上海交通大学学报(医学版) ›› 2018, Vol. 38 ›› Issue (7): 805-.doi: 10.3969/j.issn.1674-8115.2018.07.017

• 综述 • 上一篇    下一篇

代谢组学全功能软件研究进展

梁丹丹,李忆涛,郑晓皎,陈天璐   

  1. 上海交通大学附属第六人民医院转化医学中心,上海 200233
  • 出版日期:2018-07-28 发布日期:2018-07-30
  • 通讯作者: 陈天璐,电子信箱:chentianlu@sjtu.edu.cn。
  • 作者简介:梁丹丹(1992—),女,硕士生;电子信箱:dandanliang2020@sjtu.edu.cn。
  • 基金资助:
    国家自然科学基金(31501079,31500954,81772530)

Advance in full-functional software of metabolomics

LIANG Dan-dan, LI Yi-tao, ZHENG Xiao-jiao, CHEN Tian-lu   

  1. Center for Translational Medicine, Shanghai Sixth Peoples Hospital, Shanghai Jiao Tong University, Shanghai 200233, China
  • Online:2018-07-28 Published:2018-07-30
  • Supported by:
    National Natural Science Foundation of China, 31501079, 31500954, 81772530

摘要: 高通量代谢组学研究的一大难点在于数据的处理和分析。为分析和处理此类数据,各种生物信息学工具应运而生。这些工具能够对复杂高维的数据集进行预处理、物质鉴定、统计分析和结果解释等。该文以代谢组学数据处理与分析的整体流程和方法为基础,对现阶段一些常用的代谢组学全功能软件进行归纳整理,并对4种代表性软件作详细介绍和对比,为代谢组学研究提供工具选择和方法学参考。

关键词: 代谢组学, 数据处理, 统计分析, 全功能软件

Abstract:

Data processing and analysis has presented major bottlenecks in high-throughput metabolomics research. Bioinformatics tools emerged for performing these high-throughput datasets. These tools can preprocess complex high-dimensional datasets, detect and annotate metabolites, perform various statistical analysis and results interpretation. According to the workflow and methods of metabolomics data processing and analysis, this paper summarized some integral metabolomics softwares and compared the merit and demerit of four typical tools so that it can provide users with a reference guide to softwares.

Key words: metabolomics, data processing, statistical analysis, integrated software

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