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Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other 'omic' families. The toolbox is an R-based web application, and it is freely available at http://kwanjeeraw.github.io/metabox/ under the GPL-3 license., Competing Interests: The authors have declared that no competing interests exist. Majority of Dr. Dmitry Grapov’s contribution to the manuscript occurred while he was not part of CDS and instead the Principal Statistician at the University of California Davis. We have changed his affiliation to reflect this fact and he maintains his stated no conflict of interest.

Unlabelled: Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools., Availability and Implementation: Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/., Contact: ofiehn@ucdavis.edu., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)