Prospects of genomic resources available at the global databases for the flora of United Arab Emirates

DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates

The need for herbal medicinal plants is steadily increasing. Hence, the accurate identification of plant material has become vital for safe usage, avoiding adulteration, and medicinal plant trading. DNA barcoding has shown to be a valuable molecular identification tool for medicinal plants, ensuring the safety and efficacy of plant materials of therapeutic significance. Using morphological characters in genera with closely related species, species delimitation is often difficult. Here, we evaluated the capability of the nuclear barcode ITS2 and plastid DNA barcodes rbcL and matK to identify 20 medicinally important plant species of Caryophyllales. In our analysis, we applied an integrative approach for species discrimination using pairwise distance-based unsupervised operational taxonomic unit “OTU picking” methods, viz., ABGD (Automated Barcode Gap Analysis) and ASAP (Assemble Species by Automatic Partitioning). Along with the unsupervised OTU picking methods, Supervised Machine Learning methods (SML) were also implemented to recognize divergent taxa. Our results indicated that ITS2 was more successful in distinguishing between examined species, implying that it could be used to detect the contamination and adulteration of these medicinally important plants. Moreover, this study suggests that the combination of more than one method could assist in the resolution of morphologically similar or closely related taxa.

Evaluation of Arabian Vascular Plant Barcodes (rbcL and matK): Precision of Unsupervised and Supervised Learning Methods towards Accurate Identification

Arabia is the largest peninsula in the world, with >3000 species of vascular plants. Not much effort has been made to generate a multi-locus marker barcode library to identify and discriminate the recorded plant species. This study aimed to determine the reliability of the available Arabian plant barcodes (>1500; rbcL and matK) at the public repository (NCBI GenBank) using the unsupervised and supervised methods. Comparative analysis was carried out with the standard dataset (FINBOL) to assess the methods and markers' reliability. Our analysis suggests that from the unsupervised method, TaxonDNA's All Species Barcode criterion (ASB) exhibits the highest accuracy for rbcL barcodes, followed by the matK barcodes using the aligned dataset (FINBOL). However, for the Arabian plant barcode dataset (GBMA), the supervised method performed better than the unsupervised method, where the Random Forest and K-Nearest Neighbor (gappy kernel) classifiers were robust enough. These classifiers successfully recognized true species from both barcode markers belonging to the aligned and alignment-free datasets, respectively. The multi-class classifier showed high species resolution following the two classifiers, though its performance declined when employed to recognize true species. Similar results were observed for the FINBOL dataset through the supervised learning approach; overall, matK marker showed higher accuracy than rbcL. However, the lower rate of species identification in matK in GBMA data could be due to the higher evolutionary rate or gaps and missing data, as observed for the ASB criterion in the FINBOL dataset. Further, a lower number of sequences and singletons could also affect the rate of species resolution, as observed in the GBMA dataset. The GBMA dataset lacks sufficient species membership. We would encourage the taxonomists from the Arabian Peninsula to join our campaign on the Arabian Barcode of Life at the Barcode of Life Data (BOLD) systems. Our efforts together could help improve the rate of species identification for the Arabian Vascular plants.