| A combined transmembrane topology and signal peptide prediction method L Käll, A Krogh, ELL Sonnhammer Journal of molecular biology 338 (5), 1027-1036, 2004 | 2660 | 2004 |
| Semi-supervised learning for peptide identification from shotgun proteomics datasets L Käll, JD Canterbury, J Weston, WS Noble, MJ MacCoss Nature methods 4 (11), 923-925, 2007 | 2427 | 2007 |
| Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server L Käll, A Krogh, ELL Sonnhammer Nucleic acids research 35 (suppl_2), W429-W432, 2007 | 1782 | 2007 |
| The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides KD Tsirigos, C Peters, N Shu, L Käll, A Elofsson Nucleic acids research 43 (W1), W401-W407, 2015 | 919 | 2015 |
| Assigning significance to peptides identified by tandem mass spectrometry using decoy databases L Käll, JD Storey, MJ MacCoss, WS Noble Journal of proteome research 7 (01), 29-34, 2008 | 775 | 2008 |
| Gene‐specific correlation of RNA and protein levels in human cells and tissues F Edfors, F Danielsson, BM Hallström, L Käll, E Lundberg, F Pontén, ... Molecular systems biology 12 (10), 883, 2016 | 519 | 2016 |
| An HMM posterior decoder for sequence feature prediction that includes homology in formation. Bioinformatics, 21 (1): i251-i257 L Käll Bioinformatics 21 (1), i251-i257, 2005 | 383 | 2005 |
| Posterior error probabilities and false discovery rates: two sides of the same coin L Käll, JD Storey, MJ MacCoss, WS Noble Journal of proteome research 7 (01), 40-44, 2008 | 374 | 2008 |
| 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 J. Am. Soc. Mass Spectrom 27 (11), 1719, 2016 | 313 | 2016 |
| Improvements to the percolator algorithm for peptide identification from shotgun proteomics data sets M Spivak, J Weston, L Bottou, L Kall, WS Noble Journal of proteome research 8 (7), 3737-3745, 2009 | 311 | 2009 |
| Transmembrane topology and signal peptide prediction using dynamic bayesian networks SM Reynolds, L Käll, ME Riffle, JA Bilmes, WS Noble PLoS computational biology 4 (11), e1000213, 2008 | 302 | 2008 |
| HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics RMM Branca, LM Orre, HJ Johansson, V Granholm, M Huss, ... Nature methods 11 (1), 59-62, 2014 | 293 | 2014 |
| Membrane topology of the Drosophila OR83b odorant receptor C Lundin, L Käll, SA Kreher, K Kapp, EL Sonnhammer, JR Carlson, ... FEBS letters 581 (29), 5601-5604, 2007 | 259 | 2007 |
| Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry L Käll, JD Storey, WS Noble Bioinformatics 24 (16), i42-i48, 2008 | 244 | 2008 |
| Rapid and accurate peptide identification from tandem mass spectra CY Park, AA Klammer, L Kall, MJ MacCoss, WS Noble Journal of proteome research 7 (7), 3022-3027, 2008 | 243 | 2008 |
| A general model of G protein‐coupled receptor sequences and its application to detect remote homologs M Wistrand, L Käll, ELL Sonnhammer Protein science 15 (3), 509-521, 2006 | 218 | 2006 |
| Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data YS Ting, JD Egertson, SH Payne, S Kim, B MacLean, L Käll, R Aebersold, ... Molecular & Cellular Proteomics 14 (9), 2301-2307, 2015 | 190 | 2015 |
| Multi-omic data analysis using Galaxy J Boekel, JM Chilton, IR Cooke, PL Horvatovich, PD Jagtap, L Käll, ... Nature biotechnology 33 (2), 137-139, 2015 | 168 | 2015 |
| Crux: rapid open source protein tandem mass spectrometry analysis S McIlwain, K Tamura, A Kertesz-Farkas, CE Grant, B Diament, B Frewen, ... Journal of proteome research 13 (10), 4488-4491, 2014 | 159 | 2014 |
| Automated model building and protein identification in cryo-EM maps K Jamali, L Käll, R Zhang, A Brown, D Kimanius, SHW Scheres Nature 628 (8007), 450-457, 2024 | 157 | 2024 |
| Growth of cyanobacteria is constrained by the abundance of light and carbon assimilation proteins M Jahn, V Vialas, J Karlsen, G Maddalo, F Edfors, B Forsström, M Uhlén, ... Cell reports 25 (2), 478-486. e8, 2018 | 124 | 2018 |
| Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations GE Merrihew, C Davis, B Ewing, G Williams, L Käll, BE Frewen, WS Noble, ... Genome research 18 (10), 1660-1669, 2008 | 118 | 2008 |
| Training, selection, and robust calibration of retention time models for targeted proteomics L Moruz, D Tomazela, L Kall Journal of proteome research 9 (10), 5209-5216, 2010 | 116 | 2010 |
| qvality: non-parametric estimation of q-values and posterior error probabilities L Käll, JD Storey, WS Noble Bioinformatics 25 (7), 964-966, 2009 | 115 | 2009 |
| Fast and accurate database searches with MS-GF+ Percolator V Granholm, S Kim, JCF Navarro, E Sjolund, RD Smith, L Kall Journal of proteome research 13 (2), 890-897, 2014 | 112 | 2014 |
| Reliability of transmembrane predictions in whole‐genome data L Käll, ELL Sonnhammer FEBS letters 532 (3), 415-418, 2002 | 99 | 2002 |
| Peptide retention time prediction L Moruz, L Käll Mass spectrometry reviews 36 (5), 615-623, 2017 | 96 | 2017 |
| Computational mass spectrometry–based proteomics L Käll, O Vitek PLoS computational biology 7 (12), e1002277, 2011 | 83 | 2011 |
| A novel transmembrane topology of presenilin based on reconciling experimental and computational evidence A Henricson, L Käll, ELL Sonnhammer The FEBS journal 272 (11), 2727-2733, 2005 | 71 | 2005 |
| DeMix-Q: quantification-centered data processing workflow B Zhang, L Käll, RA Zubarev Molecular & Cellular Proteomics 15 (4), 1467-1478, 2016 | 69 | 2016 |
| Covariation of peptide abundances accurately reflects protein concentration differences B Zhang, M Pirmoradian, R Zubarev, L Käll Molecular & Cellular Proteomics 16 (5), 936-948, 2017 | 67 | 2017 |
| Chromatographic retention time prediction for posttranslationally modified peptides L Moruz, A Staes, JM Foster, M Hatzou, E Timmerman, L Martens, L Käll Proteomics 12 (8), 1151-1159, 2012 | 66 | 2012 |
| Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? DL Plubell, L Käll, BJ Webb-Robertson, LM Bramer, A Ives, NL Kelleher, ... Journal of proteome research 21 (4), 891-898, 2022 | 64* | 2022 |
| Expanding the use of spectral libraries in proteomics EW Deutsch, Y Perez-Riverol, RJ Chalkley, M Wilhelm, S Tate, ... Journal of proteome research 17 (12), 4051-4060, 2018 | 64 | 2018 |
| Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics V Granholm, JF Navarro, WS Noble, L Käll Journal of proteomics 80, 123-131, 2013 | 59 | 2013 |
| A cross-validation scheme for machine learning algorithms in shotgun proteomics V Granholm, WS Noble, L Käll BMC bioinformatics 13, 1-8, 2012 | 59 | 2012 |
| On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics V Granholm, WS Noble, L Kall Journal of proteome research 10 (5), 2671-2678, 2011 | 59 | 2011 |
| Quality assessments of peptide–spectrum matches in shotgun proteomics V Granholm, L Käll Proteomics 11 (6), 1086-1093, 2011 | 58 | 2011 |
| Solution to statistical challenges in proteomics is more statistics, not less O Serang, L Käll Journal of proteome research 14 (10), 4099-4103, 2015 | 56 | 2015 |
| Spatial multimodal analysis of transcriptomes and metabolomes in tissues M Vicari, R Mirzazadeh, A Nilsson, R Shariatgorji, P Bjärterot, L Larsson, ... Nature Biotechnology 42 (7), 1046-1050, 2024 | 54 | 2024 |
| Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator JC Wright, MO Collins, L Yu, L Käll, M Brosch, JS Choudhary Molecular & Cellular Proteomics 11 (8), 478-491, 2012 | 49 | 2012 |
| Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics L Moruz, P Pichler, T Stranzl, K Mechtler, L Käll Analytical chemistry 85 (16), 7777-7785, 2013 | 42 | 2013 |
| Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences O Serang, L Moruz, MR Hoopmann, L Kall Journal of proteome research 11 (12), 5586-5591, 2012 | 42 | 2012 |
| A guideline to proteome‐wide α‐helical membrane protein topology predictions KD Tsirigos, A Hennerdal, L Käll, A Elofsson Proteomics 12 (14), 2282-2294, 2012 | 41 | 2012 |
