metaFun: An analysis pipeline for metagenomic big data with fast and unified functional searches

Abstract

Metagenomic approaches offer unprecedented opportunities to characterize microbial community structure and function, yet several challenges remain unresolved. Inconsistent genome quality impairs reliability of metagenome-assembled genomes, lack of unified taxonomic criteria limits cross-study comparability, and multi-step workflows involving numerous programs and parameters hinder reproducibility and accessibility. We benchmarked existing programs and parameters using simulated metagenomic data to identify optimal configurations. metaFun is an open-source, end-to-end pipeline that integrates quality control, taxonomic profiling, functional profiling, de novo assembly, binning, genome assessment, comparative genomic analysis, pangenome annotation, network analysis, and strain-level microdiversity analysis into a unified framework. Interactive modules support standardized data interpretation and exploratory visualization. The pipeline is implemented with Nextflow and containerized with Apptainer, ensuring environment reproducibility and scalability. Comprehensive documentation is available at https://metafun-doc.readthedocs.io/en/main. The pipeline was validated using a colorectal cancer cohort dataset. By addressing key methodological gaps, metaFun facilitates accessible and reproducible metagenomic analysis for the broader research community.