mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics.
Nils HoffmannJoel ReinTimo SachsenbergJürgen HartlerKenneth HaugGerhard MayerOliver AlkaSaravanan DayalanJake T M PearcePhilippe Rocca-SerraDa QiMartin EisenacherYasset Perez-RiverolJuan Antonio VizcaínoReza M SalekSteffen NeumannAndrew R JonesPublished in: Analytical chemistry (2019)
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
Keyphrases
- mass spectrometry
- liquid chromatography
- electronic health record
- high resolution
- gas chromatography
- capillary electrophoresis
- high performance liquid chromatography
- data analysis
- big data
- quality improvement
- tandem mass spectrometry
- social media
- emergency department
- machine learning
- ms ms
- simultaneous determination
- adverse drug
- deep learning
- case control