Back

A sample-to-identification workflow to monitor animal and plant biodiversity using metabarcoding and real time analysis

Wange, L. E.; Prado-Martinez, J.; Alentorn-Moron, P.; Marques-Bonet, T.; Juan, D.; Lizano, E.

2026-10-09 ecology
10.64898/2026.10.07.756996 bioRxiv
Show abstract

Environmental DNA (eDNA) has emerged as a transformative tool for biodiversity monitoring, offering a non-invasive means to detect and identify species across various ecosystems. However, despite its potential, current eDNA methodologies have significant limitations, including limited sensitivity, restriction to a specific taxonomic group, lengthy processing times, the need for specialized laboratory conditions and equipment, as well as computational bottlenecks in equipment, connectivity, and algorithms. Overcoming these limitations is crucial for the real-time monitoring of a high number of taxa in field-based scenarios. Here we present an integrated eDNA workflow from extraction to data analysis that mitigates several of these challenges through several technical innovations. Our approach adapts the Tagsteady protocol to Nanopore sequencing, integrates multiplex PCR for simultaneous plant and animal detection, and introduces RTBioScan, a real-time analysis pipeline employing hierarchical basecalling and OTU-level consensus generation to achieve high-accuracy taxonomic assignments during sequencing. Using a single set of universal primers, we integrate metabarcoding and barcoding into a cohesive protocol, utilizing universal markers for both animals and plants (cytochrome oxidase 1 region, COI, and Internal Transcribed Spacer 2, ITS2). This comprehensive approach can detect and identify a broad spectrum of species, from individual organisms to entire communities in a single workflow.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.