latchbio-integration

仓库: davila7/claude-code-templates
安装量: 133
排名: #6480

安装

npx skills add https://github.com/davila7/claude-code-templates --skill latchbio-integration

LatchBio Integration Overview

Latch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow/@task decorators, manage cloud data with LatchFile/LatchDir, configure resources, and integrate Nextflow/Snakemake pipelines.

Core Capabilities

The Latch platform provides four main areas of functionality:

  1. Workflow Creation and Deployment Define serverless workflows using Python decorators Support for native Python, Nextflow, and Snakemake pipelines Automatic containerization with Docker Auto-generated no-code user interfaces Version control and reproducibility
  2. Data Management Cloud storage abstractions (LatchFile, LatchDir) Structured data organization with Registry (Projects → Tables → Records) Type-safe data operations with links and enums Automatic file transfer between local and cloud Glob pattern matching for file selection
  3. Resource Configuration Pre-configured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task) Custom resource specifications (CPU, memory, GPU, storage) GPU support (K80, V100, A100) Timeout and storage configuration Cost optimization strategies
  4. Verified Workflows Production-ready pre-built pipelines Bulk RNA-seq, DESeq2, pathway analysis AlphaFold and ColabFold for protein structure prediction Single-cell tools (ArchR, scVelo, emptyDropsR) CRISPR analysis, phylogenetics, and more Quick Start Installation and Setup

Install Latch SDK

python3 -m uv pip install latch

Login to Latch

latch login

Initialize a new workflow

latch init my-workflow

Register workflow to platform

latch register my-workflow

Prerequisites:

Docker installed and running Latch account credentials Python 3.8+ Basic Workflow Example from latch import workflow, small_task from latch.types import LatchFile

@small_task def process_file(input_file: LatchFile) -> LatchFile: """Process a single file""" # Processing logic return output_file

@workflow def my_workflow(input_file: LatchFile) -> LatchFile: """ My bioinformatics workflow

Args:
    input_file: Input data file
"""
return process_file(input_file=input_file)

When to Use This Skill

This skill should be used when encountering any of the following scenarios:

Workflow Development:

"Create a Latch workflow for RNA-seq analysis" "Deploy my pipeline to Latch" "Convert my Nextflow pipeline to Latch" "Add GPU support to my workflow" Working with @workflow, @task decorators

Data Management:

"Organize my sequencing data in Latch Registry" "How do I use LatchFile and LatchDir?" "Set up sample tracking in Latch" Working with latch:/// paths

Resource Configuration:

"Configure GPU for AlphaFold on Latch" "My task is running out of memory" "How do I optimize workflow costs?" Working with task decorators

Verified Workflows:

"Run AlphaFold on Latch" "Use DESeq2 for differential expression" "Available pre-built workflows" Using latch.verified module Detailed Documentation

This skill includes comprehensive reference documentation organized by capability:

references/workflow-creation.md

Read this for:

Creating and registering workflows Task definition and decorators Supporting Python, Nextflow, Snakemake Launch plans and conditional sections Workflow execution (CLI and programmatic) Multi-step and parallel pipelines Troubleshooting registration issues

Key topics:

latch init and latch register commands @workflow and @task decorators LatchFile and LatchDir basics Type annotations and docstrings Launch plans with preset parameters Conditional UI sections references/data-management.md

Read this for:

Cloud storage with LatchFile and LatchDir Registry system (Projects, Tables, Records) Linked records and relationships Enum and typed columns Bulk operations and transactions Integration with workflows Account and workspace management

Key topics:

latch:/// path format File transfer and glob patterns Creating and querying Registry tables Column types (string, number, file, link, enum) Record CRUD operations Workflow-Registry integration references/resource-configuration.md

Read this for:

Task resource decorators Custom CPU, memory, GPU configuration GPU types (K80, V100, A100) Timeout and storage settings Resource optimization strategies Cost-effective workflow design Monitoring and debugging

Key topics:

