Bioinformatics Strategy for 16s and 23s rRNA Metabarcoding Data.

Abstract

Understanding biological communities is essential for elucidating ecosystem structure and function. Metabarcoding based on ribosomal RNA (rRNA) genes, particularly 16S and 23S, is widely used to characterise bacterial and microalgal communities. However, analysing high-throughput sequencing data generated by platforms such as the Illumina MiSeq remains challenging due to fragmented bioinformatics tools, complex parameterisation, and limited accessibility for non-specialist users. In this study, a comprehensive and user-friendly bioinformatics pipeline is proposed for the analysis of 16S and 23S paired-end metabarcoding data. The workflow integrates all critical processing steps, including read merging, primer and adapter trimming, quality filtering, dereplication, chimaera removal, and clustering into Operational Taxonomic Units (OTUs). Taxonomic assignment is performed using curated reference databases, namely EZBioCloud for bacterial communities and µgreen for microalgae. The pipeline was developed in Python 3.11 and incorporates validated tools such as VSEARCH and Cutadapt, ensuring robustness and computational efficiency. Additionally, modules for alpha and beta diversity analysis are included to support comprehensive ecological interpretation. The main novelty of this work lies in providing a unified, GUI-based framework that enables the standardised processing of dual-marker (16S/23S) metabarcoding data within a single environment. In its current implementation, SOMBA supports the analysis of each marker through separate but harmonised workflows, ensuring consistency in parameterisation, processing steps, and output structure. This approach provides an accessible and standardised solution that bridges the gap between raw sequencing data and reliable biological insights, supporting applications in environmental microbiology and biotechnology.