What is the primary purpose of the DNAnexus Integration skill?

It helps developers build, deploy, and manage bioinformatics applications and data workflows specifically on the DNAnexus cloud genomics platform.

How does it assist with app configuration?

It provides comprehensive references for writing dxapp.json files, managing system dependencies (execDepends), and integrating Docker containers for complex environments.

Can I use this skill to manage genomic file formats?

Absolutely. The skill includes specific patterns and operations for handling common bioinformatics files such as FASTQ, BAM, and VCF within the DNAnexus environment.

Does this skill support the dxpy Python SDK?

Yes, it provides detailed guidance and best practices for using the dxpy library for programmatic data operations, object searching, and job execution.

DNAnexus Integration

Name: DNAnexus Integration
Author: Microck

byMicrock

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Data Science & ML

Streamlines the development, deployment, and management of bioinformatics applications and data workflows on the DNAnexus cloud genomics platform.

About

This skill empowers bioinformaticians and developers to efficiently interact with the DNAnexus platform via Claude Code. It provides domain-specific guidance for building apps and applets, managing large-scale genomic data objects, and orchestrating complex analysis workflows using the dxpy Python SDK. By offering implementation patterns for common bioinformatics file formats like FASTQ, BAM, and VCF, this skill significantly accelerates the development lifecycle of cloud-native genomic pipelines while ensuring best practices in resource management, security, and parallel execution.

Key Features

Comprehensive Python SDK (dxpy) reference for programmatic platform control
Streamlined app/applet development using dx-app-wizard and dxpy decorators
Automated configuration of dxapp.json and dependency management including Docker
Advanced job orchestration for parallel processing and multi-step pipeline chaining
Robust data management for uploading, searching, and organizing genomic datasets
81 GitHub stars

Use Cases

Building and deploying scalable variant calling or alignment pipelines
Automating high-throughput sequencing data uploads and organization across projects
Managing and monitoring parallel execution of complex genomics analysis jobs

About

Key Features

Comprehensive Python SDK (dxpy) reference for programmatic platform control
Streamlined app/applet development using dx-app-wizard and dxpy decorators
Automated configuration of dxapp.json and dependency management including Docker
Advanced job orchestration for parallel processing and multi-step pipeline chaining
Robust data management for uploading, searching, and organizing genomic datasets
81 GitHub stars

Use Cases

Building and deploying scalable variant calling or alignment pipelines
Automating high-throughput sequencing data uploads and organization across projects
Managing and monitoring parallel execution of complex genomics analysis jobs