step 5

5. Configuring app settings or dependencies?

   • Yes → Use Configuration (references/​configuration.md)

Often you'll need multiple capabilities together (e.g., app development + configuration, or data operations + job execution).

Installation and Authentication

Install dxpy

pip install dxpy

Login to DNAnexus

dx login

This authenticates your session and sets up access to projects and data.

Verify Installation

dx --version
dx whoami

Common Patterns

Pattern 1: Batch Processing

Process multiple files with the same analysis:

# Find all FASTQ files
files = dxpy.find_data_objects(
    classname="file",
    name="*.fastq",
    project="project-xxxx"
)

# Launch parallel jobs
jobs = []
for file_result in files:
    job = dxpy.DXApplet("applet-xxxx").run({
        "input": dxpy.dxlink(file_result["id"])
    })
    jobs.append(job)

# Wait for all completions
for job in jobs:
    job.wait_on_done()

Pattern 2: Multi-Step Pipeline

Chain multiple analyses together:

# Step 1: Quality control
qc_job = qc_applet.run({"reads": input_file})

# Step 2: Alignment (uses QC output)
align_job = align_applet.run({
    "reads": qc_job.get_output_ref("filtered_reads")
})

# Step 3: Variant calling (uses alignment output)
variant_job = variant_applet.run({
    "bam": align_job.get_output_ref("aligned_bam")
})

Pattern 3: Data Organization

Organize analysis results systematically:

# Create organized folder structure
dxpy.api.project_new_folder(
    "project-xxxx",
    {"folder": "/​experiments/​exp001/​results", "parents": True}
)

# Upload with metadata
result_file = dxpy.upload_local_file(
    "results.txt",
    project="project-xxxx",
    folder="/​experiments/​exp001/​results",
    properties={
        "experiment": "exp001",
        "sample": "sample1",
        "analysis_date": "2025-10-20"
    },
    tags=["validated", "published"]
)

Best Practices

1. Error Handling: Always wrap API calls in try-except blocks
2. Resource Management: Choose appropriate instance types for workloads
3. Data Organization: Use consistent folder structures and metadata
4. Cost Optimization: Archive old data, use appropriate storage classes
5. Documentation: Include clear descriptions in dxapp.json
6. Testing: Test apps with various input types before production use
7. Version Control: Use semantic versioning for apps
8. Security: Never hardcode credentials in source code
9. Logging: Include informative log messages for debugging
10. Cleanup: Remove temporary files and failed jobs

Resources

This skill includes detailed reference documentation:

references/

• app-development.md - Complete guide to building and deploying apps/​applets
• data-operations.md - File management, records, search, and project operations
• job-execution.md - Running jobs, workflows, monitoring, and parallel processing
• python-sdk.md - Comprehensive dxpy library reference with all classes and functions
• configuration.md - dxapp.json specification and dependency management

Load these references when you need detailed information about specific operations or when working on complex tasks.

Getting Help

• Official documentation: https://documentation.dnanexus.com/
• API reference: http://autodoc.dnanexus.com/
• GitHub repository: https://github.com/dnanexus/dx-toolkit
• Support: [email protected]