alterlab-latchbio

name	alterlab-latchbio
description	Builds and deploys bioinformatics pipelines on the LatchBio platform using the Latch SDK — author workflows with @workflow/@task decorators, handle LatchFile/LatchDir I/O, register serverless workflows, configure CPU/GPU task resources, organize data in the Latch Registry, and wrap Nextflow/Snakemake pipelines. Use when developing or deploying a Latch SDK workflow, sizing task resources, working with the Registry, or porting a Nextflow/Snakemake bioinformatics pipeline onto LatchBio. Not for DNAnexus (dxpy/dx CLI) or generic Flyte. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(curl:*) Bash(python:*)
compatibility	Requires a LatchBio account and workspace plus the Latch SDK (`uv pip install latch`, latch>=2.x, Python 3.9+); deploying workflows needs Docker running and `latch login`
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

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 maintained by Latch
• Importable from the latch.verified Python module: rnaseq, deseq2_wf, mafft, trim_galore, gene_ontology_pathway_analysis
• Many more pipelines (e.g. AlphaFold, single-cell, CRISPR) are available as Verified Workflows in the platform UI; verify the exact import name in latch.verified before relying on a Python import (see references/verified-workflows.md)

Quick Start

Installation and Setup

# Install Latch SDK (uv-first; pip install latch also works)
uv pip install latch

# Login to Latch
latch login

# Initialize a new workflow (scaffolds wf/, Dockerfile, version)
latch init my-workflow

# Register workflow to platform (builds container, generates the UI)
latch register my-workflow

Prerequisites:

• Docker installed and running (registration builds a container locally)
• Latch account credentials
• Python 3.9+ (Latch SDK requires-python >=3.9)

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

Load the reference that matches the task:

• references/workflow-creation.md — latch init/latch register, @workflow/@task decorators, type annotations and docstrings (these populate the UI), launch plans, conditional sections, CLI/programmatic execution, parallel map_task, registration troubleshooting.
• references/data-management.md — LatchFile/LatchDir and latch:/// paths, glob patterns, the Registry API (Project, Table, Record), column types, the table.update() transaction, and workflow-Registry integration.
• references/resource-configuration.md — standard task decorators, @custom_task(cpu, memory, storage_gib, timeout), the GPU task decorators (small_gpu_task/large_gpu_task, v100_x{1,4,8}_task, g6e_*_task), and resource/cost right-sizing.
• references/verified-workflows.md — pipelines importable from latch.verified and how to combine them with custom tasks.

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

@small_task
def process_and_track(sample_name: str, table_id: str) -> str:
    """Process a sample tracked in a Registry table and write status/result back."""
    table = Table(table_id)  # construct by id; no Table.get classmethod

    # Find the record by name. list_records() yields pages of {record_id: Record}.
    sample = None
    for page in table.list_records():
        for record in page.values():
            if record.get_name() == sample_name:
                sample = record
                break
        if sample is not None:
            break
    if sample is None:
        raise ValueError(f"No record named {sample_name}")

    # Read column values
    values = sample.get_values()
    input_file = values["fastq_file"]  # a LatchFile for a 'file'-typed column
    # ... processing logic produces an output LatchFile ...

    # Write status/result back via the table.update() transaction (upsert by name)
    with table.update() as updater:
        updater.upsert_record(sample_name, status="completed", result=input_file)
    return "Success"


 () -> :
    
     process_and_track(sample_name=sample_name, table_id=table_id)

Best Practices

Workflow Design

1. Use type annotations for all parameters
2. Write clear docstrings (appear in UI)
3. Start with standard task decorators, scale up if needed
4. Break complex workflows into modular tasks
5. Implement proper error handling

Data Management

6. Use consistent folder structures
7. Define Registry schemas before bulk entry
8. Use linked records for relationships
9. Store metadata in Registry for traceability

Resource Configuration

10. Right-size resources (don't over-allocate)
11. Use GPU only when algorithms support it
12. Monitor execution metrics and optimize
13. Design for parallel execution when possible

Development Workflow

14. Test locally with Docker before registration
15. Use version control for workflow code
16. Document resource requirements
17. 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 (verify current API/decorator names here): https://github.com/latchbio/latch
• Support: [e-mail]