TY - CHAP A1 - Holstein, Tanja A1 - Muth, Thilo ED - Salerno, C. T1 - Bioinformatic workflows for metaproteomics N2 - The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment. KW - Metaproteomics KW - Microbiomes KW - Proteomics KW - Bioinformatic workflow KW - Taxonomic assignment KW - Functional assignment KW - Protein quantification PY - 2024 SN - 978-1-0716-3909-2 SN - 978-1-0716-3910-8 SN - 978-1-0716-3912-2 DO - https://doi.org/10.1007/978-1-0716-3910-8_16 SN - 1064-3745 SN - 1940-6029 N1 - Serientitel: Methods in molecular biology – Series title: Methods in molecular biology VL - 2820 SP - 187 EP - 213 PB - Springer CY - New York, NY AN - OPUS4-62741 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Athman, Rukeia A1 - Rädler, Jörg A1 - Löhmann, Oliver A1 - Ariza de Schellenberger, Angela A1 - Muth, Thilo T1 - The BAM Data Store N2 - As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE. T2 - 1st Conference on Research Data Infrastructure DA - 12.09.2023 KW - Research Data Infrastructure KW - Electronic Lab Notebook (ELN) KW - openBIS KW - Research Data Management PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596032 DO - https://doi.org/10.52825/CoRDI.v1i.229 VL - 1 SP - 1 EP - 5 AN - OPUS4-59603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Russo, Francesco F. A1 - Nowatzky, Yannek A1 - Jaeger, Carsten A1 - Parr, Maria K. A1 - Benner, Philipp A1 - Muth, Thilo A1 - Lisec, Jan T1 - Machine learning methods for compound annotation in non‐targeted mass spectrometry—A brief overview of fingerprinting, in silico fragmentation and de novo methods N2 - Non‐targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance–based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field.In this work, we illustrate which ML‐based methods are available for compound annotation in non‐targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies. KW - Mass Spectrometry KW - Spectra annotation KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-608732 DO - https://doi.org/10.1002/rcm.9876 VL - 38 IS - 20 SP - 1 EP - 15 PB - Wiley AN - OPUS4-60873 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Monavari, Mehran A1 - Prellwitz, Matthias A1 - Muth, Thilo A1 - Eichstädt, Sascha A1 - Koch, Claudia T1 - Quality-X: A Federated Digital Ecosystem for the Future Quality Infrastructure N2 - Harmonized and interoperable national Quality Infrastructure (QI) systems are essential for fostering cooperation, promoting mutual trust, and facilitating trade. The true potential of the QI is realized when its elements and actors are seamlessly integrated into a cohesive digital QI ecosystem. Recent developments towards industrial international data spaces enable such an ecosystem but require the integration of QI principles. Recognizing the lack of such a platform, Quality-X aims at setting the stage for the implementation of a QI ecosystem in international data spaces (IDS), GAIA-X and related German and European projects dedicated to secure data sharing. Quality-X is not about the construction of a platform; it is the creation of an inclusive QI ecosystem with harmonized interfaces. Instead of imposing rigid data structures, it prioritizes interoperability. Through the utilization of Decentralized Identifiers (DIDs), Verifiable Credentials, and Identity Hubs, Quality-X seeks seamless interactions across diverse service provider systems. This white paper introduces the concept and vision of Quality-X and discusses the general prerequisites for integrating QI processes within data spaces. Further on, we introduce existing testbeds, which will serve as an experimental proving ground for exploring various use cases related to the implementation of the vision of a QI-Digital. +++ Harmonisierte und interoperable nationale Qualitätsinfrastrukturen (QI) sind für die Förderung der Zusammenarbeit, des gegenseitigen Vertrauens und der Erleichterung des Handels unerlässlich. Das wahre Potenzial der QI kommt zum Tragen, wenn ihre Elemente und Akteure nahtlos in ein kohärentes digitales QI-Ökosystem integriert werden. Die jüngsten Entwicklungen hin zu industriellen internationalen Datenräumen ermöglichen ein solches Ökosystem, erfordern jedoch die Integration von QI-Prinzipien. Angesichts des Fehlens einer solchen Plattform zielt Quality-X darauf ab, die Voraussetzungen für die Umsetzung eines QI-Ökosystems in internationalen Datenräumen (IDS), GAIA-X und verwandten deutschen und europäischen Projekten zum sicheren Datenaustausch zu schaffen. Bei Quality-X geht es nicht um den Aufbau einer Plattform, sondern um die Schaffung eines umfassenden QI-Ökosystems mit harmonisierten Schnittstellen. Anstatt starre Datenstrukturen aufzuerlegen, steht die Interoperabilität im Vordergrund. Durch die Verwendung von dezentralen Identifikatoren (DIDs), überprüfbaren Berechtigungsnachweisen und Identitäts-Hubs strebt Quality-X eine nahtlose Interaktion zwischen verschiedenen Systemen von Dienstleistern an. Dieses Whitepaper stellt das Konzept und die Vision von Quality-X vor und erörtert die allgemeinen Voraussetzungen für die Integration von QI-Prozessen in Datenräumen. Darüber hinaus stellen wir bestehende Testbeds vor, die als experimentelles Versuchsfeld für die Erforschung verschiedener Anwendungsfälle im Zusammenhang mit der Umsetzung der Vision einer QI-Digital dienen sollen. KW - Quality Assurance KW - Quality Infrastructure KW - Data Spaces KW - Digitalization KW - Quality Management