Dataset Information

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.

SUBMITTER: Jamdade R

PROVIDER: S-EPMC8708657 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Accurate identification of Arctic plant species is critical for understanding potential climate-induced changes in their diversity and distributions. To facilitate rapid identification we generated DNA barcodes for the core plastid barcode loci (rbcL and matK) for 490 vascular plant species, representing nearly half of the Canadian Arctic flora and 93% of the flora of the Canadian Arctic Archipelago. Sequence recovery was higher for rbcL than matK (93% and 81%), and rbcL was easier to recover than matK from herbarium specimens (92% and 77%). Distance-based and sequence-similarity analyses of combined rbcL + matK data discriminate 97% of genera, 56% of species, and 7% of infraspecific taxa. There is a significant negative correlation between the number of species sampled per genus and the percent species resolution per genus. We characterize barcode variation in detail in the ten largest genera sampled (Carex, Draba, Festuca, Pedicularis, Poa, Potentilla, Puccinellia, Ranunculus, Salix, and Saxifraga) in the context of their phylogenetic relationships and taxonomy. Discrimination with the core barcode loci in these genera ranges from 0% in Salix to 85% in Carex. Haplotype variation in multiple genera does not correspond to species boundaries, including Taraxacum, in which the distribution of plastid haplotypes among Arctic species is consistent with plastid variation documented in non-Arctic species. Introgression of Poa glauca plastid DNA into multiple individuals of P. hartzii is problematic for identification of these species with DNA barcodes. Of three supplementary barcode loci (psbA-trnH, psbK-psbI, atpF-atpH) collected for a subset of Poa and Puccinellia species, only atpF-atpH improved discrimination in Puccinellia, compared with rbcL and matK. Variation in matK in Vaccinium uliginosum and rbcL in Saxifraga oppositifolia corresponds to variation in other loci used to characterize the phylogeographic histories of these Arctic-alpine species.

Project description:Acacia species are very important tree species in tropical and subtropical countries of the World for their economic and medicinal benefits. Precise identification of Acacia is very important to distinguish the invasive species from rare species however, it is difficult to differentiate Acacia species based on morphological charcters. In addition, precise identification is also important for wood charcterization in the forest industry as these species are declining due to illegal logging and deforestation. To overcome thsese limitations of morphological identification, DNA barcoding is being used as an efficient and quick approach for precise identification of tree species. In this study, we selected two chloroplast and plastid base DNA markers (rbcL and matK) for the identification of five selected tree species of Acacia (A. albida, A. ampliceps, A. catechu, A. coriacea and A. tortilis). The genomic DNA of the selected Acacia species was extracted, amplified through PCR using specific primers and subsequently sequenced through Sanger sequencing. In matK DNA marker the average AT nucleotide contents were higher (59.46%) and GC contents were lower (40.44%) as compared to the AT (55.40%) and GC content (44.54%) in rbcL marker. The means genetic distance K2P between the Acacia species was higher in matK (0.704%) as compared to rbcL (0.230%). All Acacia species could be identified based on unique SNPs profile. Based on SNP data profiles, DNA sequence based scannable QR codes were developed for accurate identification of Acacia species. The phylogenetic analysis based on both markers (rbcL and matK) showed that both A. coriacea and A. tortilis were closely related with each other and clustered in the same group while other two species A. albida and A. catechu were grouped together. The specie A. ampliceps remained ungrouped distantly, compared with other four species. These finding highlights the potential of DNA barcoding for efficient and reproducible identification of Acacia species.

Dataset Information

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

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