How to Use the Command 'nf-core' (with examples)

The ’nf-core’ command provides a powerful suite of tools created to facilitate the development, execution, and management of best-practice pipelines using the Nextflow framework. This suite is particularly useful for bioinformaticians, computational biologists, and researchers looking to streamline the analysis of high-throughput data with pre-designed, reusable pipelines.

Use case 1: Listing Existing Pipelines on nf-core

Code:

nf-core list

Motivation:

Listing the available pipelines is a fundamental step when working with nf-core. It allows users to identify which pipelines can be utilized for their specific analysis needs without creating a new one from scratch. This feature is especially useful for researchers and bioinformaticians who need to quickly assess the resources available for various types of data analyses.

Explanation:

The command nf-core list involves no additional arguments. It is designed to provide a catalog of all the nf-core pipelines currently available in the repository. Running this command accesses the nf-core database online and retrieves up-to-date information about each pipeline.

Example output:

Found 50 pipelines
-----------------------------------------------
Name                  Description           Version
-----------------------------------------------
nf-core/rnaseq        RNA-seq pipeline      1.4
nf-core/htseq         HTSeq pipeline        1.3
...

Use case 2: Creating a New Pipeline Skeleton

Code:

nf-core create

Motivation:

Creating a new pipeline skeleton is pivotal for developers intending to build custom pipelines tailored to their specific analytic needs while adhering to nf-core standards. This streamlined process provides a template structure that ensures best practices are automatically implemented, saving time and reducing errors in pipeline configuration.

Explanation:

The nf-core create command generates a new pipeline directory with all the necessary files and folders in place according to nf-core guidelines. No additional arguments are needed, but you may be prompted to enter data interactively, like the pipeline name and description.

Example output:

Creating new nf-core pipeline
Pipeline name: custom-pipeline
...
Directory structure created at ./custom-pipeline/

Use case 3: Linting the Pipeline Code

Code:

nf-core lint path/to/directory

Motivation:

Linting is essential in identifying and rectifying inconsistencies and errors in pipeline code before execution. The nf-core lint command ensures that your pipeline meets nf-core standards, enhancing its reliability and compatibility in diverse computational environments.

Explanation:

In this command, nf-core lint is followed by the path to the directory containing the pipeline files. The tool then assesses the pipeline against a series of checks and validations to ensure it adheres to the necessary guidelines.

Example output:

Running pipeline tests...
✔ 8 tests passed
✗ 2 warnings
✗ 1 error
...

Use case 4: Bumping Software Versions in Pipeline Recipe

Code:

nf-core bump-version path/to/directory new_version

Motivation:

Updating software versions within a pipeline is critical to ensure compatibility with the latest tools and features, as well as to patch known bugs. The nf-core bump-version command simplifies the process of updating software dependencies directly in the pipeline’s configuration files.

Explanation:

• path/to/directory: This argument specifies the location of the pipeline’s configuration files that need updating.
• new_version: Here, users specify the new version number they wish to update the pipeline to.

Example output:

Bumping software version in ./pipeline-config/
Updated version from 1.0 to 1.1

Use case 5: Launching an nf-core Pipeline

Code:

nf-core launch pipeline_name

Motivation:

Launching a pipeline is the critical phase where data analysis is performed. The nf-core launch command simplifies the execution process, allowing researchers to run pipelines without manually configuring complex options. This command is crucial for executing standardized analyses reproducibly and efficiently.

Explanation:

• pipeline_name: Here, the user specifies the name of the pipeline from the nf-core repository they wish to launch.

Example output:

Launching nf-core/rnaseq
Pipeline started successfully

Use case 6: Downloading an nf-core Pipeline for Offline Use

Code:

nf-core download pipeline_name

Motivation:

Downloading a pipeline for offline use is advantageous when working in environments with limited or no internet connectivity. It ensures that users have all the necessary pipeline resources at their disposal without requiring continuous online access.

Explanation:

• pipeline_name: This argument refers to the specific nf-core pipeline that the user wants to download for offline use, ensuring that all dependencies and configurations are accessible locally.

Example output:

Downloading nf-core/htseq
Download completed: htseq-1.3.tar.gz available for offline execution

Conclusion:

The nf-core command and its associated tools provide a structured and efficient framework for managing bioinformatics workflows using Nextflow. By facilitating the creation, validation, and execution of best-practice pipelines, nf-core empowers users to deploy sophisticated analyses with ease and confidence in diverse computational contexts.