Lukas Käll
Lukas Käll
Professor, KTH Royal Institute of Technology, Science for Life Laboratory
Verified email at kth.se - Homepage
TitleCited byYear
A combined transmembrane topology and signal peptide prediction method
L Käll, A Krogh, ELL Sonnhammer
Journal of molecular biology 338 (5), 1027-1036, 2004
17772004
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, 2007
11492007
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
9342007
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, 2007
5392007
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
3202015
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
2742005
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, 2007
2432007
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
1992008
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
1952007
Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets
M Spivak, J Weston, L Bottou, L Käll, WS Noble
Journal of proteome research 8 (7), 3737-3745, 2009
1782009
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
1772006
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, 2014
1672014
qvality: non-parametric estimation of q-values and posterior error probabilities
L Käll, JD Storey, WS Noble
Bioinformatics 25 (7), 964-966, 2009
164*2009
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
1602008
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), 2016
1562016
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
972008
Reliability of transmembrane predictions in whole‐genome data
L Käll, ELL Sonnhammer
FEBS letters 532 (3), 415-418, 2002
792002
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
742015
Multi-omic data analysis using Galaxy
J Boekel, JM Chilton, IR Cooke, PL Horvatovich, PD Jagtap, L Käll, ...
Nature biotechnology 33 (2), 137, 2015
702015
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
692014
Training, selection, and robust calibration of retention time models for targeted proteomics
L Moruz, D Tomazela, L Käll
Journal of proteome research 9 (10), 5209-5216, 2010
682010
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
662005
Computational mass spectrometry–based proteomics
L Käll, O Vitek
PLoS computational biology 7 (12), e1002277, 2011
602011
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, 2013
532013
Quality assessments of peptide–spectrum matches in shotgun proteomics
V Granholm, L Käll
Proteomics 11 (6), 1086-1093, 2011
392011
Solution to statistical challenges in proteomics is more statistics, not less
O Serang, L Käll
Journal of proteome research 14 (10), 4099-4103, 2015
382015
DeMix-Q: quantification-centered data processing workflow
B Zhang, L Käll, RA Zubarev
Molecular & Cellular Proteomics 15 (4), 1467-1478, 2016
372016
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
372012
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
352012
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
342011
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
332012
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
312016
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
292013
Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences
O Serang, L Moruz, MR Hoopmann, L Käll
Journal of proteome research 11 (12), 5586-5591, 2012
292012
Peptide retention time prediction
L Moruz, L Käll
Mass spectrometry reviews 36 (5), 615-623, 2017
282017
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
262013
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
232017
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
232013
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
202015
A cross-validation scheme for machine learning algorithms in shotgun proteomics
V Granholm, WS Noble, L Käll
BMC bioinformatics 13 (16), S3, 2012
192012
How to talk about protein-level false discovery rates in shotgun proteomics
M The, A Tasnim, L Käll
Proteomics 16 (18), 2461–2469, 2016
172016
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 Käll
Journal of proteome research 12 (12), 5730-5741, 2013
142013
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
132013
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
122016
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
92018
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
92018
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
52010
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, ...
bioRxiv, 236471, 2017
42017
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
42014
Response to “Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra”
J Griss, Y Perez-Riverol, M The, L Käll, JA Vizcaíno
Journal of proteome research 17 (5), 1993-1996, 2018
32018
Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics
HM Afkham, X Qiu, M The, L Käll
Bioinformatics, btw619, 2016
3*2016
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
L Käll
BioRxiv, 488015, 2019
22019
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
12018
Predicting transmembrane topology and signal peptides with hidden Markov models
L Käll
Centrum för Genomik och Bioinformatik (CGB)/Center for Genomics Research, 2006
12006
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), 17, 2019
2019
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
2019
Integrated identification and quantification error probabilities for shotgun proteomics
L Käll
Molecular & Cellular Proteomics 18 (3), 561-570, 2019
2019
A simple null model for inferences from network enrichment analysis
GS Jeuken, L Käll
PloS one 13 (11), e0206864, 2018
2018
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 (11), 326, 2008
2008
Transmembrane topology prediction
L Käll, E Sonnhammer
Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 2004
2004
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