Lukas Käll

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William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Michael MacCossProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Erik SonnhammerProfessor of Bioinformatics, DBB Stockholm University, Science for Life LaboratoryVerified email at scilifelab.se
Matthew TheTU MunichVerified email at tum.de
Viktor GranholmStockholm UniversityVerified email at scilifelab.se
Anders KroghDepartment of Computer Science and Center for Health Data Science, University of CopenhagenVerified email at di.ku.dk
John D. StoreyProfessor, Princeton UniversityVerified email at princeton.edu
Jason WestonMetaVerified email at fb.com
Arne ElofssonScience for Life Laboratory and department of Biochemistry and Biophysics, Stockholm UniversityVerified email at bioinfo.se
Michael R. HoopmannInstitute for Systems BiologyVerified email at systemsbiology.org
Fredrik EdforsThe Royal Institute of Technology (KTH)Verified email at scilifelab.se
Konstantinos Tsirigos, PhD Bioinforma...DTU BiosustainVerified email at dtu.dk
Samuel H PayneProfessor, Brigham Young UniversityVerified email at byu.edu
Mathias UhlénScience for Life Laboratory, KTH, StockholmVerified email at scilifelab.se
Janne LehtiöKarolinska Institutet, SciLifeLab, Stockholm, SwedenVerified email at scilifelab.se
Nanjiang ShuDepartment of Biochemistry and Biophysics, Stockholm UniversityVerified email at scilifelab.se
Jeffrey A. BilmesProfessor of Electrical & Computer Engineering & Computer Science, University of Washington SeattleVerified email at uw.edu
Michael RiffleUniversity of WashingtonVerified email at uw.edu
Sangtae KimBertis Bioscience IncVerified email at bertis.com
Richard D SmithChief Scientist and Battelle Fellow, Biological Sciences Division, PNNL; retired 2/2024Verified email at pnnl.gov

Lukas Käll

Professor, Science for Life Laboratory, KTH Royal Institute of Technology

Verified email at kth.se - Homepage

Computational Biology Proteomics Machine learning


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A combined transmembrane topology and signal peptide prediction method L Käll, A Krogh, ELL Sonnhammer Journal of molecular biology 338 (5), 1027-1036, 2004	2630	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	2363	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	1760	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	898	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	767	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	510	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	380	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	368	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	306	2009
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	303	2016
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	298	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	288	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	258	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	240	2008
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	237	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	216	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	186	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	166	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	155	2014
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	118	2018

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