Oliver M. Bernhardt
Oliver M. Bernhardt
Principal Scientist Bioinformatics at Biognosys
Verified email at biognosys.com - Homepage
Title
Cited by
Cited by
Year
Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues
R Bruderer, OM Bernhardt, T Gandhi, SM Miladinović, LY Cheng, ...
Molecular & Cellular Proteomics 14 (5), 1400-1410, 2015
3232015
Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry
N Selevsek, CY Chang, LC Gillet, P Navarro, OM Bernhardt, L Reiter, ...
Molecular & Cellular Proteomics 14 (3), 739-749, 2015
1422015
A multicenter study benchmarks software tools for label-free proteome quantification
P Navarro, J Kuharev, LC Gillet, OM Bernhardt, B MacLean, HL Röst, ...
Nature biotechnology 34 (11), 1130-1136, 2016
1362016
Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results
R Bruderer, OM Bernhardt, T Gandhi, Y Xuan, J Sondermann, M Schmidt, ...
Molecular & Cellular Proteomics 16 (12), 2296-2309, 2017
1132017
High‐precision iRT prediction in the targeted analysis of data‐independent acquisition and its impact on identification and quantitation
R Bruderer, OM Bernhardt, T Gandhi, L Reiter
Proteomics 16 (15-16), 2246-2256, 2016
762016
Spectronaut: A fast and efficient algorithm for MRM-like processing of data independent acquisition (SWATH-MS) data
OM Bernhardt, N Selevsek, LC Gillet, O Rinner, P Picotti, R Aebersold, ...
Biognosys. ch, 2012
302012
Analysis of 1508 plasma samples by capillary-flow data-independent acquisition profiles proteomics of weight loss and maintenance
R Bruderer, J Muntel, S Müller, OM Bernhardt, T Gandhi, O Cominetti, ...
Molecular & Cellular Proteomics 18 (6), 1242-1254, 2019
232019
Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries
DB Bekker-Jensen, OM Bernhardt, A Hogrebe, A Martinez-Val, L Verbeke, ...
Nature communications 11 (1), 1-12, 2020
132020
Surpassing 10000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy
J Muntel, T Gandhi, L Verbeke, OM Bernhardt, T Treiber, R Bruderer, ...
Molecular omics 15 (5), 348-360, 2019
102019
Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics
R Bruderer, OM Bernhardt, T Gandhi, Y Xuan, J Sondermann, M Schmidt, ...
Molecular & Cellular Proteomics, 2017
42017
Data-independent acquisition improves quantitative cross-linking mass spectrometry
F Müller, L Kolbowski, OM Bernhardt, L Reiter, J Rappsilber
Molecular & Cellular Proteomics 18 (4), 786-795, 2019
32019
Quantifying dynamic protein acetylation using quantitative stoichiometry
J Baeza, AJ Lawton, J Fan, MJ Smallegan, I Lienert, T Gandhi, ...
bioRxiv, 472530, 2018
22018
Comparison of DIA and Shotgun Quantitation on a Thermo Q Exactive
RM Bruderer, OM Bernhardt, TP Gandhi, SM Miladinović, R Ossola, ...
2*
Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries
DB Bekker-Jensen, OM Bernhardt, A Hogrebe, AM del Val, L Verbeke, ...
bioRxiv, 657858, 2019
12019
Revealing dynamic protein acetylation across subcellular compartments
J Baeza, AJ Lawton, J Fan, MJ Smallegan, I Lienert, T Gandhi, ...
Journal of Proteome Research, 2020
2020
A Tale of Two-Data Independent Acquisition applied to maximize proteome coverage and throughput
RM Bruderer, OM Bernhardt, T Gandhi, J Muntel, S Muller, P Mironova, ...
Molecular & Cellular Proteomics 16 (8), S25-S25, 2017
2017
WITHDRAWN: Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics.
R Bruderer, OM Bernhardt, T Gandhi, Y Xuan, J Sondermann, M Schmidt, ...
Molecular & Cellular Proteomics: MCP, 2017
2017
Analysis of post translational modifications using DIA with high resolution MS1 and high resolution retention time prediction
R Bruderer, OM Bernhardt, T Gandhi, L Reiter
F1000Research 6, 2015
2015
Highly multiplexed protein profiling across large sets of samples: A comparison of DIA/HRM-MS acquisition with shotgun LC-MS/MS using a “Gold Standard Sample Set”
RM Bruderer, SM Miladinović, OM Bernhardt, O Rinner, R Aebersold, ...
F1000Research 6, 2015
2015
General guidelines for validation of decoy models for HRM/DIA/SWATH as exemplified using Spectronaut
OM Bernhardt, RM Bruderer, T Gandhi, SM Miladinović, M Bober, ...
F1000Research 6, 2015
2015
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Articles 1–20