Global Pharmacopoeia Genome Database is an integrated and mineable genomic database for traditional medicines derived from eight international pharmacopoeias.

Genomic data have demonstrated considerable traction in accelerating contemporary studies in traditional medicine. However, the lack of a uniform format and dispersed storage limits the full potential of herb genomic data. In this study, we developed a Global Pharmacopoeia Genome Database (GPGD). The database contains 34,346 records for 903 herb species from eight global pharmacopoeias (Brazilian, Egyptian, European, Indian, Japanese, Korean, the Pharmacopoeia of the People's Republic of China, and U.S. Pharmacopoeia's Herbal Medicines Compendium). In particular, the GPGD contains 21,872 DNA barcodes from 867 species, 2,203 organelle genomes from 674 species, 55 whole genomes from 49 species, 534 genomic sequencing datasets from 366 species, and 9,682 transcriptome datasets from 350 species. Among the organelle genomes, 534 genomes from 366 species were newly generated in this study. Whole genomes, organelle genomes, genomic fragments, transcriptomes, and DNA barcodes were uniformly formatted and arranged by species. The GPGD is publicly accessible at http://www.gpgenome.com and serves as an essential resource for species identification, decomposition of biosynthetic pathways, and molecular-assisted breeding analysis. Thus, the database is an invaluable resource for future studies on herbal medicine safety, drug discovery, and the protection and rational use of herbal resources.

Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources.

In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.

Establishing a genomic database for the medicinal plants in the Brazilian Pharmacopoeia.

BACKGROUND: Brazil is exceptionally abundant in medicinal plant resources and has a rich ethnopharmacological history. Brazilian Pharmacopoeia (BP) acts as a national standard that regulates drug quality and has six published editions. Recent genomic approaches have led to a resurgence of interest in herbal drugs. The genomic data of plants has been used for pharmaceutical applications, protecting natural resources, and efficiently regulating the market. However, there are few genomic databases specifically for medicinal plants, and the establishment of a database that focuses on the herbs contained in the BP is urgently required. METHODS: The medicinal plant species included in each edition of the BP were analyzed to understand the evolution of the Brazilian herbal drugs. The data of 82 plants in the BP were collected and categorized into four sections: DNA barcodes, super-barcodes, genomes, and sequencing data. A typical web server architecture pattern was used to build the database and website. Furthermore, the cp-Gs of the Aloe genus in the database were analyzed as an illustration. RESULTS: A new database, the Brazilian Pharmacopoeia Genomic Database (BPGD) was constructed and is now publicly accessible. A BLAST server for species identification and sequence searching with the internal transcribed spacer 2 (ITS2), the intergenic region (psbA-trnH), and the chloroplast genome (cp-G) of Brazilian medicinal plants was also embedded in the BPGD. The database has 753 ITS2 of 76 species, 553 psbA-trnH and 190 genomes (whole genome and chloroplast genome) of 57 species. In addition, it contains 37 genome sequence data sets of 24 species and 616 transcriptome sequence data sets of 34 species and also includes 187 cp-Gs representing 57 medicinal species in the BP. Analyses of the six cp-Gs of three Aloe species identified the variable regions in the cp-Gs. These can be used to identify species and understand the intraspecific relationships. CONCLUSIONS: This study presents the first genomic database of medicinal plants listed in the latest BP. It serves as an efficient platform to obtain and analyze genomic data, accelerate studies regarding Brazilian medicinal plants and facilitate the rational development on their market regulation.