Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop
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J. Am. Soc. Mass Spectrom. (2019) DOI: 10.1007/s13361-019-02295-3
CRITICAL INSIGHT
Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop Erin S. Baker,1
Gary J. Patti2
1
Department of Chemistry, North Carolina State University, Raleigh, NC, USA Departments of Chemistry and Medicine, Washington University in St. Louis, St. Louis, MO, USA
2
Abstract. 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. Keywords: Metabolomics, Informatics, ASMS Fall Workshop, Metabolism Received: 23 February 2019/Revised: 17 July 2019/Accepted: 17 July 2019
W
ith recent advances in instrumentation, it has become routine to acquire high-quality LC-/MS-based metabolomic data [1, 2]. Accordingly, the field has grown exponentially and the number of facilities offering metabolomic services continues to rise. At this time, the technology is readily available to most interested researchers at relatively affordable costs around the world. Following the path of its “omic” predecessors, metabolomics is now in high demand among both technological specialists as well as biologists and clinicians who see the power of its application. Despite the increasing amount of untargeted metabolomic data being acquired, the ability to process and interpret the data is still severely limited. It is typical to detect thousands of metabolomic features in liquid chromatography/mass spectrometry (LC/MS) experiments performed on biological samples [3, 4]. Yet, at this time, only a small fraction of these features can typically be
Correspondence to: Erin Baker; e-mail: [email protected], Gary Patti; e-mail: [email protected]
identified with biochemical names [5]. Moreover, in most cases, the process of going from raw metabolomic data to biochemical structures is not automated. The informatic burden can require days, weeks, or even months of time and resources [6]. Even then, after extensive data analysis, there may be large numbers of “unknowns” that cannot be characterized. Thus, although many researchers now have acces
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