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Yang X, Kui L, Tang M, Li D, Wei K, Chen W, Miao J, Dong Y. High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery. Front Genet 2020; 11:19. [PMID: 32117438 PMCID: PMC7013098 DOI: 10.3389/fgene.2020.00019] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/07/2020] [Indexed: 01/26/2023] Open
Abstract
The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.
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Affiliation(s)
- Xiaonan Yang
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Ling Kui
- Dana-Farber Cancer Institute, Harvard Medical School, Brookline, MA, United States
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, China
| | - Dawei Li
- College of Biological Big Data, Yunnan Agricultural University, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming, China
| | - Kunhua Wei
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants, Nanning, China.,School of Pharmacy, Guangxi Medical University, Nanning, China
| | - Wei Chen
- College of Biological Big Data, Yunnan Agricultural University, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming, China
| | - Jianhua Miao
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants, Nanning, China.,School of Pharmacy, Guangxi Medical University, Nanning, China
| | - Yang Dong
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants, Nanning, China.,College of Biological Big Data, Yunnan Agricultural University, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming, China
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