The metaRbolomics Toolbox in Bioconductor and beyond
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
Stanstrup, J. et al. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites (2019)
doi:10.3390/metabo9100200
Quantitative Fragmentation Model for Bottom-up Shotgun Lipidomics
Abstract
Quantitative bottom-up shotgun lipidomics relies on molecular species-specific “signature” fragments consistently detectable in MS/MS spectra of analytes and standards. Molecular species of glycerophospholipids are typically quantified using carboxylate fragments of their fatty acid moieties produced by higher-energy collisional dissociation of their molecular anions. However, employing standards whose fatty acids moieties are similar, yet not identical to the target lipids could severely compromise their quantification. We developed a generic and portable fragmentation model implemented in the open-source LipidXte software that harmonizes the abundances of carboxylate anion fragments originating from fatty acid moieties having different sn-1/2 position at the glycerol backbone, length of the hydrocarbon chain, number and location of double bonds. The post-acquisition adjustment enables unbiased absolute (molar) quantification of glycerophospholipid species independent of instrument settings, collision energy, and employed internal standards.
Schuhmann, K. et al. Quantitative Fragmentation Model for Bottom-up Shotgun Lipidomics. Analytical Chemistry (2019)
doi:10.1021/acs.analchem.9b03270
Lipidomics needs more standardization
Abstract
Modern mass spectrometric technologies provide quantitative readouts for a wide variety of lipid specimens. However, many studies do not report absolute lipid concentrations and differ vastly in methodologies, workflows and data presentation. Therefore, we encourage researchers to engage with the Lipidomics Standards Initiative to develop common standards for minimum acceptable data quality and reporting for lipidomics data, to take lipidomics research to the next level.
Lipidomics Standards Initiative Consortium Lipidomics needs more standardization. Nature Metabolism (2019)
doi:10.1038/s42255-019-0094-z
mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics
Abstract
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 re-analysis 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 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), as well as 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.
Hoffmann, N. et al. mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Analytical Chemistry (2019)
doi:10.1021/acs.analchem.8b04310
- Identification of key lipids critical for platelet activation by comprehensive analysis of the platelet lipidome
- Lipidomes of lung cancer and tumour-free lung tissues reveal distinct molecular signatures for cancer differentiation, age, inflammation, and pulmonary emphysema
- Lipidomics informatics for life-science
- Shotgun Lipidomics Approach for Clinical Samples
