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Publications by Lukas Käll

Peer reviewed

Articles

[1]
G. S. Jeuken and L. Käll, "Pathway analysis through mutual information," Bioinformatics, vol. 40, no. 1, 2024.
[2]
J. Vasicek et al., "Finding haplotypic signatures in proteins," GigaScience, vol. 12, 2023.
[3]
[4]
B. A. Neely et al., "Toward an Integrated Machine Learning Model of a Proteomics Experiment," Journal of Proteome Research, vol. 22, no. 3, pp. 681-696, 2023.
[5]
P. Truong, M. The and L. Käll, "Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry," Journal of Proteome Research, vol. 22, no. 4, pp. 1359-1366, 2023.
[6]
X. Luo et al., "A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics," Journal of Proteome Research, vol. 21, no. 6, pp. 1566-1574, 2022.
[7]
M. Palmblad et al., "Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics," Journal of Proteome Research, vol. 21, no. 4, pp. 1204-1207, 2022.
[8]
M. Ekvall et al., "Prosit Transformer : A transformer for Prediction of MS2 Spectrum Intensities," Journal of Proteome Research, vol. 21, no. 5, pp. 1359-1364, 2022.
[9]
D. L. Plubell et al., "Putting Humpty Dumpty Back Together Again : What Does Protein Quantification Mean in Bottom-Up Proteomics? br," Journal of Proteome Research, vol. 21, no. 4, pp. 891-898, 2022.
[10]
G. S. Jeuken, N. P. Tobin and L. Käll, "Survival analysis of pathway activity as a prognostic determinant in breast cancer," PloS Computational Biology, vol. 18, no. 3, 2022.
[11]
K. Lucke et al., "Performing Selection on a Monotonic Function in Lieu of Sorting Using Layer-Ordered Heaps," Journal of Proteome Research, vol. 20, no. 4, pp. 1849-1854, 2021.
[13]
M. The and L. Käll, "Triqler for MaxQuant : Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration," Journal of Proteome Research, vol. 20, no. 4, pp. 2062-2068, 2021.
[14]
M. The and L. Käll, "Focus on the spectra that matter by clustering of quantification data in shotgun proteomics," Nature Communications, vol. 11, no. 1, 2020.
[15]
M. Ekvall, M. Hohle and L. Käll, "Parallelized calculation of permutation tests," Bioinformatics, vol. 36, no. 22-23, pp. 5392-5397, 2020.
[16]
C. Ashwood et al., "Proceedings of the EuBIC-MS 2020 Developers’ Meeting," EuPA Open Proteomics, vol. 24, pp. 1-6, 2020.
[17]
[18]
M. The and L. Käll, "Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics," Molecular & Cellular Proteomics, vol. 18, no. 3, pp. 561-570, 2019.
[19]
J. T. Halloran et al., "Speeding Up Percolator," Journal of Proteome Research, vol. 18, no. 9, pp. 3353-3359, 2019.
[20]
M. The et al., "A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms," Journal of Proteome Research, vol. 17, no. 5, pp. 1879-1886, 2018.
[21]
G. S. Jeuken and L. Käll, "A simple null model for inferences from network enrichment analysis," PLOS ONE, vol. 13, no. 11, 2018.
[22]
J. -. Lee et al., "ABRF Proteome Informatics Research Group (iPRG) 2016 Study : Inferring Proteoforms from Bottom-up Proteomics Data," Journal of biomolecular techniques : JBT, vol. 29, no. 2, pp. 39-45, 2018.
[23]
E. W. Deutsch et al., "Expanding the Use of Spectral Libraries in Proteomics," Journal of Proteome Research, vol. 17, no. 12, pp. 4051-4060, 2018.
[25]
J. Griss et al., "Response to "Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra"," Journal of Proteome Research, vol. 17, no. 5, pp. 1993-1996, 2018.
[26]
B. Zhang et al., "Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences," Molecular & Cellular Proteomics, vol. 16, no. 5, pp. 936-948, 2017.
[27]
[28]
B. Zhang, L. Käll and R. A. Zubarev, "DeMix-Q : Quantification-Centered Data Processing Workflow," Molecular & Cellular Proteomics, vol. 15, no. 4, pp. 1467-1478, 2016.
[29]
