Bioinformatics Strategy for 16s and 23s rRNA Metabarcoding Data

Area of Science:

• Microbiology
• Bioinformatics
• Ecology

Background:

• Metabarcoding using ribosomal RNA (rRNA) genes (16S and 23S) is crucial for studying bacterial and microalgal communities.
• Analyzing high-throughput sequencing data is complex due to fragmented tools and limited user accessibility.

Purpose of the Study:

• To develop a user-friendly, comprehensive bioinformatics pipeline for analyzing 16S and 23S paired-end metabarcoding data.
• To provide a standardized and accessible solution for processing dual-marker metabarcoding data.

Main Methods:

• Developed a Python 3.11 pipeline integrating read merging, trimming, quality filtering, dereplication, chimaera removal, and Operational Taxonomic Unit (OTU) clustering.
• Utilized VSEARCH and Cutadapt tools for robustness and efficiency.
• Incorporated taxonomic assignment using EZBioCloud and µgreen databases.
• Included modules for alpha and beta diversity analysis.

Main Results:

• The pipeline, SOMBA, offers a unified, GUI-based framework for standardized processing of 16S/23S metabarcoding data.
• Ensures consistency in parameterization, processing steps, and output structure across separate but harmonized workflows for each marker.
• Facilitates robust and computationally efficient analysis.

Conclusions:

• SOMBA provides an accessible and standardized solution for transforming raw sequencing data into reliable biological insights.
• Supports applications in environmental microbiology and biotechnology by bridging the gap for non-specialist users.
• Enables comprehensive ecological interpretation through integrated diversity analyses.