| How to talk about protein-level false discovery rates in shotgun proteomics M The, A Tasnim, L Käll Proteomics 16 (18), 2461–2469, 2016 | 40 | 2016 |
| The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters FA Rosenberger, D Moore, I Atanassov, MF Moedas, P Clemente, ... Science Advances 7 (8), eabf0717, 2021 | 36 | 2021 |
| Toward an integrated machine learning model of a proteomics experiment BA Neely, V Dorfer, L Martens, I Bludau, R Bouwmeester, S Degroeve, ... Journal of proteome research 22 (3), 681-696, 2023 | 35 | 2023 |
| 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 | 35 | 2016 |
| IPeak: An open source tool to combine results from multiple MS/MS search engines B Wen, C Du, G Li, F Ghali, AR Jones, L Käll, S Xu, R Zhou, Z Ren, ... Proteomics 15 (17), 2916-2920, 2015 | 33 | 2015 |
| Nonparametric Bayesian evaluation of differential protein quantification O Serang, AE Cansizoglu, L Kall, H Steen, JA Steen Journal of proteome research 12 (10), 4556-4565, 2013 | 31 | 2013 |
| Integrated identification and quantification error probabilities for shotgun proteomics M The, L Käll Molecular & Cellular Proteomics 18 (3), 561-570, 2019 | 28 | 2019 |
| Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics HM Afkham, X Qiu, M The, L Käll Bioinformatics, btw619, 2016 | 26* | 2016 |
| Prosit transformer: A transformer for prediction of MS2 spectrum intensities M Ekvall, P Truong, W Gabriel, M Wilhelm, L Kall Journal of Proteome Research 21 (5), 1359-1364, 2022 | 23 | 2022 |
| Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times L Moruz, MR Hoopmann, M Rosenlund, V Granholm, RL Moritz, L Kall Journal of proteome research 12 (12), 5730-5741, 2013 | 21 | 2013 |
| Focus on the spectra that matter by clustering of quantification data in shotgun proteomics M The, L Käll Nature communications 11 (1), 3234, 2020 | 19 | 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 | 19 | 2018 |
| Membrane protein shaving with thermolysin can be used to evaluate topology predictors M Bendz, M Skwark, D Nilsson, V Granholm, S Cristobal, L Käll, ... Proteomics 13 (9), 1467-1480, 2013 | 16 | 2013 |
| Spatial landmark detection and tissue registration with deep learning M Ekvall, L Bergenstråhle, A Andersson, P Czarnewski, J Olegård, L Käll, ... Nature Methods 21 (4), 673-679, 2024 | 10 | 2024 |
| Interpretation of the dome recommendations for machine learning in proteomics and metabolomics M Palmblad, S Bocker, S Degroeve, O Kohlbacher, L Kall, WS Noble, ... Journal of proteome research 21 (4), 1204-1207, 2022 | 10 | 2022 |
| Prediction of transmembrane topology and signal peptide given a protein’s amino acid sequence L Käll Computational Biology, Methods in Molecular Biology 673, 53-62, 2010 | 10 | 2010 |
| 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 | 9 | 2019 |
| quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data C Dai, J Pfeuffer, H Wang, P Zheng, L Käll, T Sachsenberg, V Demichev, ... Nature Methods 21 (9), 1603-1607, 2024 | 8 | 2024 |
| 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 | 8 | 2018 |
| CoExpresso: assess the quantitative behavior of protein complexes in human cells MH Chalabi, V Tsiamis, L Käll, F Vandin, V Schwämmle BMC bioinformatics 20, 1-10, 2019 | 7 | 2019 |
| 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 |
| Pathway analysis through mutual information GS Jeuken, L Käll Bioinformatics 40 (1), btad776, 2024 | 6 | 2024 |
| A comprehensive evaluation of consensus spectrum generation methods in proteomics X Luo, W Bittremieux, J Griss, EW Deutsch, T Sachsenberg, LI Levitsky, ... Journal of proteome research 21 (6), 1566-1574, 2022 | 6 | 2022 |
| Proceedings of the EuBIC-MS 2020 Developers’ Meeting C Ashwood, W Bittremieux, EW Deutsch, NT Doncheva, V Dorfer, ... EuPA Open Proteomics 24, 1-6, 2020 | 6 | 2020 |
| A simple null model for inferences from network enrichment analysis GS Jeuken, L Käll PloS one 13 (11), e0206864, 2018 | 6 | 2018 |
