Latchbio IntegrationSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

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---
name: latchbio-integration
description: "Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake 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

```bash
# 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

```python
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

```python
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) -> La

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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