Untargeted Metabolomics Experiments Research Articles

MicrobeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms’ role in ecology and human health.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation.

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

Transcriptomics and Metabolomics Reveal Tomato Consumption Alters Hepatic Xenobiotic Metabolism and Induces Steroidal Alkaloid Metabolite Accumulation in Mice.

Tomato consumption is associated with many health benefits including lowered risk for developing certain cancers. It is hypothesized that tomato phytochemicals are transported to the liver and other tissues where they alter gene expression in ways that lead to favorable health outcomes. However, the effects of tomato consumption on mammalian liver gene expression and chemical profile are not well defined. The study hypothesizes that tomato consumption would alter mouse liver transcriptomes and metabolomes compared to a control diet. C57BL/6J mice (n = 11-12/group) are fed a macronutrient matched diet containing either 10% red tomato, 10% tangerine tomato, or no tomato powder for 6 weeks after weaning. RNA-Seq followed by gene set enrichment analyses indicates that tomato type and consumption, in general, altered expression of phase I and II xenobiotic metabolism genes. Untargeted metabolomics experiments reveal distinct clustering between control and tomato fed animals. Nineteen molecular formulas (representing 75 chemical features) are identified or tentatively identified as steroidal alkaloids and isomers of their phase I and II metabolites; many of which are reported for the first time in mammals. These data together suggest tomato consumption may impart benefits partly through enhancing detoxification potential.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or “suspects,” were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Improved quantification of carbonyl sub-metabolome by liquid chromatography mass spectrometry using a fragment controlled multiplexed isotopic tag

BackgroundCarbonyl-containing metabolites are a class of key intermediate in metabolism, which has potentials to be biomarkers. Since their poor ionization, derivatization reagents, such as dansylhydrazine, are usually used to improve the sensitivity and/or to facilitate quantification. However, most current carbonyl derivatization reagents only have two channels, one is isotopically labeled and the other one is non-labeled. To quantify more samples in a run and using data-independent acquisition (DIA) mode to get comprehensive and unbiased mass fragmentation, we proposed a fragment-controlled isotopic tag, called DiMe-FP-NHNH2 (FP) which has five channels: Δ0, Δ3, Δ6, Δ9, and Δ12, thus up to 5 samples can be analyzed in a run. ResultsThe most important improvement is that the FP tag can produce multiple characteristic signals in tandem mass, diagnostic ions and neutral losses, which helps to selectively detect aldehydes/ketones for targeted and untargeted analysis. To exhibit all capabilities of the FP tag, we mimicked an untargeted metabolomics experiment, which comprises two steps. First, discovery step, using Data-Independent Analysis (SWATH-MS) and the labeling of two channels (Δ0 and Δ3), we picked out aldehyde/ketone from the pooled urine samples based on three characteristic signals, including isotope patterns, diagnostic ions, and neutral losses. Second, five-plex quantification, relative and absolute quantification were achieved in a single LC-MS analysis. Notably, because of different nominal masses, the FP tag can be used on any low or high resolution mass spectrometers. SignificanceThe benefits and performance of the FP tag are demonstrated by the analysis of urine samples collected from patients from a prostate cancer study, in which more than a thousand features were found based on MS1 fingerprint, but only around 120 aldehyde/ketone candidates were confirmed with characteristic signals and nine of which were quantified showing significant differences from healthy and reference urine samples.

Reverse metabolomics for the discovery of chemical structures from humans.

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input.

We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.

IpaPy2: Integrated Probabilistic Annotation (IPA) 2.0 - an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments, that provides statistically rigorous estimates of the probabilities associated with each annotation. Here we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features, but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy. The source code, extensive documentation and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2. Supplementary data are available at Bioinformatics online.

Lian-Qu formula treats metabolic syndrome via reducing fat synthesis, insulin resistance and inflammation.

