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

Abstract

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.

Multilocus marker-based delimitation of Salicornia persica and its population discrimination assisted by supervised machine learning approach

“…Moreover, the supervised learning method was employed, which outperforms other approaches. Likewise, machine learning algorithms have demonstrated their effectiveness in resolving plant taxa [ 28 – 31 , 64 ]. Higher genetic divergence was observed using the nrDNA (ETS+ITS2) dataset exhibiting four haplotypes in the studied populations.…”

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

“…Moreover, the efficiency of the utilized markers and methods depends on the sample size, as the singleton species or small sample size could lead to skewed results [21]. In our study, we had about 10 singleton species, which were considered as singletons and not independent OTUs to reduce the probability of biased identification.…”

“…Currently, there are initiatives for generating DNA barcode libraries of vascular plant flora. Completing such libraries and making them available will help fast, accurate identification, which would lead to the better conservation and utilization of native plants, particularly those used in herbal medicine [20][21][22]. In this context, a tool such as DNA barcoding could help resolve these issues and lead to the rapid and accurate identification of medicinal species.…”

“…The most conventional DNA barcode analysis method is the pairwise distance-based unsupervised Operational Taxonomic Unit (OTU) picking method, where Automated Barcode Gap Discovery (ABGD) is the most widely used tool, followed by the recently developed Assemble Species by Automatic Partitioning (ASAP) [50,51]. Comparatively, some studies have shown a higher rate of species discrimination using the supervised learning approach [21,[52][53][54][55]. Thus, in this study, we used a comparative approach of implementing both unsupervised and supervised methods for species delimitation.…”

“…Since 2018, the Sharjah Seed Bank and Herbarium have engaged in the process of DNA barcoding the entire UAE flora [20][21][22]. Here, we assessed the capability of plastid DNA barcode markers rbcL and matK, and a nuclear marker, ITS2, for the identification of 20 medicinally important plant species belonging to the order Caryophyllales.…”