Gergely Csaba

The paper introduces MS-EmpiReS, a method to detect differential alternative splicing in proteomics data, identifying cancer-relevant splicing candidates and showing correlation with RNA sequencing.

EmpiReS improves detection of differentially expressed and alternatively spliced genes using fold change analysis with empirical error estimation.

The paper benchmarks gene set enrichment methods using a curated compendium of disease datasets, identifying performance differences and providing recommendations for improved analysis.

MS-EmpiRe improves detection of differentially abundant proteins by using peptide-level noise distributions, doubling correct identifications at FDR cutoffs compared to existing tools.

The paper introduces a method to improve spectral library coverage in DIA proteomics by using multiple characteristic intensity patterns, achieving near-complete coverage without increasing false discovery rates.