Maarten Dhaenens

This paper develops a method to identify species in mixed palaeoproteomic samples by analyzing both physical and genetic mixtures, revealing new faunal information from ancient bone fragments.

This paper introduces a normalization framework to decouple protocol and instrument effects in multicenter proteomics studies, demonstrating its application to the PME13 dataset.

The paper constructs a multi-omics biomolecular network in AML using proteins, metabolites, and histone PTMs to reveal functional entanglement between biomolecules.

The paper uses mass spectrometry to profile histone post-translational modifications in T-ALL cell lines and identifies drug response signatures, but finds discrepancies between in vitro and in vivo models.

The paper generates a benchmark dataset to evaluate proteomics instruments and workflows using high-throughput LC gradients across multiple platforms and organisms.

iDeepLC improves retention time prediction for peptides with unseen modifications by incorporating chemical structural information, outperforming previous models.

ProteoBench provides a centralized platform for comparing proteomics data analysis workflows, enabling benchmarking and community collaboration.

This paper develops a mass spectrometry-based method to profile single-cell histone post-translational modifications, demonstrating its ability to detect treatment responses and chromatin heterogeneity.

This paper uses proteomics alongside use-wear and microscopy analyses to identify bovine, plant, and human proteins on Mesolithic-Neolithic tools, demonstrating the potential of palaeoproteomics for understanding prehistoric activities.

ClassiCOL improves species classification from ancient collagen LC-MS/MS data by resolving peptide ambiguity through isoBLAST and taxonomic branching analysis.

The paper develops a targeted LC-MS method to quantify SARS-CoV-2 spike protein expression and enables proteome profiling of infected cells.

The paper presents a comprehensive benchmark dataset for evaluating label-free quantification methods across various proteomics acquisition strategies.

The paper provides a roadmap for selecting a bottom-up mass spectrometry protocol for histone analysis by comparing four methods and introducing a new enzyme.

The paper removes the dependency on DDA libraries in DIA by using predicted spectral libraries, making the workflow deterministic and improving standardization in clinical proteomics.

The paper introduces ion-networks, a data format for DIA MS that captures full data integrity by representing reproducible fragment ions and their consistent co-elution across runs.