The metaRbolomics Toolbox in Bioconductor and beyond

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

The metaRbolomics Toolbox in Bioconductor and beyond

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

The metaRbolomics Toolbox in Bioconductor and beyond

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

PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org, a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase

Prediction of retention time for plant food compounds & metabolites in a multi-laboratory initiative

Participating laboratories: University of Eastern Finland, Kuopio, National Institute of Agricultural Research, Clermont-Ferrand; King's College, London; SzentIstván Egyetem, Budapest, Flanders Research Institute for Agriculture Fisheries and Food, Bruxelles,Institute of Microbiology of the CAS, Prague, Quadram Institute,Norwich; Teagasc, Dublin; University of Barcelona, CEBAS-CSIC, Murcia, ICTAN-CSIC, Madrid, Instituto de Tecnologia Química e Biológica, Lisbon, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Norwegian Institute of Food, Fisheries and Aquaculture Research, As, Edmund Mach Foundation, San Michele all'Adige, University of Lisbon, University of Copenhagen, University of Parma.Plant food bioactives (flavonoids, phenolic acids, lignans, carotenoids, monoterpenes, glucosinolates, alkaloids…) receive widespread interest for their protective health effects. However, their identification in untargeted metabolomic profiles of food, biofluids and tissues remains a challenging feat. Plant food bioactives and their Phase I, -II and gut microbial metabolites cover a large chemical space ranging from highly polar to lipophilic compounds and including aglycones, glycosides, and conjugated metabolites. Spectral libraries are incomplete for these compounds and standards are often costly or not commercially available. In addition to mass fragmentation data, retention time (RT) is a valuable information for assisting the identification of unknowns, as it helps to narrow the number of hypotheses within an observed RT window to a manageable number of compounds.In the framework of the COST Action POSITIVe (https://www6.inra.fr/cost-positive, FA1403), we evaluated the usefulness of PredRet (http://predret.org), an open access RT database, to predict RT of plant food bioactive metabolites in a multi-laboratory test involving 19 laboratories across Europe, using 24 reversed-phase LC-MS or LC-PDA Chromatographic Systems (CS=column + elution phases and gradient). PredRet is a community-driven database of compound RTs that is free to use to predict in your own CS the RT of compounds that have beenexperimentally measured in other CS