latchbio-integration

LatchBio Integration - Cloud Bioinformatics Workflow Platform

Skills Overview

Latch is a Python platform for building and deploying bioinformatics workflows, supporting deployment of analysis pipelines as serverless cloud pipelines and compatible with native Python, Nextflow, and Snakemake.

Applicable Scenarios

1. Genomics Data Analysis

RNA-seq differential expression analysis (DESeq2)

Protein structure prediction (AlphaFold, ColabFold)

Single-cell data analysis (ArchR, scVelo)

CRISPR editing analysis

2. Workflow Cloud Deployment

Migrate local Python analysis pipelines to the cloud

Cloud deployment of Nextflow/Snakemake pipelines

GPU-accelerated compute jobs (supports K80, V100, A100)

Serverless architecture with automatic containerization

3. Data Management and Collaboration

Cloud file management (LatchFile, LatchDir)

Experiment data tracking (Latch Registry)

Team collaboration and version control

Workflow automation and scheduling

Core Features

1. Workflow Development and Deployment

Use Python decorators (@workflow, @task) to define analysis pipelines, automatically generate a no-code user interface, and support Docker containerization and version control.

from latch import workflow, small_task
from latch.types import LatchFile

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

@workflow
def my_workflow(input_file: LatchFile) -> LatchFile:
    return process_file(input_file=input_file)

2. Cloud Data Management

Abstract cloud storage via LatchFile/LatchDir and organize experimental data using the Registry system (project → table → record), supporting automatic file transfer and bulk operations.

3. Flexible Resource Configuration

Provides preconfigured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task) and supports custom CPU, memory, GPU, and storage configurations to optimize compute cost.

4. Validated Workflow Library

Built-in production-ready analysis pipelines, including Bulk RNA-seq, DESeq2, pathway analysis, AlphaFold, etc., which can be called directly or combined with custom workflows.

Frequently Asked Questions

What is the Latch platform? What is it suitable for?

Latch is a cloud workflow platform designed for bioinformatics. It allows researchers to define analysis pipelines using Python code and deploy them to the cloud with one click. It is suitable for genomics analysis, protein structure prediction, single-cell data analysis, and other bioinformatics tasks that require large compute resources.

How do I deploy my first workflow on Latch?

First install the Latch SDK: python3 -m uv pip install latch. Then log in: latch login. Initialize a new workflow: latch init my-workflow. After writing it, register it to the platform: latch register my-workflow. Docker and Python 3.8+ are required.

What is the difference between Latch and Nextflow?

Latch is Python-based and uses decorator syntax to define workflows and automatically generate a user interface; Nextflow uses the DSL2 domain-specific language. Latch provides a native cloud Registry data management system and automatic containerization, while Nextflow requires additional configuration. The two can interoperate, and Latch also supports deploying Nextflow pipelines.

Which GPU types does Latch support?

Latch supports K80, V100, and A100 GPU types. Use the @small_gpu_task or @large_gpu_task decorators to enable GPU support, suitable for AlphaFold, deep learning, and other GPU-accelerated tasks. You can also use @custom_task to precisely configure the number of GPUs and other resources.

Is Latch free? How is it billed?

Latch offers free trial credits; actual billing is based on compute resource usage (CPU, memory, GPU, storage time). It's recommended to start testing with @small_task and upgrade resource configurations based on actual needs to optimize cost. For detailed pricing, consult official channels or the documentation.