Oliver M. Bernhardt

发表

High‐precision iRT prediction in the targeted analysis of data‐independent acquisition and its impact on identification and quantitation

Roland Bruderer, Oliver M. Bernhardt, Tejas Gandhi, 2016, Proteomics.

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries

Oliver M. Bernhardt, Dorte B. Bekker-Jensen, Alexander Hogrebe, 2019, Nature Communications.

A Tale of Two - Data Independent Acquisition applied to maximize proteome coverage and throughput

Loïc Dayon, Roland Bruderer, Oliver M. Bernhardt, 2017 .

A novel algorithm for protein profiling based on cross-run correlations implemented in Spectronaut

Roland Bruderer, Oliver M. Bernhardt, Lukas Reiter, 2012 .

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries

Oliver M. Bernhardt, Tejas Gandhi, Lukas Reiter, 2019, bioRxiv.

A multi-center study benchmarks software tools for label-free proteome quantification

Yasset Perez-Riverol, Brendan MacLean, Ruedi Aebersold, 2016, Nature Biotechnology.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance

Loïc Dayon, Roland Bruderer, Oliver M. Bernhardt, 2019, Molecular & Cellular Proteomics.

Extending the limits of quantitative proteome profiling with data-independent acquisition

Olga Vitek, Roland Bruderer, Oliver M. Bernhardt, 2015 .

Spectronaut A fast and efficient algorithm for MRM-like processing of data independent acquisition (SWATH-MS) data

Ruedi Aebersold, Oliver M. Bernhardt, Lukas Reiter, 2012 .

Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues*

Oliver M. Bernhardt, Saša M. Miladinović, Roland Bruderer, 2015, Molecular & Cellular Proteomics.

Revealing dynamic protein acetylation across subcellular compartments.

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2020, Journal of proteome research.

A novel algorithm for protein profiling based on cross-run correlations in SWATH-MS data implemented in Spectronaut

Oliver M. Bernhardt, Saša M. Miladinović, Roland Bruderer, 2014 .

Analysis of post translational modifications using DIA with high resolution MS1 and high resolution retention time prediction

Oliver M. Bernhardt, Roland Bruderer, Tejas Gandhi, 2015 .

Optimized spectral library generation for HRM/DIA/SWATH as implemented within spectronaut

Oliver M. Bernhardt, Roland Bruderer, Tejas Gandhi, 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"

Oliver M. Bernhardt, Saša M. Miladinović, Roland Bruderer, 2015 .

Single shot deep DIA methods with optimal coverage, reproducibility and quantification precision

Oliver M. Bernhardt, Roland Bruderer, Tejas Gandhi, 2016 .

Reproducible and Consistent Quantification of the Saccharomyces cerevisiae Proteome by SWATH-mass spectrometry*

Oliver M. Bernhardt, Ludovic C. Gillet, Oliver M Bernhardt, 2015, Molecular & Cellular Proteomics.

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results*

Oliver M. Bernhardt, Oliver M Bernhardt, Roland Bruderer, 2017, Molecular & Cellular Proteomics.

WITHDRAWN: Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics.

Roland Bruderer, Oliver M. Bernhardt, Lukas Reiter, 2017, Molecular & cellular proteomics : MCP.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Oliver M. Bernhardt, A. Valsesia, A. Astrup, 2019, Molecular & cellular proteomics : MCP.

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy.

Roland Bruderer, Oliver M. Bernhardt, Lukas Reiter, 2019, Molecular omics.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets

Yasset Perez-Riverol, Alexey I Nesvizhskii, Guo Ci Teo, 2020, Nature Methods.

Revealing dynamic protein acetylation across subcellular compartments.

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2018, Journal of proteome research.

Quantifying dynamic protein acetylation using quantitative stoichiometry

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2018, bioRxiv.

Data-independent Acquisition Improves Quantitative Cross-linking Mass Spectrometry*

Oliver M. Bernhardt, Oliver M Bernhardt, L. Reiter, 2019, Molecular & Cellular Proteomics.

Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2023, bioRxiv.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.

Oliver M. Bernhardt, L. Reiter, B. MacLean, 2023, Journal of proteome research.

Data-independent Acquisition Improves Quantitative Cross-linking Mass Spectrometry.

Oliver M. Bernhardt, L. Reiter, J. Rappsilber, 2019, Molecular & cellular proteomics : MCP.

General guidelines for validation of decoy models for HRM/DIA/SWATH as exemplified using spectronaut

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2015 .

Revealing dynamic protein acetylation across subcellular compartments.

Oliver M. Bernhardt, Tejas Gandhi, L. Reiter, 2020, Journal of proteome research.