Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis...

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Metagenomics enables the study of microbial communities but often yields fragmented genomes (MAGs) from short-read sequencing.
• Long-read sequencing offers improved genome reconstruction but integrating both data types complicates analysis.

Purpose of the Study:

• To develop MUFFIN, a comprehensive metagenomic workflow for high-quality genome binning and annotation using both short and long reads.
• To streamline downstream analysis of microbial communities and their functions.

Main Methods:

• Developed MUFFIN, a Nextflow-based workflow integrating short and long sequencing reads.
• Implemented features for taxonomic classification, KEGG pathway prediction, and optional RNA-Seq data integration for quantification and annotation.
• Tested the workflow on twenty biogas reactor samples.

Main Results:

• MUFFIN successfully processed metagenomic data, producing high-quality bins and annotations.
• The workflow generated taxonomic classifications and predicted functional pathways for each bin.
• Functional pathway predictions were generated for the entire metagenomic sample and individual bins, with optional metatranscript annotations.

Conclusions:

• MUFFIN provides a reproducible and efficient solution for reconstructing and annotating metagenome-assembled genomes (MAGs) from hybrid sequencing data.
• The workflow facilitates in-depth analysis of microbial community structure and function.
• MUFFIN is publicly available on GitHub, promoting its use in microbial research.