Medicinal plants are important sources of bioactive specialized metabolites with significant therapeutic potential. Advances in multi-omics have accelerated the understanding of specialized metabolite biosynthesis and regulation. Genomics, transcriptomics, proteomics, and metabolomics have each contributed new insights into biosynthetic gene clusters (BGCs), metabolic pathways, and stress responses. However, single-omics approaches often fail to fully address these complex processes. Integrated multi-omics provides a holistic perspective on key regulatory networks. High-throughput sequencing and emerging technologies like single-cell and spatial omics have deepened our understanding of cell-specific and spatially resolved biosynthetic dynamics. Despite these advancements, challenges remain in managing large datasets, standardizing protocols, accounting for the dynamic nature of specialized metabolism, and effectively applying synthetic biology for sustainable specialized metabolite production. This review highlights recent progress in omics-based research on medicinal plants, discusses available bioinformatics tools, and explores future research trends aimed at leveraging integrated multi-omics to improve the medicinal quality and sustainable utilization of plant resources.

Background/Objectives: Modern sequencing technologies have transformed the identification of medicinal plant species and varieties, overcoming the limitations of traditional morphological and chemical approaches. This review explores the key DNA-based techniques, including molecular markers, DNA barcoding, and high-throughput sequencing, and their contributions to enhancing the accuracy and reliability of plant identification. Additionally, the integration of multi-omics approaches is examined to provide a comprehensive understanding of medicinal plant identity. Methods: The literature search for this review was conducted across databases such as Google Scholar, Web of Science, and PubMed, using keywords related to plant taxonomy, genomics, and biotechnology. Inclusion criteria focused on peer-reviewed studies closely related to plant identification methods and techniques that contribute significantly to the field. Results: The review highlights that while sequencing technologies offer substantial improvements, challenges such as high costs, technical expertise, and the lack of standardized protocols remain barriers to widespread adoption. Potential solutions, including AI-driven data analysis and portable sequencers, are discussed. Conclusions: This review provides a comprehensive overview of molecular techniques, their transformative impact, and future perspectives for more accurate and efficient medicinal plant identification.

Sesame (Sesamum indicum L.) is an important oilseed crop that produces abundant seed oil and has a pleasant flavor and high nutritional value. To date, several Illumina-based genome assemblies corresponding to different sesame genotypes have been published and widely used in genetic and genomic studies of sesame. However, these assemblies consistently showed low continuity with numerous gaps. Here, we reported a high-quality, reference-level sesame genome assembly by integrating PacBio high-fidelity sequencing and Hi-C technology. Our updated sesame assembly was 309.35 Mb in size with a high chromosome anchoring rate (97.54%) and contig N50 size (13.48 Mb), which were better than previously published genomes. We identified 163.38 Mb repetitive elements and 24,345 high-confidence protein-coding genes in the updated sesame assembly. Comparative genomic analysis showed that sesame shared an ancient whole-genome duplication event with two Lamiales species. A total of 2,782 genes were tandemly duplicated. We also identified several genes that were likely involved in fatty acid and triacylglycerol biosynthesis. Our improved sesame assembly and annotation will facilitate future genetic studies and genomics-assisted breeding of sesame.

The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map.