New software tools, databases, and resources in metabolomics: updates from 2020

Abstract

Background Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. Aim of the review In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. Key scientific concepts of review In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

“…MS is more sensitive and can detect a wider range of metabolites but is less reproducible compared to NMR. Although MS has been criticized to provide less information on chemical identity than NMR, MS fragmentation as well as recent advancements in bioinformatic methods for the automatic annotation of chromatographic-mass spectral data can provide some structural information [77][78][79][80]. The advantages and disadvantages of NMR and MS have been reviewed previously [66] and are briefly described below.…”

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“…These steps are dependent on the instrument (e.g., GC-MS, LC-MS, NMR) used to acquire the data. For MS-based instrumentation, if vendor-specific software is not used, data files need to be converted to an open file format (e.g., mzML, netCDF, ABF) to be further processed with open-source software packages, such as MS-DIAL, XCMS, MZmine, and OpenMS [ 106 , 107 ]. The software can be used to perform peak picking, deconvolution, alignment across samples, and in some cases, the same software can perform metabolite annotation with experimental and/or in silico libraries (e.g., MS-DIAL [ 108 ]).…”

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