Metabolic syndrome (MetS) is a pathological condition characterized by obesity, hyperglycemia, hypertension and hyperlipidemia that increases the risk of cardiovascular disease, type 2 diabetes and non-alcoholic fatty liver disease. The traditional Chinese medicine Lian-Qu formula (LQF) is modified from Xiaoxianxiong decoction, which has been used for coronary heart disease or metabolic disease in clinical for a long time. However, the pharmacological mechanism of LQF on MetS is unclear. Here, we explored the actions of LQF on MetS via network pharmacology and validated the mechanism in the MetS mice. The chemical components of LQF were searched in the traditional Chinese medicine systems pharmacology database and the natural product activity & species source database. The related targets of MetS disease were gathered from genes cluster with literature profiles database. The protein-protein interaction network was constructed to obtain the key target genes. The Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment of the key targets were performed to predict the potential mechanisms of LQF action on MetS. And then, the high-fat diet-induced MetS mice were used to validate its therapeutic effect and molecular targets. Insulin tolerance test and oral glucose tolerance test were used to assess insulin sensitivity. Body weight and visceral fat index were measured to assess obesity. Liver metabolism was detected by H&E section, oil red O staining and untargeted lipid metabolomics experiments. Finally, the key targets of LQF action on MetS were verified by PCR and ELISA kits. A total of 466 components in LQF were obtained, among which 71 were active. These components correspond to 74 targets associated with MetS. The predicted targets of LQF worked on MetS were AKT1, INSR, PPARs, FASN, LDLR, TNF, CRP, IL-6, IL-1β and so on. Furthermore, these targets were related to pathways in cellular response to lipid, inflammatory response, glucose transmembrane transport and insulin resistance. Finally, the animal experiments validated that LQF inhibited lipids accumulation by inhibiting the gene expression of FASN and increasing ADPN, and it relieved insulin resistance by increasing GLUT-4 expression. Moreover, LQF alleviated inflammation by reducing IL-6 and CRP levels. LQF exerted anti-MetS effects through improving insulin sensitivity, ameliorating hyperlipidemia and obesity, reducing liver injury, and inhibiting inflammatory response.

A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

High-confidence structural annotation of metabolites absent from spectral libraries

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.

Recent Developments Toward Integrated Metabolomics Technologies (UHPLC-MS-SPE-NMR and MicroED) for Higher-Throughput Confident Metabolite Identifications.

Metabolomics has emerged as a powerful discipline to study complex biological systems from a small molecule perspective. The success of metabolomics hinges upon reliable annotations of spectral features obtained from MS and/or NMR. In spite of tremendous progress with regards to analytical instrumentation and computational tools, < 20% of spectral features are confidently identified in most untargeted metabolomics experiments. This article explores the integration of multiple analytical instruments such as UHPLC-MS/MS-SPE-NMR and the cryo-EM method MicroED to achieve large-scale and confident metabolite identifications in a higher-throughput manner. UHPLC-MS/MS-SPE allows for the simultaneous automated purification of metabolites followed by offline structure elucidation and structure validation by NMR and MicroED. Large-scale study of complex metabolomes such as that of the model plant legume Medicago truncatula can be achieved using an integrated UHPLC-MS/MS-SPE-NMR metabolomics platform. Additionally, recent developments in MicroED to study structures of small organic molecules have enabled faster, easier and precise structure determinations of metabolites. A MicroED small molecule structure elucidation workflow (e.g., crystal screening, sample preparation, data collection and data processing/structure determination) has been described. Ongoing MicroED methods development and its future scope related to structure elucidation of specialized metabolites and metabolomics are highlighted. The incorporation of MicroED with a UHPLC-MS/MS-SPE-NMR instrumental ensemble offers the potential to accelerate and achieve higher rates of metabolite identification.

MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization.

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

Tracking the development of the superficial scald disorder and effects of treatments with diphenylamine and 1-MCP using an untargeted metabolomic approach in apple fruit

Superficial scald is a physiological storage disorder that significantly reduces the marketability of apple fruit. To gain fundamental knowledge about the biochemical pathways leading to the development of the disorder and mechanisms of treatments for prevention, an untargeted metabolomics experiment employing liquid chromatography and mass spectrometry with data independent acquisition was performed. Metabolomic changes of two apple cultivars ‘Cortland’ and ‘Red Delicious’ with scald development and scald control treatments, using diphenylamine and 1-MCP, at 0–1 °C for up to 7 months was investigated. In total, 833 features/compounds were analyzed, and among them 59 were found to change significantly in controls involved in scald development, and in response to DPA and 1-MCP treatments. Our results provide new evidence that metabolites in association with phenylpropanoid metabolism, antioxidant and redox systems, and amino acid metabolism are related closely to scald development and response to potential treatments.

