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.
npxskills add davila7/claude-code-templates--skill dnanexus-integrationLoading…
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.
This skill should be used when:
The skill is organized into five main areas, each with detailed reference documentation:
Purpose: Create executable programs (apps/applets) that run on the DNAnexus platform.
Key Operations:
dx-app-wizarddx build or dx build --appCommon Use Cases:
Reference: See references/app-development.md for:
Purpose: Manage files, records, and other data objects on the platform.
Key Operations:
dxpy.upload_local_file() and dxpy.download_dxfile()Common Use Cases:
Reference: See references/data-operations.md for:
Purpose: Run analyses, monitor execution, and orchestrate workflows.
Key Operations:
applet.run() or app.run()Common Use Cases:
Reference: See references/job-execution.md for:
Purpose: Programmatic access to DNAnexus platform through Python.
Key Operations:
Common Use Cases:
Reference: See references/python-sdk.md for:
Purpose: Configure app metadata and manage dependencies.
Key Operations:
Common Use Cases:
Reference: See references/configuration.md for:
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()
When working with DNAnexus, follow this decision tree:
Need to create a new executable?
Need to manage files or data?
Need to run an analysis or workflow?
Writing Python scripts for automation?
Configuring app settings or dependencies?
Often you'll need multiple capabilities together (e.g., app development + configuration, or data operations + job execution).
uv pip install dxpy
dx login
This authenticates your session and sets up access to projects and data.
dx --version
dx whoami
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()
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")
})
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"]
)
This skill includes detailed reference documentation:
Load these references when you need detailed information about specific operations or when working on complex tasks.
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