KW - Interoperability KW - Verification KW - Identification KW - Certificate KW - Datenräume KW - Qualitätsinfrastruktur KW - Gaia-X KW - Manufacturing-X KW - Catena-X KW - Datenökosystem KW - Qualitätssicherung KW - Qualitätsmanagement PY - 2023 UR - https://www.qi-digital.de/fileadmin/user_upload/website/publikationen/1022_Brosch%C3%BCre_Quality-X_v4.pdf SP - 1 CY - Berlin AN - OPUS4-59354 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nowatzky, Yannek A1 - Russo, Francesco Friedrich A1 - Lisec, Jan A1 - Kister, Alexander A1 - Reinert, Knut A1 - Muth, Thilo A1 - Benner, Philipp T1 - FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events N2 - Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions. KW - Metabolomics KW - Machine Learning KW - Mass spectrometry KW - Bioinformatics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627585 DO - https://doi.org/10.1038/s41467-025-57422-4 SN - 2041-1723 VL - 16 IS - 1 SP - 1 EP - 17 PB - Springer Science and Business Media LLC AN - OPUS4-62758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Portella, Pedro Dolabella A1 - Skrotzki, Birgit A1 - Muth, Thilo T1 - Die Digitalisierung der Materialwissenschaft und Werkstofftechnik - ein Überblick N2 - Die Bereitstellung von zuverlässigen Werkstoffdaten stand für die Werkstoffprüfung seit dem 19. Jahrhundert in zentraler Stelle. Mit der zunehmender Digitalisierung unserer Gesellschaft gewinnt das Datenmanagement insbesondere im Hinblick auf die Modellierungs- und Simulationsaktivitäten an Bedeutung. In unserem Beitrag beschreiben wir die gegenwärtigen Aktivitäten in Deutschland – insbesondere die NFDI-MatWerk und die BMBF-Initiative MaterialDigital – sowie in Europa und Übersee. Abschließend heben wir die Auswirkungen auf die technische und akademische Aus- und Weiterbildung. T2 - DVM - Tagung Werkstoffprüfung CY - Online meeting DA - 03.12.2020 KW - Werkstoffdaten KW - Digitalisierung KW - Materialwissenschaft PY - 2020 AN - OPUS4-52044 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Tscheuschner, Georg A1 - Kaiser, Melanie N. A1 - Lisec, Jan A1 - Beslic, D. A1 - Muth, Thilo A1 - Krüger, M. A1 - Mages, H.-W. A1 - Dorner, B. G. A1 - Knospe, J. A1 - Schenk, J. A. A1 - Sellrie, F. A1 - Weller, Michael G. T1 - MALDI-TOF-MS-based identification of monoclonal murine anti-SARS-CoV-2 antibodies within one hour N2 - During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used thirty-five monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied onto the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 45 minutes and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 °C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0 (https://gets.shinyapps.io/ABID/). This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context. KW - SARS-CoV-2 antibody KW - Reproducibility crisis KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Traceability KW - Antibody identification KW - Identity KW - Antibody light chain KW - MALDI-TOF-MS KW - Trypsin KW - Acidic cleavage KW - Antibody subclass KW - Database KW - Peak overlap KW - ABID KW - Sulfuric acid KW - Online software KW - Sequencing KW - Peptide coverage PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-545182 DO - https://doi.org/10.20944/preprints202203.0229.v1 SN - 2310-287X SP - 1 EP - 24 PB - MDPI CY - Basel AN - OPUS4-54518 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tscheuschner, Georg A1 - Kaiser, Melanie N. A1 - Lisec, Jan A1 - Beslic, D. A1 - Muth, Thilo A1 - Krüger, M. A1 - Mages, H. W. A1 - Dorner, B. G. A1 - Knospe, J. A1 - Schenk, J. A. A1 - Sellrie, F. A1 - Weller, Michael G. T1 - MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour N2 - During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context. KW - Reproducibility KW - Quality control KW - Traceability KW - Peptides KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Recombinant antibody KW - Identity KW - Antibody identification KW - Sequencing KW - Light chain KW - Mass spectrometry KW - Software KW - Open science KW - Library KW - COVID-19 KW - Corona virus KW - Sequence coverage KW - NIST-mAb 8671 KW - Reference material KW - RBD KW - Spike protein KW - Nucleocapsid KW - Cleavage KW - Tryptic digest KW - MALDI KW - DHAP KW - 2,5-dihydroxyacetophenone KW - Github KW - Zenodo KW - ABID PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547347 DO - https://doi.org/10.3390/antib11020027 VL - 11 IS - 2 SP - 1 EP - 22 PB - MDPI CY - Basel AN - OPUS4-54734 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beslic, D. A1 - Tscheuschner, Georg A1 - Renard, B. Y. A1 - Weller, Michael G. A1 - Muth, Thilo T1 - Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly N2 - Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody–antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69–99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences. KW - De novo peptide sequencing KW - Bioinformatics KW - Benchmarking study KW - Monoclonal antibody KW - Mass spectrometry KW - Sequence coverage KW - Light chains KW - Heavy chains KW - IgG KW - Immunoglobulins KW - Error correction KW - Sequencing algorithm KW - Preprocessing KW - Missing fragmentation sites KW - Deep learning-based tools PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570363 DO - https://doi.org/10.1093/bib/bbac542 VL - 24 IS - 1 SP - 1 EP - 12 PB - Oxford University Press AN - OPUS4-57036 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -