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dnanexus-integration

DNAnexus cloud genomics platform.

Skill by lamm-mit

IndexedApache-2.0★ 250
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About

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Use cases: data

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What's inside

Skill name
dnanexus-integration
Description
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
License
Apache-2.0
Compatibility
Requires a DNAnexus account
Metadata
skill-author: K-Dense Inc.
Instructions
375 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (8)

  • SKILL.md
  • app-development.md · references/
  • configuration.md · references/
  • data-operations.md · references/
  • job-execution.md · references/
  • python-sdk.md · references/
  • demo.cpython-313.pyc · scripts/__pycache__/
  • demo.py · scripts/

SKILL.md (first lines)

# 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

From lamm-mit/scienceclaw, Apache-2.0.

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Signals

Stars
250
Forks
47
Open issues
1
Last commit (this folder)
Mar 14, 2026 (7 months ago)
Last release
Jun 23, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

Same skill elsewhere

Copies of this SKILL.md published by others; search shows only one of them.

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dnanexus-integration skill. Agent Mart. https://agentmart.dev/skills/lamm-mit-dnanexus-integration (last updated 2026-10-24)