Towards next‐generation biodiversity assessment using DNA metabarcoding

TLDR

DNA metabarcoding automates species identification from bulk or environmental samples, enabling high‑throughput taxon detection, but its current power is limited by PCR dependence and the need for extensive reference libraries. The study aims to eliminate the PCR step and construct comprehensive reference libraries from whole organellar genomes and repetitive ribosomal nuclear DNA using curated DNA extract collections. By leveraging advances in DNA sequencing, the authors propose building reference libraries from standardized barcoding initiatives' curated DNA extracts. The near‑term future of DNA metabarcoding has enormous potential to boost data acquisition in biodiversity research.

Abstract

Virtually all empirical ecological studies require species identification during data collection. DNA metabarcoding refers to the automated identification of multiple species from a single bulk sample containing entire organisms or from a single environmental sample containing degraded DNA (soil, water, faeces, etc.). It can be implemented for both modern and ancient environmental samples. The availability of next-generation sequencing platforms and the ecologists need for high-throughput taxon identification have facilitated the emergence of DNA metabarcoding. The potential power of DNA metabarcoding as it is implemented today is limited mainly by its dependency on PCR and by the considerable investment needed to build comprehensive taxonomic reference libraries. Further developments associated with the impressive progress in DNA sequencing will eliminate the currently required DNA amplification step, and comprehensive taxonomic reference libraries composed of whole organellar genomes and repetitive ribosomal nuclear DNA can be built based on the well-curated DNA extract collections maintained by standardized barcoding initiatives. The near-term future of DNA metabarcoding has an enormous potential to boost data acquisition in biodiversity research.