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Printed publications

Khoonsari, P. E., Moreno, P., Bergmann, S., Burman, J., Capuccini, M., Carone, M., Cascante, M., de Atauri, P., Foguet, C., Gonzalez-Beltran, A., Hankemeier, T., Haug, K., He, S., Herman, S., Johnson, D., Kale, N., Larsson, A., Neumann, S., Peters, K., Pireddu, L., Rocca-Serra, P., Roger, P., Rueedi, R., Ruttkies, C., Sadawi, N., Salek, R. M., Sansone, S.-A., Schober, D., Selivanov, V., Thévenot, E. A., van Vliet, M., Zanetti, G., Steinbeck, C., Kultima, K. & Spjuth, O. Interoperable and scalable data analysis with microservices: Applications in Metabolomics BioRxiv (2018) DOI: 10.1101/213603

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

Schober, D., Jacob, D., Wilson, M., Cruz, J. A., Marcu, A., Grant, J. R., Moing, A., Deborde, C., de Figueiredo, L. F., Haug, K., Rocca-Serra, P., Easton, J., Ebbels, T. M. D., Hao, J., Ludwig, C., Günther, U. L., Rosato, A., Klein, M. S., Lewis, I. A., Luchinat, C., Jones, A. R., Grauslys, A., Larralde, M., Yokochi, M., Kobayashi, N., Porzel, A., Griffin, J. L., Viant, M. R., Wishart, D. S., Steinbeck, C., Salek, R. M. & Neumann, S. nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data Anal Chem 90, 649–656, (2018) DOI: 10.1021/acs.analchem.7b02795

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

Döll, S., Kuhlmann, M., Rutten, T., Mette, M. F., Scharfenberg, S., Petridis, A., Berreth, D.-C. & Mock, H.-P. Accumulation of the coumarin scopolin under abiotic stress conditions is mediated by the Arabidopsis thaliana THO/TREX complex Plant J 93, 431-444, (2018) DOI: 10.1111/tpj.13797

Secondary metabolites are involved in the plant stress response. Among these are scopolin and its active form scopoletin, which are coumarin derivatives associated with reactive oxygen species scavenging and pathogen defence. Here we show that scopolin accumulation can be induced in the root by osmotic stress and in the leaf by low‐temperature stress in Arabidopsis thaliana. A genetic screen for altered scopolin levels in A. thaliana revealed a mutant compromised in scopolin accumulation in response to stress; the lesion was present in a homologue of THO1 coding for a subunit of the THO/TREX complex. The THO/TREX complex contributes to RNA silencing, supposedly by trafficking precursors of small RNAs. Mutants defective in THO, AGO1, SDS3 and RDR6 were impaired with respect to scopolin accumulation in response to stress, suggesting a mechanism based on RNA silencing such as the trans‐acting small interfering RNA pathway, which requires THO/TREX function.
Printed publications

Peters, K., Bradbury, J., Bergmann, S., Capuccini, M., Cascante, M., de Atauri, P., Ebbels, T., Foguet, C., Glen, R., Gonzalez-Beltran, A., Guenther, U., Handakas, E., Hankemeier, T., Herman, S., Haug, K., Holub, P., Izzo, M., Jacob, D., Johnson, D., Jourdan, F., Kale, N., Karaman, I., Khalili, B., Khoonsari, P. E., Kultima, K., Lampa, S., Larsson, A., Ludwig, C., Moreno, P., Neumann, S., Novella, J. A., O'Donovan, C., Pearce, J. T. M., Peluso, A., Pireddu, L., Piras, M. E., Reed, M. A. C., Rocca-Serra, P., Roger, P., Rosato, A., Rueedi, R., Ruttkies, C., Sadawi, N., Salek, R., Sansone, S.-A., Selivanov, V., Spjuth, O., Schober, D., Thévenot, E. A., Tomasoni, M., Rijswijk, M., Vliet, M., Viant, M., Weber, R., Zanetti, G. & Steinbeck, C. PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud bioRxiv (2018) DOI: 10.1101/409151

Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings: The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

Frainay, C., Schymanski, E. L., Neumann, S., Merlet, B., Salek, R. M., Jourdan, F. & Yanes, O. Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas Metabolites 8, 51, (2018) DOI: 10.3390/metabo8030051

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.

