Can I use this skill to migrate existing Nextflow pipelines?

Yes, the skill provides specific implementation patterns for integrating and deploying Nextflow and Snakemake pipelines directly onto the Latch platform.

Do I need to manually configure Docker for my workflows?

No, the Latch SDK automates the containerization process, and this skill helps you define the necessary dependencies and environment settings within your Python code.

Does it support GPU-intensive bioinformatics tasks?

Absolutely. It includes resource configuration patterns for V100 and A100 GPUs, making it ideal for high-compute tools like AlphaFold and ColabFold.

How does it handle large biological datasets?

It utilizes LatchFile and LatchDir abstractions alongside the Latch Registry to manage cloud storage, data transfers, and structured metadata tracking.

LatchBio Integration

Name: LatchBio Integration
Author: Microck

byMicrock

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데이터 과학 및 ML

Builds, deploys, and manages serverless bioinformatics workflows and data pipelines using the Latch SDK.

소개

The LatchBio Integration skill enables developers and bioinformaticians to build reproducible, serverless pipelines in Python using the Latch platform. It provides specialized patterns for creating workflows with @workflow and @task decorators, managing complex cloud data via Latch Registry, and optimizing compute resources for high-intensity tasks like AlphaFold or RNA-seq analysis. By bridging the gap between local script development and production-grade cloud infrastructure, this skill facilitates automated containerization, GPU configuration, and seamless integration with existing Nextflow or Snakemake pipelines.

주요 기능

Granular resource configuration for CPU, memory, and specialized GPUs (A100, V100, K80)
81 GitHub stars
Native support for containerizing Nextflow and Snakemake pipelines
Deployment of verified production-ready workflows for protein folding and sequencing analysis
Automated cloud data management using LatchFile, LatchDir, and Latch Registry
Serverless workflow creation with Python-native @workflow and @task decorators

사용 사례

Organizing and tracking large-scale biological datasets using the Latch Registry for data lineage
Developing and deploying reproducible RNA-seq or single-cell analysis pipelines to cloud environments
Scaling bioinformatics computations with specialized GPU resources for AlphaFold or protein modeling

소개

주요 기능

Granular resource configuration for CPU, memory, and specialized GPUs (A100, V100, K80)
81 GitHub stars
Native support for containerizing Nextflow and Snakemake pipelines
Deployment of verified production-ready workflows for protein folding and sequencing analysis
Automated cloud data management using LatchFile, LatchDir, and Latch Registry
Serverless workflow creation with Python-native @workflow and @task decorators

사용 사례

Organizing and tracking large-scale biological datasets using the Latch Registry for data lineage
Developing and deploying reproducible RNA-seq or single-cell analysis pipelines to cloud environments
Scaling bioinformatics computations with specialized GPU resources for AlphaFold or protein modeling