Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
Departments of Chemistry and Medicine, Washington University in St. Louis, St. Louis, MO, USA.
J Am Soc Mass Spectrom. 2019 Oct;30(10):2031-2036. doi: 10.1007/s13361-019-02295-3. Epub 2019 Aug 22.
In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.
2018 年 11 月，美国质谱学会主办了代谢组学信息学方法年度秋季研讨会。该研讨会包括 12 位特邀演讲者的 16 场讲座。演讲的重点是非靶向代谢组学与液相色谱/质谱联用。在这篇综述中，我们重点介绍了多位演讲者提到的五个反复出现的主题：（i）数据共享，（ii）伪影和污染物，（iii）特征退化，（iv）数据库组织，以及（v）代谢物鉴定的要求。我们的目的是展示与会者普遍认同的观点，以及表达不同意见的观点。我们注意到，大多数演讲者使用了不同的数据处理软件，这突显了目前在代谢组学中使用的信息学程序的多样性。最后，我们对参考数据集在标准化代谢组学数据处理方法方面的潜在作用进行了思考。
