Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates

Area of Science:

• Ecology
• Molecular Biology
• Bioinformatics

Background:

• DNA metabarcoding offers a cost-effective approach for biodiversity monitoring.
• Accurate diversity estimates are crucial but challenging to obtain and validate.
• Existing methods for processing high-throughput sequencing data can introduce errors.

Purpose of the Study:

• To introduce and validate the LULU method for removing erroneous molecular operational taxonomic units (OTUs) from DNA metabarcoding data.
• To assess the impact of LULU curation on biodiversity metrics using a robust validation dataset.
• To provide a reliable tool for improving biodiversity estimation in ecological studies.

Main Methods:

• Developed the LULU method, which combines sequence similarity and co-occurrence patterns to identify and remove erroneous OTUs.
• Utilized a unique dataset pairing high-quality vascular plant field survey data with ITS2 metabarcoding data from soil samples across 130 Danish sites.
• Generated OTU tables using multiple algorithms and subsequently curated them with LULU for validation against field data.

Main Results:

• LULU curation consistently improved alpha-diversity estimates and other biodiversity metrics compared to uncurated data.
• The method demonstrated effectiveness across different OTU definition algorithms.
• Validation against field survey data confirmed the enhanced accuracy of LULU-curated metabarcoding data.

Conclusions:

• The LULU method provides a reliable approach for error removal in DNA metabarcoding data.
• It significantly enhances the accuracy of biodiversity estimates without requiring a sequence reference database.
• LULU represents a promising tool for advancing cost-effective and reliable biodiversity monitoring.