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