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.
# name defaults to exact match; pass name_mode="glob" to use wildcards.
files = dxpy.find_data_objects(
    classname="file",
    name="*.bam",
    name_mode="glob",
    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

1. Need to manage files or data?

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

1. Need to run an analysis or workflow?

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

1. Writing Python scripts for automation?

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

1. 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

Batch processing (find files, launch parallel jobs) is shown in the Quick Start above; see references/job-execution.md for scatter-gather and subjobs.

Pattern 1: Multi-Step Pipeline

Chain multiple analyses together (get_output_ref passes one job's output as the next job's input):

# 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 2: 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

• Never hardcode API tokens; authenticate via dx login or DX_SECURITY_CONTEXT.
• find_* name filters match exactly by default — pass name_mode="glob" for wildcards.
• Pass job outputs to downstream inputs with get_output_ref() directly (it is already a dxlink).
• Pick instance types deliberately and set timeoutPolicy to cap runaway cost.

Each reference file ends with topic-specific best practices for deeper guidance.

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]