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:

Need to create a new executable?

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

Need to manage files or data?

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

Need to run an analysis or workflow?

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

Writing Python scripts for automation?

Yes → Use Python SDK (references/python-sdk.md) No → Continue to step 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 uv 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 Error Handling: Always wrap API calls in try-except blocks Resource Management: Choose appropriate instance types for workloads Data Organization: Use consistent folder structures and metadata Cost Optimization: Archive old data, use appropriate storage classes Documentation: Include clear descriptions in dxapp.json Testing: Test apps with various input types before production use Version Control: Use semantic versioning for apps Security: Never hardcode credentials in source code Logging: Include informative log messages for debugging 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: support@dnanexus.com