Publications of Eugenia Voytik

Strauss, M. T.; Bludau, I.; Zeng, W.-F.; Voytik, E.; Ammar, C.; Schessner, J. P.; Ilango, R.; Gill, M.; Meier, F.; Willems, S. et al.; Mann, M.: AlphaPept: A modern and open framework for MS-based proteomics. Nature Communications 15 (1), 2168 (2024)

Zeng, W.-F.; Zhou, X.-X.; Willems, S.; Ammar, C.; Wahle, M.; Bludau, I.; Voytik, E.; Strauss, M. T.; Mann, M.: AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics. Nature Communications 13 (1), 7238 (2022)

Skowronek, P.; Thielert, M.; Voytik, E.; Tanzer, M. C.; Hansen, F. M.; Willems, S.; Karayel, O.; Brunner, A.-D.; Meier, F.; Mann, M.: Rapid and in-depth coverage of the (phospho-) proteome with deep libraries and optimal window design for dia-PASEF. Molecular & Cellular Proteomics 21 (9), 100279 (2022)

Schessner, J. P.; Voytik, E.; Bludau, I.: A practical guide to interpreting and generating bottom-up proteomics data visualizations. Proteomics 22 (8), e2100103 (2022)

Voytik, E.; Bludau, I.; Willems, S.; Hansen, F. M.; Brunner, A.-D.; Strauss, M. T.; Mann, M.: AlphaMap: An open-source Python package for the visual annotation of proteomics data with sequence-specific knowledge. Bioinformatics 38 (3), pp. 849 - 852 (2022)

Meier, F.; Koehler, N. D.; Brunner, A.-D.; Wanka, J.-M. H.; Voytik, E.; Strauss, M. T.; Theis, F. J.; Mann, M.: Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nature Communications 12 (1), 1185 (2021)

Meier, F.; Brunner, A.-D.; Frank, M.; Ha, A.; Bludau, I.; Voytik, E.; Kaspar-Schoenefeld, S.; Lubeck, M.; Raether, O.; Bache, N. et al.; Aebersold, R.; Collins, B.C.; Röst, H.L.; Mann, M.: diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nature Methods 17 (12), pp. 1229 - 1236 (2020)

Geyer, P. E.; Voytik, E.; Treit, P. V.; Doll, S.; Kleinhempel, A.; Niu, L.; Müller, J. B.; Buchholtz, M.-L.; Bader, J. M.; Teupser, D. et al.; Holdt, L. M.; Mann, M.: Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. Embo Molecular Medicine 11 (11), e10427 (2019)

Meier, F.; Brunner, A.-D.; Frank, M.; Ha, A.; Voytik, E.; Kaspar-Schoenefeld, S.; Lubeck, M.; Raether, O.; Aebersold, R.; Collins, B. C. et al.; Roest, H. L.; Mann, M.: Parallel accumulation - serial fragmentation combined with data-independent acquisition (diaPASEF). In Molecular & Cellular Proteomics, 18 (8), p. S17 - S17. Elsevier ; American Society for Biochemistry and Molecular Biology (ASBMB), Bethesda, MD (2019)

Skotte, N. H.; Geyer, P. E.; Steger, M.; Treit, P. V.; Voytik, E.; Hellem, M. N.; Nielsen, J. E.; Mann, M.: Quality control for plasma and cerebrospinal fluid samples using mass spectrometry. In Journal of Neurology, Neurosurgery & Psychiatry, 89, pp. A31 - A32. Plenary Meeting of the European-Huntington's-Disease-Network (EHDN), Vienna, AUSTRIA. British Medical Association, London (2018)

Voytik, E.: Visualization and exploration of next-generation proteomics data. Dissertation, Ludwig-Maximilians-Universität München, Fakultät für Chemie und Pharmazie, München (2022)

Voytik, E.: A machine learning system for quality assurance in plasma proteome profiling. Master, LMU, München (2018)