| GradientOptimizer: An open‐source graphical environment for calculating optimized gradients in reversed‐phase liquid chromatography L Moruz, L Käll Proteomics 14 (12), 1464-1466, 2014 | 6 | 2014 |
| The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020) PD Jagtap, MR Hoopmann, BA Neely, A Harvey, L Käll, Y Perez-Riverol, ... Journal of biomolecular techniques: JBT 34 (3), 2023 | 5 | 2023 |
| Survival analysis of pathway activity as a prognostic determinant in breast cancer GS Jeuken, NP Tobin, L Käll PLoS computational biology 18 (3), e1010020, 2022 | 4 | 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 |
| Parallelized calculation of permutation tests M Ekvall, M Höhle, L Käll Bioinformatics 36 (22-23), 5392-5397, 2020 | 4 | 2020 |
| Semi-supervised learning while controlling the fdr with an application to tandem mass spectrometry analysis J Freestone, L Käll, WS Noble, U Keich International Conference on Research in Computational Molecular Biology, 448-453, 2024 | 3 | 2024 |
| Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides D Skiadopoulou, J Vašíček, K Kuznetsova, D Bouyssié, L Käll, ... Journal of Proteome Research 22 (10), 3190-3199, 2023 | 3 | 2023 |
| Triqler for protein summarization of data from data-independent acquisition mass spectrometry P Truong, M The, L Käll Journal of Proteome Research 22 (4), 1359-1366, 2023 | 3 | 2023 |
| Finding haplotypic signatures in proteins J Vašíček, D Skiadopoulou, KG Kuznetsova, B Wen, S Johansson, ... GigaScience 12, giad093, 2023 | 3 | 2023 |
| Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry–a perspective D Schulte, M Šiborová, L Käll, J Snijder bioRxiv, 2024.06. 21.600107, 2024 | 1 | 2024 |
| Performing Selection on a Monotonic Function in Lieu of Sorting Using Layer-Ordered Heaps K Lucke, J Pennington, P Kreitzberg, L Kall, O Serang Journal of proteome research 20 (4), 1849-1854, 2021 | 1 | 2021 |
| Predicting transmembrane topology and signal peptides with hidden Markov models L Käll PQDT-Global, 2006 | 1 | 2006 |
| ProHap enables human proteomic database generation accounting for population diversity J Vašíček, KG Kuznetsova, D Skiadopoulou, L Unger, S Chera, LM Ghila, ... Nature Methods, 1-5, 2024 | | 2024 |
| Open-source and FAIR Research Software for Proteomics Y Perez-Riverol, W Bittremieux, WS Noble, L Martens, A Bilbao, ... | | 2024 |
| Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease SP Correia, MF Moedas, LS Taylor, K Naess, AZ Lim, R McFarland, ... JCI insight 9 (20), e178645, 2024 | | 2024 |
| ECCB2024: The 23rd European Conference on Computational Biology A Kukkonen-Macchi, S Hautaniemi, KF Heil, M Heinäniemi, LJ Jensen, ... Bioinformatics 40 (Supplement_2), ii1-ii3, 2024 | | 2024 |
| ProHap enables proteomic database generation accounting for population diversity J Vašíček, KG Kuznetsova, D Skiadopoulou, PR Njølstad, S Johansson, ... bioRxiv, 2023.12. 24.572591, 2023 | | 2023 |
| How to train a post-processor for tandem mass spectrometry proteomics database search while maintaining control of the false discovery rate JA Freestone, L Käll, WS Noble, U Keich | | 2023 |
| Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics. X Qiu, L Käll Bioinformatics (Oxford, England) 33 (4), 508-513, 2017 | | 2017 |
| Pattern Recognition in Bioinformatics: 9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings M Comin, L Käll, E Marchiori, A Ngom, JC Rajapakse Springer, 2014 | | 2014 |
| Engagera och aktivera studenter med inspiration från konferenser: examination genom poster-presentation O Emanuelsson, L Arvestad, L Käll LTHs 8: e Pedagogiska Inspirationskonferens, 17 december 2014, 2014 | | 2014 |
| From sequence to structure to networks N Yosef, L Käll Genome Biology 9, 1-3, 2008 | | 2008 |
| Transmembrane topology prediction L Käll, E Sonnhammer Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 2004 | | 2004 |
William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
