Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice.
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ABSTRACT:
SUBMITTER: Xu F
PROVIDER: S-EPMC8266907 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
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Project description:Geographical indication (GI) is used to identify a product's origin when its characteristics or quality are a result of geographical origin, which includes agricultural products and foodstuff. Metabolomics is an "omics" technique that can support product authentication by providing a chemical fingerprint of a biological system, such as plant and plant-derived products. The main purpose of this article is to verify possible contributions of metabolomic studies to the marketing field, mainly for certified regions, through an integrative review of the literature and maps produced by VOSviewer software. The results indicate that studies based on metabolomics approaches can relate specific food attributes to the region's terroir and know-how. The evidence of this connection, marketing of GIs and metabolomics methods, is viewed as potential tool for marketing purposes (e.g., to assist communication of positive aspects and quality), and legal protection. In addition, our results provide a taxonomic categorization that can guide future marketing research involving metabolomics. Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.Supplementary informationThe online version contains supplementary material available at 10.1007/s00217-021-03782-2.
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Project description:The replication strategy of metazoan genomes is still unclear, mainly because definitive maps of replication origins are missing. High-throughput methods are based on population average and thus may exclusively identify efficient initiation sites, whereas ineffective origins go undetected. Single-molecule analyses of specific loci can detect both common and rare initiation events along the targeted regions. However, these usually concentrate on positioning individual events, which only gives an overview of the replication dynamics. Here, we computed the replication fork directionality (RFD) profiles of two large genes in different transcriptional states in chicken DT40 cells, namely untranscribed and transcribed DMD and CCSER1 expressed at WT levels or overexpressed, by aggregating hundreds of oriented replication tracks detected on individual DNA fibres stretched by molecular combing. These profiles reconstituted RFD domains composed of zones of initiation flanking a zone of termination originally observed in mammalian genomes and were highly consistent with independent population-averaging profiles generated by Okazaki fragment sequencing (OK-seq). Importantly, we demonstrate that inefficient origins do not manifest as detectable RFD shifts, explaining why dispersed initiation has remained invisible to population-based assays. Our method can both generate quantitative profiles and identify discrete events, thereby constituting a comprehensive approach to study metazoan genome replication.
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