M. The et al., "Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0," Journal of the American Society for Mass Spectrometry, vol. 27, no. 11, pp. 1719-1727, 2016.
[30]
F. Edfors et al., "Gene-specific correlation of RNA and protein levels in human cells and tissues," Molecular Systems Biology, vol. 12, no. 10, 2016.
[31]
M. The, A. Tasnim and L. Käll, "How to talk about protein-level false discovery rates in shotgun proteomics," Proteomics, vol. 16, no. 18, pp. 2461-2469, 2016.
[32]
M. The and L. Käll, "MaRaCluster : A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics," Journal of Proteome Research, vol. 15, no. 3, pp. 713-720, 2016.
[33]
L. Moruz and L. Käll, "Peptide retention time prediction," Mass spectrometry reviews (Print), 2016.
[34]
B. Wen et al., "IPeak : An open source tool to combine results from multiple MS/MS search engines," Proteomics, vol. 15, no. 17, pp. 2916-2920, 2015.
[35]
J. Boekel et al., "Multi-omic data analysis using Galaxy," Nature Biotechnology, vol. 33, no. 2, pp. 137-9, 2015.
[36]
Y. S. Ting et al., "Peptide-Centric Proteome Analysis : An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data," Molecular & Cellular Proteomics, vol. 14, no. 9, pp. 2301-2307, 2015.
[37]
S. McIlwain et al., "Crux : Rapid Open Source Protein Tandem Mass Spectrometry Analysis," Journal of Proteome Research, vol. 13, no. 10, pp. 4488-4491, 2014.
[38]
V. Granholm et al., "Fast and accurate database searches with MS-GF+percolator," Journal of Proteome Research, vol. 13, no. 2, pp. 890-897, 2014.
[40]
R. M. M. Branca et al., "HiRIEF LC-MSMS enables deep proteome coverage and unbiased proteogenomics," Nature Methods, vol. 11, no. 1, pp. 59, 2014.
[41]
[42]
[43]
M. Bendz et al., "Membrane protein shaving with thermolysin can be used to evaluate topology predictors," Proteomics, vol. 13, no. 9, pp. 1467-1480, 2013.
[44]
O. Serang et al., "Nonparametric bayesian evaluation of differential protein quantification," Journal of Proteome Research, vol. 12, no. 10, pp. 4556-4565, 2013.
[45]
L. Moruz et al., "Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics," Analytical Chemistry, vol. 85, no. 16, pp. 7777-7785, 2013.
[46]
V. Granholm, W. S. Noble and L. Käll, "A cross-validation scheme for machine learning algorithms in shotgun proteomics," BMC Bioinformatics, vol. 13, no. S16, pp. S3, 2012.
[47]
L. Moruz et al., "Chromatographic retention time prediction for posttranslationally modified peptides," Proteomics, vol. 12, no. 8, pp. 1151-1159, 2012.
[48]
J. C. Wright et al., "Enhanced peptide identification by electron transfer dissociation using an improved mascot percolator," Molecular & Cellular Proteomics, vol. 11, no. 8, pp. 478-491, 2012.
[49]
O. Serang et al., "Recognizing Uncertainty Increases Robustness and Reproducibility of Mass Spectrometry-based Protein Inferences," Journal of Proteome Research, vol. 11, no. 12, pp. 5586-5591, 2012.
[50]
L. Käll and O. Vitek, "Computational Mass Spectrometry-Based Proteomics," PloS Computational Biology, vol. 7, no. 12, pp. e1002277, 2011.
[51]
V. Granholm, W. S. Noble and L. Käll, "On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics," Journal of Proteome Research, vol. 10, no. 5, pp. 2671-2678, 2011.
[52]
V. Granholm and L. Käll, "Quality assessments of peptide-spectrum matches in shotgun proteomics," Proteomics, vol. 11, no. 6, pp. 1086-1093, 2011.
[53]
L. Moruz, D. Tomazela and L. Käll, "Training, selection, and robust calibration of retention time models for targeted proteomics," Journal of Proteome Research, vol. 9, no. 10, pp. 5209-5216, 2010.
[54]
M. Spivak et al., "Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets," Journal of Proteome Research, vol. 8, no. 7, pp. 3737-3745, 2009.
[55]
