TY - JOUR A1 - Bekemeier, Simon A1 - Caldeira Rêgo, C. R. A1 - Mai, H. L. A1 - Sikia, Ujjal A1 - Waseda, O. A1 - Apel, M. A1 - Arendt, F. A1 - Aschemann, A. A1 - Bayerlein, Bernd A1 - Courant, R. A1 - Dziwis, G. A1 - Fuchs, F. A1 - Giese, U. A1 - Junghanns, K. A1 - Kamal, M. A1 - Koschmieder, L. A1 - Leineweber, S. A1 - Luger, M. A1 - Lukas, M. A1 - Maas, J. A1 - Mertens, J. A1 - Mieller, Björn A1 - Overmeyer, L. A1 - Pirch, N. A1 - Reimann, J. A1 - Schröck, S. A1 - Schulze, P. A1 - Schuster, J. A1 - Seidel, A. A1 - Shchyglo, O. A1 - Sierka, M. A1 - Silze, F. A1 - Stier, S. A1 - Tegeler, M. A1 - Unger, Jörg F. A1 - Weber, M. A1 - Hickel, Tilmann A1 - Schaarschmidt, J. T1 - Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative N2 - The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store’s role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science. KW - Digitalisation KW - FAIR principles KW - MaterialDigital KW - Scientific workflows KW - Semantic interoperability PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624088 DO - https://doi.org/10.1002/adem.202402149 SN - 1527-2648 IS - 2402149 SP - 1 EP - 25 PB - Wiley-VCH GmbH AN - OPUS4-62408 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Altenburg, T. A1 - Giese, S. A1 - Wang, S. A1 - Muth, Thilo A1 - Renard, B.Y. T1 - Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides N2 - Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. KW - Mass spectrometry KW - Machine learning KW - Deep learning KW - Peptide identification PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547580 DO - https://doi.org/10.1038/s42256-022-00467-7 SN - 2522-5839 VL - 4 SP - 378 EP - 388 PB - Springer Nature CY - London AN - OPUS4-54758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -