George Rosenberger

Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

The paper introduces a multi-omics framework to analyze protein activity in context-specific networks, demonstrating its utility in linking molecular profiles to phenotypic outcomes using HeLa and Kyoto cell lines.

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

TIMS2Rescore is a data-driven rescoring pipeline optimized for DDA-PASEF data that improves protein identification in proteomics analyses.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

The paper uses phosphoproteomic time-series data and the VESPA algorithm to analyze drug resistance mechanisms in colon cancer cells, revealing adaptive signaling rewiring confirmed by CRISPRko assays.

A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events

The paper introduces SEC-MX, a method to simultaneously study protein assembly-states and phosphorylation events, and demonstrates its application in HEK293 and HCT116 cells.

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

The paper demonstrates improved proteomic depth using DIA-NN and FragPipe for dia-PASEF data, achieving higher sensitivity and quantification in complex samples.

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

This paper introduces a data-driven method to detect functional proteoform groups from bottom-up proteomic datasets, identifying state-specific proteoform groups linked to molecular mechanisms.

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

DIAproteomics is an automated pipeline for analyzing DIA proteomics and peptidomics data, offering tools for spectral library generation, alignment, and statistical analysis.

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

SECAT quantifies differential protein-protein interaction states across cellular states by analyzing PPI networks, showing improved scalability and effectiveness over previous methods.

Quantitative Method for Assessing the Role of Lysine & Arginine Post-Translational Modifications in Nonalcoholic Steatohepatitis

The paper introduces a method to quantify lysine and arginine PTMs in NASH using mass spectrometry and applies it to mouse liver lysates, finding altered methylation and acetylation in NASH.

Global impact of phosphorylation on protein endurance

The paper introduces DeltaSILAC, a proteomic method to quantify how phosphorylation affects protein endurance in cancer cells, revealing its global impact on protein turnover and identifying sequence features associated with endurance changes.

A global screen for assembly state changes of the mitotic proteome by SEC-SWATH-MS

The paper quantifies proteome assembly state changes during mitosis using SEC-SWATH-MS and identifies new mitotic complex disassembly events.

Complex-centric proteome profiling by SEC-SWATH-MS

The paper introduces a method to quantify protein complexes using SEC-SWATH-MS and identifies 462 complexes from HEK293 cells.