Isabell Bludau

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.

This study identifies local translation as a driver of glioblastoma tumor microtubes and invasion by linking subcellular protein synthesis to invasive cell states.

ProteoPy provides a framework for integrated proteomics analysis using AnnData, enabling proteoform group inference and differential analysis with integration into multi-omics workflows.

AlphaPept is an open-source framework for efficient processing of large MS-based proteomics datasets, achieving significant speed improvements using Python and machine learning.

The paper develops a high-throughput AP-MS method to map a comprehensive yeast interactome, doubling the number of proteins and tripling reliable interactions.

This paper introduces a multiplex-DIA workflow that enhances single-cell proteome depth using dimethyl labeling and a reference channel, achieving reliable quantification of nearly 4,000 proteins per cell.

AlphaPeptDeep predicts peptide properties using deep learning, offering a modular framework that matches or exceeds existing tools for retention time, ion mobility, and fragment intensity prediction.

The paper determines the structural context of PTMs across the human proteome and identifies regulatory sites by analyzing their distribution in folded and disordered regions.

The paper presents a rapid workflow combining experimental and computational methods to profile protein complex re-organization in cellular states, showing distinct changes during differentiation and macrophage stimulation.

This study uses phosphoproteomics to identify CDKs' role in TNF signaling, revealing their importance in cytokine production and cell death prevention.

AlphaMap visually annotates proteomics data with sequence-specific knowledge, enabling integration of experimental results with UniProt annotations and proteolytic cleavage sites.

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.

The paper develops a DIA-based workflow for ubiquitinome analysis, revealing circadian regulation through novel ubiquitination sites and clusters.

This study uses proteomics to analyze dynamic phospho-signaling networks in human erythropoiesis, identifying key regulatory mechanisms and markers across differentiation stages.

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

The paper introduces PCprophet, a computational framework for identifying and analyzing protein complexes from co-fractionation mass spectrometry data across conditions.

The paper introduces a new DIA scan mode that improves peptide precursor ion sampling and identification specificity using ion mobility, achieving high proteome coverage and accuracy.

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

This study integrates transcriptome, proteome, and phosphoproteome data from yeast to show how genetic variation affects molecular networks and cellular growth traits, with phosphorylation playing a key role.

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

This study quantifies genomic, proteomic, and phenotypic variability in HeLa cells across laboratories and passages, revealing implications for research reproducibility.

ACEseq estimates allele-specific copy numbers, tumor purity, ploidy, and homologous recombination deficiency scores from tumor whole-genome sequencing by combining GC-bias correction, replication-timing correction, and structural variant breakpoints.