latchbio-integration

LatchBio Integration - A Serverless Workflow Platform for Bioinformatics

Skill Overview

LatchBio Integration is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. It supports creating cloud compute flows via @workflow/@task decorators, integrates Nextflow/Snakemake, and provides cloud storage management with LatchFile/LatchDir.

Use Cases

1. Genomics Data Analysis Pipeline Development

When you need to build RNA-seq, whole-genome sequencing, or single-cell analysis workflows, you can use the Latch SDK’s Python decorators to define compute tasks. The platform automatically handles Docker containerization and cloud resource scheduling. You only need to focus on algorithm logic—without managing server infrastructure. The platform supports complete genomics workflows, from quality control to differential expression analysis.

2. Protein Structure Prediction and GPU-Accelerated Computation

For protein folding tasks that require substantial compute resources (e.g., AlphaFold, ColabFold), Latch offers preconfigured GPU support (K80, V100, A100). You just need to use the @large_gpu_task decorator to get GPU acceleration. The platform also provides verification workflows that run production-grade AlphaFold pipelines directly, without manually configuring CUDA environments.

3. Cloud Migration and Integration of Existing Pipelines

If you already have Nextflow or Snakemake pipelines and want to run them in the cloud, Latch provides a seamless integration solution. You can embed your existing scripts into Python workflows, or use the Registry system to manage sample data and metadata—enabling a smooth transition from local to cloud while keeping pipelines reproducible and version-controlled.

Core Features

1. Python Decorator-Based Workflow Definition

Use decorators such as @workflow, @task, @small_task, @large_task, @small_gpu_task, and @large_gpu_task to define complex, multi-step bioinformatics workflows with pure Python code. The platform automatically handles task dependencies, parallel execution, and resource allocation. Write a function for each compute step, add type annotations and docstrings, and you’ll get an auto-generated, no-code user interface.

2. Cloud Storage and Data Management System

With LatchFile and LatchDir types, you can easily manage cloud file storage, including automatic file transfer, Glob pattern matching, and path operations. The Registry system offers structured data organization (Projects → Tables → Records), supports type-safe columns (string, number, file, link, enum), and record associations—helping you build a sample tracking and metadata management system.

3. Prebuilt Verification Workflow Library

Latch provides a wide range of production-grade, prebuilt pipelines, including Bulk RNA-seq, DESeq2, pathway analysis, AlphaFold, ColabFold, ArchR, scVelo, emptyDropsR, CRISPR analysis, and more. You can directly call these verification workflows via the latch.verified module, or use them as components in your custom workflows—saving development time and getting validated analysis results.

Frequently Asked Questions

How do I get started building my first workflow with the Latch SDK?

First install the Latch SDK (uv pip install latch) and log in (latch login). Then run latch init my-workflow to initialize the project. In the generated wf.py file, define your workflow function with the @workflow decorator and define compute tasks with the @task decorator. Finally, run latch register my-workflow to register the workflow on the platform. The platform will automatically build the Docker image and generate a user interface, so you can start a workflow run from the web UI.

What GPU types and resource configurations does the Latch platform support?

Latch provides several preconfigured resource decorators: @small_task (2 CPU, 8GB memory), @large_task (8 CPU, 30GB memory), @small_gpu_task (1 GPU), @large_gpu_task (4 GPU). Supported GPU types include K80, V100, and A100. You can also use the @custom_task decorator to precisely specify CPU, memory, number of GPUs, and temporary storage. The platform charges based on actual usage. Reasonable resource configuration can effectively control costs.

How can I migrate an existing Nextflow pipeline to Latch?

You can wrap Nextflow processes in Latch tasks, use @large_task or @custom_task to call the nextflow run command, and pass input/output files via LatchFile. Latch will automatically handle the environment and dependencies Nextflow requires. For complex Nextflow pipelines, it’s recommended to migrate step by step: first wrap independent Nextflow modules as Latch tasks, then gradually replace them with native Python tasks to achieve a better cloud-native experience and UI integration.