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.