dnanexus-integration

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.

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リポジトリ: oimiragieo/agent-studio
ソースの最終更新活動: 2026年4月21日 23:45
検出された SKILL.md の言語: 英語
スター: 36
フォーク: 6

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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	Unknown
compatibility	Requires a DNAnexus account
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	2addf736a117ee2f

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:

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")

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)

# 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()

uv pip install dxpy

dx login

dx --version
dx whoami

# 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()

# 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")
})

# 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"]
)