Motivation: The rapid advances in metabolomics pose a significant challenge in presentation and interpretation of results. Development of new, engaging visual aids is crucial to advancing our understanding of new findings. Results: We have developed MetaboCraft, a Minecraft plugin which creates immersive visualizations of metabolic networks and pathways in a 3D environment and allows the results of user experiments to be viewed in this context, presenting a novel approach to exploring the metabolome. Availability and implementation: https://github.com/argymeg/MetaboCraft/; https://hub.docker.com/r/ronandaly/metabocraft/. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by 'evidence cards', which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information. AVAILABILITY AND IMPLEMENTATION: PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50 GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The use of liquid chromatography coupled to mass spectrometry has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This article looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite. RESULTS: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation. The results inherently contain a quantitative estimate of confidence in the peak annotations and allow an accurate trade-off between precision and recall. Extensive validation experiments using authentic chemical standards show that this system is able to produce more accurate putative identifications than other state-of-the-art systems, while at the same time giving a probabilistic measure of confidence in the annotations. AVAILABILITY AND IMPLEMENTATION: The software has been implemented as part of the mzMatch metabolomics analysis pipeline, which is available for download at http://mzmatch.sourceforge.net/.

SUMMARY: The application of emerging metabolomics technologies to the comprehensive investigation of cellular biochemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabolite identification. IDEOM provides a user-friendly data processing application that automates filtering and identification of metabolite peaks, paying particular attention to common sources of noise and false identifications generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Building on advanced processing tools such as mzMatch and XCMS, it allows users to run a comprehensive pipeline for data analysis and visualization from a graphical user interface within Microsoft Excel, a familiar program for most biological scientists. AVAILABILITY AND IMPLEMENTATION: IDEOM is provided free of charge at http://mzmatch.sourceforge.net/ideom.html, as a macro-enabled spreadsheet (.xlsb). Implementation requires Microsoft Excel (2007 or later). R is also required for full functionality. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.