WITHDRAWN: Tracking development of the superficial scald disorder and effects of treatments with diphenylamine and 1-MCP using an untargeted metabolomic approach in apple fruit

Abstract Superficial scald is a physiological storage disorder that significantly reduces the marketability of apple fruit. To gain fundamental knowledge about the biochemical pathways leading to the development of the disorder and mechanisms of treatments for prevention, an untargeted metabolomics experiment employing liquid chromatography and mass spectrometry with data independent acquisition was performed. Metabolomic changes of two apple cultivars ‘Cortland’ and ‘Red Delicious’ with scald development and scald control treatments, using diphenylamine and 1-MCP, at 0–1 °C for up to 7 months was investigated. In total, 833 features/compounds were analyzed, and among them 59 were found to change significantly in controls involved in scald development, and in response to DPA and 1-MCP treatments. Our results provide new evidence that metabolites in association with phenylpropanoid metabolism, antioxidant and redox systems, and amino acid metabolism are related closely to scald development and response to potential treatments.

Abstract 4758: Identifying metabolic dark matter (unknown) with therapeutic potential in tumor suppressor deficient colorectal cancer cells

Abstract Numerous unknown features than identified metabolites are detected in untargeted metabolomics experiments. Surprisingly, over 80% of the metabolic features are currently unknown, representing a ‘black hole (dark matter)' of the metabolic output that is necessary to explore. Unfortunately, research specific to unknown identification and its role in tumor and therapeutics are largely understated. In recent studies, the chemical formula acquired from the high-resolution Mass Spectrometry (MS) detection and further targeted analysis by the Liquid Chromatography-Mass Spectrometry (LC-MS) have been proven useful in confirming the presence and functions of unknown metabolites or their isomers. Emerging evidence indicates that cancer is a metabolic disease and altered metabolism is a cancer hallmark. Genetic alterations including gene loss or gain of function mutations lead to altered metabolic regulation. Tumors frequently mutate genes in order for satisfy growth and survival. For example, tumor suppressor genes such as TP53 and APC are genes with the highest frequency of somatically acquired mutations in diverse cancers. In TCGA colorectal cancer (CRCs), p53 and APC genes show 58% and 81% loss of function mutations, respectively. Tumor suppressor genes with loss of function mutation involve in tumorigenicity of multiple cancers. Therefore, exploring unknown metabolites associated with tumor suppressors has excellent prospect for the discovery of cancer biomarker(s) and therapy. In our preliminary analysis, using LC-MS, we profiled the untargeted metabolomes between wild-type and isogenic clone of p53-KO or APC-KO HCT116 CRCs. Interestingly, we first identified a large number of significantly upregulated metabolic features in both p53-KO or APC-KO cells compared to the wild type cells. We further annotated these upregulated metabolic features using our in house metabolite library containing over 1,000 metabolite standards and identified less than 5% of known leaving over 90% unknown. To increase the metabolite annotation coverage, we then ran these unknown metabolites through open-source metabolite databases including KEGG, HMDB, METLIN, and LipidMaps and were able to annotate over 75% of unknown metabolic features. Strikingly, our data revealed 37 commonly upregulated significant metabolites in both clones and there are 48 and 127 unique metabolic features present in APC and p53 KO clones, respectively. Furthermore, by integrating tandem MS and in silico approaches, we identified N,N-Dihydroxy-L-Tyrosine as a novel metabolite which is substantially upregulated in both p53 and APC KO clones compared to wild-type. N,N-Dihydroxy-L-Tyrosine is involved in branched-chain amino acid and phenylalanine metabolism which may have a significant role in tumorigenesis. We are certain that identification of new unknowns will meaningfully contribute novel route in next generation cancer metabolism and drug development research. Citation Format: Iqbal Mahmud, Satya Narayan, Timothy J. Garrett. Identifying metabolic dark matter (unknown) with therapeutic potential in tumor suppressor deficient colorectal cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4758.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry.

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing.

Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters’ influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100–1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.

Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships.

In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.

Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.