Arboleda-Baena, C. | bioRxiv Science

bioRxiv2025

HVRLocator: A Computationally Efficient Tool for Identifying Hypervariable Regions in 16S rRNA Big Datasets

BackgroundAmplicon sequencing of the 16S rRNA gene is widely used to assess microbial diversity due to its cost-effectiveness and efficiency. However, public 16S rRNA datasets often lack standardized metadata, particularly information on the sequenced hypervariable regions or primers used, which are critical for accurate analysis and data reuse. To address this, we present the HVRLocator, a computational tool that reliably identifies sequenced hypervariable regions, enhancing metadata quality and enabling more robust large-scale microbiome studies. ResultsThe HVRLocator tool processed samples at an average rate of 0.147 per minute. Validation confirmed 100% accuracy in predicting alignment positions, correctly matching sequences to the expected primer regions based on literature. We demonstrated how to use the tool to select appropriate and comparable sequences for building a global bacterial database from V4 region amplicons of the 16S rRNA gene. Using HVRLocator, we selected 36,217 valid samples out of 45,882 runs, enabling us to identify cases where metadata incorrectly labeled sequences as targeting the V4 region. ConclusionEven when metadata is available, it can be inaccurate or misleading. HVRLocator offers a reliable and efficient method to identify the exact hypervariable sequenced region, ensuring accurate processing of large-scale 16S rRNA amplicon data. By bypassing inconsistent metadata and literature, it streamlines data curation and enhances the reliability of microbial studies, syntheses, and meta-analyses. Its use is essential for critically evaluating published data and enabling accurate and reproducible research in microbial ecology.

bioinformatics↗

bioRxiv2023

Unifying microorganisms and macrograzers in rocky shore ecological networks

Over the past decades, our understanding of the vital role microbes play in ecosystem processes has greatly expanded. However, we still have limited knowledge about how microbial communities interact with larger organisms. Many existing representations of microbial interactions are based on co-occurrence patterns, which do not provide clear insights into trophic or non-trophic relationships. In this study, we untangled trophic and non-trophic interactions between macroscopic and microscopic organisms on a marine rocky shore. Five abundant mollusk grazers were selected, and their consumptive (grazing) and non-consumptive (grazer pedal mucus) interactions with bacteria in biofilms were measured using 16S rRNA amplicon sequencing. While no significant effects on a commonly used measure of biofilm grazing (Chlorophyll-a concentration) were observed, detailed image analysis revealed that all grazers had a detrimental impact on biofilm cover. Moreover, different grazers exhibited distinct effects on various bacterial groups. Some groups, such as Rhodobacteraceae, Saprospiraceae, Flavobacteriaceae, and Halieaceae, experienced positive effects from specific grazers, while others, like Rhizobiaceae, Rhodobacteraceae, and Flavobacteriaceae were negatively affected by certain grazers. This study presents the first attempt to construct an interaction network between macroorganisms and bacteria. It demonstrates that the strength of trophic and non-trophic interactions varies significantly depending on the mollusk grazer or bacterial group involved. Notably, certain bacterial groups exhibited a generalized response, while others showed specialized responses to specific macroorganisms in trophic or non-trophic interactions. Overall, this work highlights the potential for integrating microbes into ecological networks, providing valuable insights and methodologies for quantifying interactions across Domains. This research complements the previous ecological network, showing that mollusk grazers interact not only trophically but also non-trophically with epilithic biofilms. It identifies three drivers affecting microbial community assembly, crucial for understanding macro-microorganism dynamics in intertidal systems.

ecology↗