| Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0 M The, MJ MacCoss, WS Noble, L Käll Journal of The American Society for Mass Spectrometry 27 (11), 1719-1727, 2016 | 250 | 2016 |
| How to talk about protein‐level false discovery rates in shotgun proteomics M The, A Tasnim, L Käll Proteomics 16 (18), 2461-2469, 2016 | 36 | 2016 |
| ProteomicsDB: toward a FAIR open-source resource for life-science research L Lautenbacher, P Samaras, J Muller, A Grafberger, M Shraideh, J Rank, ... Nucleic Acids Research, 2021 | 30 | 2021 |
| MaRaCluster: A fragment rarity metric for clustering fragment spectra in shotgun proteomics M The, L Käll Journal of proteome research 15 (3), 713-720, 2016 | 29 | 2016 |
| Integrated identification and quantification error probabilities for shotgun proteomics M The, L Käll Molecular & Cellular Proteomics 18 (3), 561-570, 2019 | 25 | 2019 |
| The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters FA Schober, D Moore, I Atanassov, MF Moedas, P Clemente, Á Végvári, ... Science Advances 7 (8), eabf0717, 2021 | 24 | 2021 |
| Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics H Maboudi Afkham, X Qiu, M The, L Käll Bioinformatics 33 (4), 508-513, 2017 | 22 | 2017 |
| Identification of 7 000–9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC–MS/MS Y Bian, M The, P Giansanti, J Mergner, R Zheng, M Wilhelm, A Boychenko, ... Analytical Chemistry 93 (25), 8687-8692, 2021 | 20 | 2021 |
| Mass spectrometry-based draft of the mouse proteome P Giansanti, P Samaras, Y Bian, C Meng, A Coluccio, M Frejno, ... Nature Methods 19 (7), 803-811, 2022 | 18 | 2022 |
| Focus on the spectra that matter by clustering of quantification data in shotgun proteomics M The, L Käll Nature Communications 11 (1), 1-12, 2020 | 17 | 2020 |
| A protein standard that emulates homology for the characterization of protein inference algorithms M The, F Edfors, Y Perez-Riverol, SH Payne, MR Hoopmann, M Palmblad, ... Journal of proteome research 17 (5), 1879-1886, 2018 | 16 | 2018 |
| Decrypting drug actions and protein modifications by dose-and time-resolved proteomics J Zecha, FP Bayer, S Wiechmann, J Woortman, N Berner, J Müller, ... Science 380 (6640), 93-101, 2023 | 13 | 2023 |
| Evaluation of disposable trap column nanoLC–FAIMS–MS/MS for the proteomic analysis of FFPE tissue S Eckert, YC Chang, FP Bayer, M The, PH Kuhn, W Weichert, B Kuster Journal of proteome research 20 (12), 5402-5411, 2021 | 9 | 2021 |
| SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering F Hamood, FP Bayer, M Wilhelm, B Kuster, M The Molecular & Cellular Proteomics 21 (8), 2022 | 8 | 2022 |
| Prosit-TMT: deep learning boosts identification of TMT-labeled peptides W Gabriel, M The, DP Zolg, FP Bayer, O Shouman, L Lautenbacher, ... Analytical Chemistry 94 (20), 7181-7190, 2022 | 8 | 2022 |
| Speeding up percolator JT Halloran, H Zhang, K Kara, C Renggli, M The, C Zhang, DM Rocke, ... Journal of proteome research 18 (9), 3353-3359, 2019 | 8 | 2019 |
| Abrf proteome informatics research group (Iprg) 2016 study: inferring proteoforms from bottom-up proteomics data JY Lee, H Choi, CM Colangelo, D Davis, MR Hoopmann, L Käll, H Lam, ... Journal of biomolecular techniques: JBT 29 (2), 39, 2018 | 7 | 2018 |
| Response to “comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra” J Griss, Y Perez-Riverol, M The, L Käll, JA Vizcaino Journal of proteome research 17 (5), 1993-1996, 2018 | 7 | 2018 |
| Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups M The, P Samaras, B Kuster, M Wilhelm Molecular & Cellular Proteomics 21 (12), 2022 | 6 | 2022 |
| Triqler for MaxQuant: Enhancing results from MaxQuant by Bayesian error propagation and integration M The, L Kall Journal of Proteome Research 20 (4), 2062-2068, 2021 | 4 | 2021 |
Lukas KällProfessor, Science for Life Laboratory, KTH Royal Institute of TechnologyVerified email at kth.se