@small_task, @large_task, @small_gpu_task, @large_gpu_task @custom_task with precise specifications Multi-GPU configuration Resource selection by workload type Platform limits and quotas references/verified-workflows.md

Read this for:

Pre-built production workflows Bulk RNA-seq and DESeq2 AlphaFold and ColabFold Single-cell analysis (ArchR, scVelo) CRISPR editing analysis Pathway enrichment Integration with custom workflows

Key topics:

latch.verified module imports Available verified workflows Workflow parameters and options Combining verified and custom steps Version management Common Workflow Patterns Complete RNA-seq Pipeline from latch import workflow, small_task, large_task from latch.types import LatchFile, LatchDir

@small_task def quality_control(fastq: LatchFile) -> LatchFile: """Run FastQC""" return qc_output

@large_task def alignment(fastq: LatchFile, genome: str) -> LatchFile: """STAR alignment""" return bam_output

@small_task def quantification(bam: LatchFile) -> LatchFile: """featureCounts""" return counts

@workflow def rnaseq_pipeline( input_fastq: LatchFile, genome: str, output_dir: LatchDir ) -> LatchFile: """RNA-seq analysis pipeline""" qc = quality_control(fastq=input_fastq) aligned = alignment(fastq=qc, genome=genome) return quantification(bam=aligned)

GPU-Accelerated Workflow from latch import workflow, small_task, large_gpu_task from latch.types import LatchFile

@small_task def preprocess(input_file: LatchFile) -> LatchFile: """Prepare data""" return processed

@large_gpu_task def gpu_computation(data: LatchFile) -> LatchFile: """GPU-accelerated analysis""" return results

@workflow def gpu_pipeline(input_file: LatchFile) -> LatchFile: """Pipeline with GPU tasks""" preprocessed = preprocess(input_file=input_file) return gpu_computation(data=preprocessed)

Registry-Integrated Workflow from latch import workflow, small_task from latch.registry.table import Table from latch.registry.record import Record from latch.types import LatchFile

@small_task def process_and_track(sample_id: str, table_id: str) -> str: """Process sample and update Registry""" # Get sample from registry table = Table.get(table_id=table_id) records = Record.list(table_id=table_id, filter={"sample_id": sample_id}) sample = records[0]

# Process
input_file = sample.values["fastq_file"]
output = process(input_file)

# Update registry
sample.update(values={"status": "completed", "result": output})
return "Success"

@workflow def registry_workflow(sample_id: str, table_id: str): """Workflow integrated with Registry""" return process_and_track(sample_id=sample_id, table_id=table_id)

Best Practices Workflow Design Use type annotations for all parameters Write clear docstrings (appear in UI) Start with standard task decorators, scale up if needed Break complex workflows into modular tasks Implement proper error handling Data Management Use consistent folder structures Define Registry schemas before bulk entry Use linked records for relationships Store metadata in Registry for traceability Resource Configuration Right-size resources (don't over-allocate) Use GPU only when algorithms support it Monitor execution metrics and optimize Design for parallel execution when possible Development Workflow Test locally with Docker before registration Use version control for workflow code Document resource requirements Profile workflows to determine actual needs Troubleshooting Common Issues

Registration Failures:

Ensure Docker is running Check authentication with latch login Verify all dependencies in Dockerfile Use --verbose flag for detailed logs

Resource Problems:

Out of memory: Increase memory in task decorator Timeouts: Increase timeout parameter Storage issues: Increase ephemeral storage_gib

Data Access:

Use correct latch:/// path format Verify file exists in workspace Check permissions for shared workspaces

Type Errors:

Add type annotations to all parameters Use LatchFile/LatchDir for file/directory parameters Ensure workflow return type matches actual return Additional Resources Official Documentation: https://docs.latch.bio GitHub Repository: https://github.com/latchbio/latch Slack Community: Join Latch SDK workspace API Reference: https://docs.latch.bio/api/latch.html Blog: https://blog.latch.bio Support

For issues or questions:

Check documentation links above Search GitHub issues Ask in Slack community Contact support@latch.bio

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