Advancing plant DNA barcoding: integrating chloroplast genome sequencing, cryptic diversity discovery and machine...

Area of Science:

• Botany
• Molecular Biology
• Bioinformatics

Background:

• Accurate plant species identification is crucial for taxonomy, conservation, and resource management.
• Traditional morphology methods face challenges with cryptic or closely related species.
• DNA barcoding offers a standardized molecular approach, but resolution limits exist for certain plant lineages.

Purpose of the Study:

• To review recent advancements in DNA barcoding for plant species identification.
• To explore the impact of high-throughput sequencing and machine learning on plant identification.
• To outline future directions for genome-aware plant identification systems.

Main Methods:

• Review of literature on DNA barcoding, plastome sequencing, and machine learning in plant identification.
• Synthesis of data on traditional markers (rbcL, matK, ITS, ITS2) and emerging techniques.
• Analysis of multi-locus barcoding, metabarcoding, and environmental DNA (eDNA) approaches.

Main Results:

• Plastome sequencing and "super-barcoding" significantly enhance discriminatory power.
• Multi-locus barcoding, metabarcoding, and eDNA reveal cryptic diversity and community structures.
• Machine learning improves classification accuracy, library curation, and species delimitation.

Conclusions:

• Advanced sequencing and computational methods are revolutionizing plant identification.
• Expanding and curating reference libraries is essential for reliable systems.
• Genome-aware approaches are key for scalable and accurate plant identification in the genomic era.