Overview

nf-core pipelines are designed to be accessible, flexible, and reproducible across different computing environments. Whether you’re running a pipeline on a laptop, HPC cluster, or cloud infrastructure, nf-core provides consistent command structures and comprehensive configuration options to meet your needs.

This section covers everything you need to run nf-core pipelines effectively, from basic execution commands to advanced configuration and specialised scenarios.

Getting started

All nf-core pipelines follow a consistent command structure and execution pattern. Start here to learn the essential commands for running any nf-core pipeline with your own data.

• Running pipelines: Essential commands and patterns for running nf-core pipelines, including testing with sample data, using parameter files, and resuming failed runs

Configuration

nf-core pipelines can be configured to work with different execution environments, resource requirements, and infrastructure constraints.

• Configuration options: Detailed guidance on configuring options
• System requirements: Guidance on configuring pipelines to match your system’s capabilities, including resource allocation, executors, and tool arguments

Reference data

Many nf-core pipelines require reference genomes and annotation files. Learn how to access and manage reference data efficiently.

• Reference genomes: Approaches for managing reference genomes, including AWS iGenomes, custom genome files, and Refgenie

Running pipelines offline

For systems without internet access, nf-core provides solutions for preparing and transferring all required components.

• Running pipelines offline: Guidance on preparing and running nf-core pipelines on systems without internet access, including transferring pipeline code, containers, and reference data

Advanced topics

For specialised computing environments or resource management requirements, these guides address specific challenges in pipeline execution.

• Google Colab: Guidance on running nf-core pipelines using Google Colab’s cloud resources, addressing limitations in local computing environments
• Managing work directory growth: Strategies for managing intermediate files and work directory storage during pipeline execution