Selected publications
Müller, J., Bayer, F. P., Wilhelm, M., Schuh, M. G., Kuster, B., & The, M. (2025). PTMNavigator: interactive visualization of differentially regulated post-translational modifications in cellular signaling pathways. Nature Communications, https://doi.org/10.1038/s41467-024-55533-y.
Bayer, F. P., Gander, M., Kuster, B., & The, M. (2023). CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves. Nature Communications, doi:10.1038/s41467-023-43696-z.
Zecha, J., Bayer, F. P., Wiechmann, S., Woortman, J., Berner, N., Müller, J., … & The, M., Wilhelm, M., Kuster, B. (2023). Decrypting drug actions and protein modifications by dose-and time-resolved proteomics. Science, doi:10.1126/science.ade3925.
The, M., Samaras, P., Kuster, B., & Wilhelm, M. (2022). Re-analysis of ProteomicsDB using an accurate, sensitive and scalable false discovery rate estimation approach for protein groups. Molecular & Cellular Proteomics, doi:10.1016/j.mcpro.2022.100437.
Hamood, F., Bayer, F. P., Wilhelm, M., Kuster, B., & The, M. (2022). SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering. Molecular & Cellular Proteomics, doi:10.1016/j.mcpro.2022.100238.
Lautenbacher, L., Samaras, P., Muller, J., Grafberger, A., Shraideh, M., Rank, J., Fuchs, S. T., Schmidt, T. K., The, M., Dallago, C., Wittges, H., Rost, B., Krcmar, H., Kuster, B., & Wilhelm, M. (2021). ProteomicsDB: toward a FAIR open-source resource for life-science research. Nucleic Acids Research, doi:10.1093/nar/gkab1026.
The, M., Käll, L. (2020). Focus on the spectra that matter by clustering of quantification data in shotgun proteomics. Nature Communications, doi:10.1038/s41467-020-17037-3.
The, M., Käll, L. (2019). Integrated identification and quantification error probabilities for shotgun proteomics. Molecular & Cellular Proteomics, doi:10.1074/mcp.RA118.001018.
The, M., Tasnim, A., & Käll, L. (2016). How to talk about protein‐level false discovery rates in shotgun proteomics. Proteomics, doi:10.1002/pmic.201500431.
The, M., MacCoss, M.J., Noble, W.S., Käll, L. (2016). Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. J. Am. Soc. Mass Spectrom., doi:10.1007/s13361-016-1460-7.
The, M., Käll, L. (2016). MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics. J. Proteome Res., doi:10.1021/acs.jproteome.5b00749.