Lukas Käll
Lukas Käll
Professor, Science for Life Laboratory, KTH Royal Institute of Technology
Verified email at - Homepage
Cited by
Cited by
A combined transmembrane topology and signal peptide prediction method
L Käll, A Krogh, ELL Sonnhammer
Journal of molecular biology 338 (5), 1027-1036, 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
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
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
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
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
An HMM posterior decoder for sequence feature prediction that includes homology information
L Käll, A Krogh, ELL Sonnhammer
Bioinformatics 21 (suppl_1), i251-i257, 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
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
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
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
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
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
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
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
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
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
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
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
qvality: non-parametric estimation of q-values and posterior error probabilities
L Käll, JD Storey, WS Noble
Bioinformatics 25 (7), 964-966, 2009
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
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
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
Reliability of transmembrane predictions in whole‐genome data
L Käll, ELL Sonnhammer
FEBS letters 532 (3), 415-418, 2002
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
Peptide retention time prediction
L Moruz, L Käll
Mass spectrometry reviews 36 (5), 615-623, 2017
Computational mass spectrometry–based proteomics
L Käll, O Vitek
PLoS computational biology 7 (12), e1002277, 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
DeMix-Q: quantification-centered data processing workflow
B Zhang, L Käll, RA Zubarev
Molecular & Cellular Proteomics 15 (4), 1467-1478, 2016
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
Solution to statistical challenges in proteomics is more statistics, not less
O Serang, L Käll
Journal of proteome research 14 (10), 4099-4103, 2015
Quality assessments of peptide–spectrum matches in shotgun proteomics
V Granholm, L Käll
Proteomics 11 (6), 1086-1093, 2011
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
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
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
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
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 Käll
Journal of proteome research 10 (5), 2671-2678, 2011
A cross-validation scheme for machine learning algorithms in shotgun proteomics
V Granholm, WS Noble, L Käll
BMC bioinformatics 13, 1-8, 2012
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
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
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
How to talk about protein-level false discovery rates in shotgun proteomics
M The, A Tasnim, L Käll
Proteomics 16 (18), 2461–2469, 2016
Nonparametric Bayesian evaluation of differential protein quantification
O Serang, AE Cansizoglu, L Käll, H Steen, JA Steen
Journal of proteome research 12 (10), 4556-4565, 2013
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
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
Integrated identification and quantification error probabilities for shotgun proteomics
M The, L Käll
Molecular & Cellular Proteomics 18 (3), 561-570, 2019
Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics
HM Afkham, X Qiu, M The, L Käll
Bioinformatics, btw619, 2016
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
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
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
M The, L Käll
Nature communications 11 (1), 3234, 2020
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
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
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
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
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
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
A simple null model for inferences from network enrichment analysis
GS Jeuken, L Käll
PloS one 13 (11), e0206864, 2018
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
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
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), 1-10, 2019
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
Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities
M Ekvall, P Truong, W Gabriel, M Wilhelm, L Käll
Journal of Proteome Research 21 (5), 1359-1364, 2022
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
Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
M Palmblad, S Böcker, S Degroeve, O Kohlbacher, L Käll, WS Noble, ...
Journal of Proteome Research 21 (4), 1204-1207, 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
Pathway Analysis Through Mutual Information
GS Jeuken, L Käll
bioRxiv, 2022.06. 30.495461, 2022
Parallelized calculation of permutation tests
M Ekvall, M Höhle, L Käll
Bioinformatics 36 (22-23), 5392-5397, 2020
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
Predicting transmembrane topology and signal peptides with hidden Markov models
L Käll
PQDT-Global, 2006
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
Identifying Protein Haplotypes by Mass Spectrometry
J Vasicek, D Skiadopoulou, KG Kuznetsova, S Johansson, PR Njolstad, ...
bioRxiv, 2022.11. 21.517096, 2022
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
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
From sequence to structure to networks
N Yosef, L Käll
Genome Biology 9 (11), 1-3, 2008
Transmembrane topology prediction
L Käll, E Sonnhammer
Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 2004
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