E-LIB Suche - Ergebnisse für: Bernhardt, Oliver M.

1

MSstats Version 4.0: Statistical Analyses of Quantitative M..:

Kohler, Devon ; Staniak, Mateusz ; Tsai, Tsung-Heng...
Journal of Proteome Research. 22 (2023) 5 - p. 1466-1482 , 2023

Link: https://doi.org/10.1021/..

2

Revealing Dynamic Protein Acetylation across Subcellular Co..:

Baeza, Josue ; Lawton, Alexis J. ; Fan, Jing...
Journal of Proteome Research. 19 (2020) 6 - p. 2404-2418 , 2020

Link: https://doi.org/10.1021/..

3

Rapid and site-specific deep phosphoproteome profiling by d..:

Bekker-Jensen, Dorte B. ; Bernhardt, Oliver M. ; Hogrebe, Alexander...
Nature Communications. 11 (2020) 1 - p. , 2020

Link: https://doi.org/10.1038/..

4

Surpassing 10 000 identified and quantified proteins in a s..:

Muntel, Jan ; Gandhi, Tejas ; Verbeke, Lynn...
Molecular Omics. 15 (2019) 5 - p. 348-360 , 2019

Link: https://doi.org/10.1039/..

5

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Inde..:

Bruderer, Roland ; Muntel, Jan ; Müller, Sebastian...
Molecular & Cellular Proteomics. 18 (2019) 6 - p. 1242-1254 , 2019

Link: https://doi.org/10.1074/..

6

Data-independent Acquisition Improves Quantitative Cross-li..:

Müller, Fränze ; Kolbowski, Lars ; Bernhardt, Oliver M...
Molecular & Cellular Proteomics. 18 (2019) 4 - p. 786-795 , 2019

Link: https://doi.org/10.1074/..

7

Optimization of Experimental Parameters in Data-Independent..:

Bruderer, Roland ; Bernhardt, Oliver M. ; Gandhi, Tejas...
Molecular & Cellular Proteomics. 16 (2017) 12 - p. 2296-2309 , 2017

Link: https://doi.org/10.1074/..

8

High‐precision iRT prediction in the targeted analysis of d..:

Bruderer, Roland ; Bernhardt, Oliver M. ; Gandhi, Tejas.
PROTEOMICS. 16 (2016) 15-16 - p. 2246-2256 , 2016

Link: https://doi.org/10.1002/..

9

A multicenter study benchmarks software tools for label-fre..:

Navarro, Pedro ; Kuharev, Jörg ; Gillet, Ludovic C...
Nature Biotechnology. 34 (2016) 11 - p. 1130-1136 , 2016

Link: https://doi.org/10.1038/..

10

Extending the Limits of Quantitative Proteome Profiling wit..:

Bruderer, Roland ; Bernhardt, Oliver M. ; Gandhi, Tejas...
Molecular & Cellular Proteomics. 14 (2015) 5 - p. 1400-1410 , 2015

Link: https://doi.org/10.1074/..

11

Reproducible and Consistent Quantification of the Saccharom..:

Selevsek, Nathalie ; Chang, Ching-Yun ; Gillet, Ludovic C....
Molecular & Cellular Proteomics. 14 (2015) 3 - p. 739-749 , 2015

Link: https://doi.org/10.1074/..

12

MSstats Version 4.0: Statistical Analyses of Quantitative M..:

Kohler, Devon ; Staniak, Mateusz ; Tsai, Tsung-Heng...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629259/. , 2023

Link: http://www.ncbi.nlm.nih...

13

Rapid and site-specific deep phosphoproteome profiling by d..:

Bekker-Jensen, Dorte B ; Bernhardt, Oliver M ; Hogrebe, Alexander...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005859/. , 2020

Link: http://www.ncbi.nlm.nih...

14

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Inde..:

Bruderer, Roland ; Muntel, Jan ; Müller, Sebastian...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553938/. , 2019

Link: http://www.ncbi.nlm.nih...

15

Data-independent Acquisition Improves Quantitative Cross-li..:

Müller, Fränze ; Kolbowski, Lars ; Bernhardt, Oliver M..
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442367/. , 2019

Link: http://www.ncbi.nlm.nih...