SUMMARY: Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis. AVAILABILITY AND IMPLEMENTATION: The msFeaST workflow is freely available through https://github.com/kevinmildau/msFeaST and built to work on MacOS and Linux systems.

MOTIVATION: Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. RESULTS: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AVAILABILITY AND IMPLEMENTATION: The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AVAILABILITY AND IMPLEMENTATION: homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

BACKGROUND: Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. RESULTS: The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. CONCLUSIONS: QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.

MOTIVATION: Liquid chromatography-mass spectrometry (LC/MS) is a key technique in metabolomics. Since the efficient assignment of MS signals to true biological metabolites becomes feasible in combination with in vivo stable isotopic labelling, our aim was to provide a new software tool for this purpose. RESULTS: An algorithm and a program (MetExtract) have been developed to search for metabolites in in vivo labelled biological samples. The algorithm makes use of the chromatographic characteristics of the LC/MS data and detects MS peaks fulfilling the criteria of stable isotopic labelling. As a result of all calculations, the algorithm specifies a list of m/z values, the corresponding number of atoms of the labelling element (e.g. carbon) together with retention time and extracted adduct-, fragment- and polymer ions. Its function was evaluated using native (12)C- and uniformly (13)C-labelled standard substances. AVAILABILITY: MetExtract is available free of charge and warranty at http://code.google.com/p/metextract/. Precompiled executables are available for Windows operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.