Matthew The
Matthew The
Verified email at tum.de - Homepage
Title
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 Kšll
Journal of The American Society for Mass Spectrometry 27 (11), 1719-1727, 2016
1092016
How to talk about protein‐level false discovery rates in shotgun proteomics
M The, A Tasnim, L Kšll
Proteomics 16 (18), 2461-2469, 2016
272016
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
202016
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
M The, L Kšll
Nature Communications 11 (1), 1-12, 2020
112020
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
112018
Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics
H Maboudi Afkham, X Qiu, M The, L Kšll
Bioinformatics 33 (4), 508-513, 2017
112017
Integrated identification and quantification error probabilities for shotgun proteomics
M The, L Kšll
Molecular & Cellular Proteomics 18 (3), 561-570, 2019
82019
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
52019
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
52018
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, Ń VťgvŠri, ...
Science Advances 7 (8), eabf0717, 2021
42021
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 Kšll, H Lam, ...
Journal of biomolecular techniques: JBT 29 (2), 39, 2018
42018
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
2021
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, 2021
2021
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, 2021
2021
Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration
M The, L Käll
Journal of proteome research 20 (4), 2062-2068, 2021
2021
Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler
M The, L Kšll
bioRxiv, 2020
2020
Statistical and machine learning methods to analyze large-scale mass spectrometry data
M The
KTH Royal Institute of Technology, Stockholm, Sweden., 2018
2018
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Articles 1–17