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Matthew The
Matthew The
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Cited by
Cited by
Year
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
Journal of The American Society for Mass Spectrometry 27 (11), 1719-1727, 2016
2932016
ProteomicsDB: toward a FAIR open-source resource for life-science research
L Lautenbacher, P Samaras, J Muller, A Grafberger, M Shraideh, J Rank, ...
Nucleic Acids Research, 2021
392021
How to talk about protein‐level false discovery rates in shotgun proteomics
M The, A Tasnim, L Käll
Proteomics 16 (18), 2461-2469, 2016
392016
Decrypting drug actions and protein modifications by dose-and time-resolved proteomics
J Zecha, FP Bayer, S Wiechmann, J Woortman, N Berner, J Müller, ...
Science 380 (6640), 93-101, 2023
332023
Mass spectrometry-based draft of the mouse proteome
P Giansanti, P Samaras, Y Bian, C Meng, A Coluccio, M Frejno, ...
Nature methods 19 (7), 803-811, 2022
332022
The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters
FA Schober, D Moore, I Atanassov, MF Moedas, P Clemente, Á Végvári, ...
Science Advances 7 (8), eabf0717, 2021
322021
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
322016
Integrated identification and quantification error probabilities for shotgun proteomics
M The, L Käll
Molecular & Cellular Proteomics 18 (3), 561-570, 2019
282019
Identification of 7 000–9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC–MS/MS
Y Bian, M The, P Giansanti, J Mergner, R Zheng, M Wilhelm, A Boychenko, ...
Analytical chemistry 93 (25), 8687-8692, 2021
252021
Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics
H Maboudi Afkham, X Qiu, M The, L Käll
Bioinformatics 33 (4), 508-513, 2017
232017
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
M The, L Käll
Nature Communications 11 (1), 1-12, 2020
182020
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
172018
Evaluation of disposable trap column nanoLC–FAIMS–MS/MS for the proteomic analysis of FFPE tissue
S Eckert, YC Chang, FP Bayer, M The, PH Kuhn, W Weichert, B Kuster
Journal of proteome research 20 (12), 5402-5411, 2021
152021
Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit
M Picciani, W Gabriel, VG Giurcoiu, O Shouman, F Hamood, ...
Proteomics 24 (8), 2300112, 2024
132024
Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups
M The, P Samaras, B Kuster, M Wilhelm
Molecular & Cellular Proteomics 21 (12), 2022
132022
Prosit-TMT: deep learning boosts identification of TMT-labeled peptides
W Gabriel, M The, DP Zolg, FP Bayer, O Shouman, L Lautenbacher, ...
Analytical Chemistry 94 (20), 7181-7190, 2022
132022
SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering
F Hamood, FP Bayer, M Wilhelm, B Kuster, M The
Molecular & Cellular Proteomics 21 (8), 2022
112022
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
92019
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
82018
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
72018
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Articles 1–20