L. Käll, J. D. Storey and W. S. Noble, "QVALITY : non-parametric estimation of q-values and posterior error probabilities," Bioinformatics, vol. 25, no. 7, pp. 964-966, 2009.
[56]
L. Käll et al., "Assigning significance to peptides identified by tandem mass spectrometry using decoy databases," Journal of Proteome Research, vol. 7, no. 1, pp. 29-34, 2008.
[57]
N. Yosef and L. Käll, "From sequence to structure to networks," Genome Biology, vol. 9, no. 11, 2008.
[58]
L. Käll, J. D. Storey and W. S. Noble, "Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry," Bioinformatics, vol. 24, no. 16, pp. i42-i48, 2008.
[59]
L. Käll et al., "Posterior error probabilities and false discovery rates : two sides of the same coin," Journal of Proteome Research, vol. 7, no. 1, pp. 40-44, 2008.
[60]
C. Y. Park et al., "Rapid and accurate peptide identification from tandem mass spectra," Journal of Proteome Research, vol. 7, no. 7, pp. 3022-3027, 2008.
[61]
S. M. Reynolds et al., "Transmembrane topology and signal peptide prediction using dynamic bayesian networks," PloS Computational Biology, vol. 4, no. 11, pp. e1000213, 2008.
[62]
G. E. Merrihew et al., "Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations," Genome Research, vol. 18, no. 10, pp. 1660-1669, 2008.
[63]
L. Käll, A. Krogh and E. L. L. Sonnhammer, "Advantages of combined transmembrane topology and signal peptide prediction - the Phobius web server," Nucleic Acids Research, vol. 35, no. Web Server issue, 1, pp. W429-W432, 2007.
[64]
C. Lundin et al., "Membrane topology of the Drosophila OR83b odorant receptor," FEBS Letters, vol. 581, no. 29, pp. 5601-5604, 2007.
[65]
L. Käll et al., "Semi-supervised learning for peptide identification from shotgun proteomics datasets," Nature Methods, vol. 4, no. 11, pp. 923-925, 2007.
[66]
M. Wistrand, L. Käll and E. L. L. Sonnhammer, "A general model of G protein-coupled receptor sequences and its application to detect remote homologs," Protein Science, vol. 15, no. 3, pp. 509-521, 2006.
[67]
A. Henricson, L. Käll and E. L. L. Sonnhammer, "A novel transmembrane topology of presenilin based on reconciling experimental and computational evidence," The FEBS Journal, vol. 272, no. 11, pp. 2727-2733, 2005.
[68]
L. Käll, A. Krogh and E. L. L. Sonnhammer, "An HMM posterior decoder for sequence feature prediction that includes homology information," Bioinformatics, vol. 21, no. Suppl.1, pp. i251-i257, 2005.
[69]
L. Käll, A. Krogh and E. L. L. Sonnhammer, "A combined transmembrane topology and signal peptide prediction method," Journal of Molecular Biology, vol. 338, no. 5, pp. 1027-1036, 2004.
[70]
L. Käll and E. L. L. Sonnhammer, "Reliability of transmembrane predictions in whole-genome data," FEBS Letters, vol. 532, no. 3, pp. 415-418, 2002.

Conference papers

[71]
J. Freestone et al., "Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis," in Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings, 2024, pp. 448-453.
[72]
O. Emanuelsson, L. Arvestad and L. Käll, "Engagera och aktivera studenter med inspiration från konferenser : examination genom poster-presentation," in Proceedings 2014, 8:e Pedagogiska inspirationskonferensen 17 december 2014, 2014.

Chapters in books

[73]
L. Käll, "Prediction of transmembrane topology and signal peptide given a protein's amino acid sequence," in Computational Biology, David Fenyö Ed., : Springer, 2010, pp. 53-62.

Non-peer reviewed

Articles

[74]
O. Serang and L. Käll, "Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less," Journal of Proteome Research, vol. 14, no. 10, pp. 4099-4103, 2015.

Chapters in books

[76]
M. The and L. Käll, "Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler," in Methods in Molecular Biology, : Humana Press Inc., 2023, pp. 91-117.
Latest sync with DiVA:
2024-06-16 02:16:42