Peters, K., Gorzolka, K., Bruelheide, H. & Neumann, S. Computational workflow to study the seasonal variation of secondary metabolites in nine different bryophytes Sci Data 5, 180179, (2018) DOI: 10.1038/sdata.2018.179

In Eco-Metabolomics interactions are studied of non-model organisms in their natural environment and relations are made between biochemistry and ecological function. Current challenges when processing such metabolomics data involve complex experiment designs which are often carried out in large field campaigns involving multiple study factors, peak detection parameter settings, the high variation of metabolite profiles and the analysis of non-model species with scarcely characterised metabolomes. Here, we present a dataset generated from 108 samples of nine bryophyte species obtained in four seasons using an untargeted liquid chromatography coupled with mass spectrometry acquisition method (LC/MS). Using this dataset we address the current challenges when processing Eco-Metabolomics data. Here, we also present a reproducible and reusable computational workflow implemented in Galaxy focusing on standard formats, data import, technical validation, feature detection, diversity analysis and multivariate statistics. We expect that the representative dataset and the reusable processing pipeline will facilitate future studies in the research field of Eco-Metabolomics.

Peters, K., Worrich, A., Weinhold, A., Alka, O., Balcke, G., Birkemeyer, C., Bruelheide, H., Calf, O. W., Dietz, S., Dührkop, K., Gaquerel, E., Heinig, U., Kücklich, M., Macel, M., Müller, C., Poeschl, Y., Pohnert, G., Ristok, C., Rodríguez, V. M., Ruttkies, C., Schuman, M., Schweiger, R., Shahaf, N., Steinbeck, C., Tortosa, M., Treutler, H., Ueberschaar, N., Velasco, P., Weiß, B. M., Widdig, A., Neumann, S. & van Dam, N. M. Current Challenges in Plant Eco-Metabolomics Int J Mol Sci 19, 1385, (2018) DOI: 10.3390/ijms19051385

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology

Peters, K., Gorzolka, K., Bruelheide, H. & Neumann, S. Seasonal variation of secondary metabolites in nine different bryophytes Ecol Evol 8, 9105-9117, (2018) DOI: 10.1002/ece3.4361

Bryophytes occur in almost all land ecosystems and contribute to global biogeochemical cycles, ecosystem functioning, and influence vegetation dynamics. As growth and biochemistry of bryophytes are strongly dependent on the season, we analyzed metabolic variation across seasons with regard to ecological characteristics and phylogeny. Using bioinformatics methods, we present an integrative and reproducible approach to connect ecology with biochemistry. Nine different bryophyte species were collected in three composite samples in four seasons. Untargeted liquid chromatography coupled with mass spectrometry (LC/MS) was performed to obtain metabolite profiles. Redundancy analysis, Pearson's correlation, Shannon diversity, and hierarchical clustering were used to determine relationships among species, seasons, ecological characteristics, and hierarchical clustering. Metabolite profiles of Marchantia polymorpha and Fissidens taxifolius which are species with ruderal life strategy (R‐selected) showed low seasonal variability, while the profiles of the pleurocarpous mosses and Grimmia pulvinata which have characteristics of a competitive strategy (C‐selected) were more variable. Polytrichum strictum and Plagiomnium undulatum had intermediary life strategies. Our study revealed strong species‐specific differences in metabolite profiles between the seasons. Life strategies, growth forms, and indicator values for light and soil were among the most important ecological predictors. We demonstrate that untargeted Eco‐Metabolomics provide useful biochemical insight that improves our understanding of fundamental ecological strategies.
Printed publications

Deutsch, E. W., Perez-Riverol, Y., Chalkley, R. J., Wilhelm, M., Tate, S., Sachsenberg, T., Walzer, M., Käll, L., Delanghe, B., Böcker, S., Schymanski, E. L., Wilmes, P., Dorfer, V., Kuster, B., Volders, P.-J., Jehmlich, N., Vissers, J. P. C., Wolan, D. W., Wang, A. Y., Mendoza, L., Shofstahl, J., Dowsey, A. W., Griss, J., Salek, R. M., Neumann, S., Binz, P.-A., Lam, H., Vizcaíno, J. A., Bandeira, N. & Röst, H. Expanding the Use of Spectral Libraries in Proteomics J Proteome Res (2018) DOI: 10.1021/acs.jproteome.8b00485

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Schüler, J.-A., Neumann, S., Müller-Hannemann, M. & Brandt, W. ChemFrag: Chemically meaningful annotation of fragment ion mass spectra J Mass Spectrom 53, 1104-1115, (2018) DOI: 10.1002/jms.4278

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule‐based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.
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