Optimization of metabolomic data processing using NOREVA
文献类型：期刊论文
| 作者 | Fu, Jianbo2; Zhang, Ying2; Wang, Yunxia2; Zhang, Hongning2; Liu, Jin2; Tang, Jing2; Yang, Qingxia2; Sun, Huaicheng2,3; Qiu, Wenqi4,5; Ma, Yinghui6 |
| 刊名 | NATURE PROTOCOLS
 |
| 出版日期 | 2021-12-24 |
| 页码 | 25 |
| ISSN号 | 1754-2189 |
| DOI | 10.1038/s41596-021-00636-9 |
| 通讯作者 | Zhu, Feng(zhufeng@zju.edu.cn) |
| 英文摘要 | A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data. Peak table processing is essential for metabolomics, but finding the best workflow is challenging. This protocol describes NOREVA, an out-of-the-box software tool that can process and evaluate thousands of workflows in a single experiment. |
| WOS关键词 | MASS-SPECTROMETRY ; NORMALIZATION ; DISCOVERY ; MICROBIOME ; SOFTWARE ; WORKFLOW ; PATHWAY ; QUALITY ; TOOLS |
| 资助项目 | Natural Science Foundation of Zhejiang Province[LR21H300001] ; National Natural Science Foundation of China[81872798] ; National Natural Science Foundation of China[U1909208] ; Leading Talent of the `Ten Thousand Plan'-National High-Level Talents Special Support Plan of China ; Fundamental Research Fund for Central Universities[2018QNA7023] ; `Double Top-Class' University Project[181201*194232101] ; Key R&D Program of Zhejiang Province[2020C03010] ; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare ; Information Technology Center of Zhejiang University ; Alibaba Cloud |
| WOS研究方向 | Biochemistry & Molecular Biology |
| 语种 | 英语 |
| 出版者 | NATURE PORTFOLIO |
| WOS记录号 | WOS:000734014300001 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/300824]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Zhu, Feng |
| 作者单位 | 1.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai, Peoples R China 2.Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou, Peoples R China 3.Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Alibaba Zhejiang Univ Joint Res Ctr Future Digita, Hangzhou, Peoples R China 4.Univ Hong Kong, HKU SZH, Dept Surg, Hong Kong, Peoples R China 5.Univ Hong Kong, Fac Med, Hong Kong, Peoples R China 6.Jiangsu Univ Sci & Technol, Sch Econ & Management, Zhenjiang, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fu, Jianbo,Zhang, Ying,Wang, Yunxia,et al. Optimization of metabolomic data processing using NOREVA[J]. NATURE PROTOCOLS,2021:25. |
| APA | Fu, Jianbo.,Zhang, Ying.,Wang, Yunxia.,Zhang, Hongning.,Liu, Jin.,...&Zhu, Feng.(2021).Optimization of metabolomic data processing using NOREVA.NATURE PROTOCOLS,25. |
| MLA | Fu, Jianbo,et al."Optimization of metabolomic data processing using NOREVA".NATURE PROTOCOLS (2021):25. |
入库方式： OAI收割
来源：上海药物研究所
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