Lennart Martens

This study optimizes a DDA-PASEF-based immunopeptidomics workflow to enhance HLA class I peptide coverage in low-input samples.

The paper examines how changes to UniProtKB and taxonomic restrictions affect metaproteomics analysis, finding reduced peptide mapping coverage but stable dominant taxonomic profiles.

OmicsFM is a proteomics foundation model that matches transcriptomic models in performance, revealing complementary biology between proteomics and transcriptomics.

This paper demonstrates that using ionbot with OpenProt improves detection of non-canonical proteins by reducing FDR and increasing reproducibility compared to TPP.

Scop3P-Toolkit provides an open-source framework for integrating PTMs, mutations, and proteomics data with structural and functional annotations to enable reproducible analysis.

The paper evaluates non-linear models for peptide-spectrum match rescoring in proteomics, finding that complex models increase PSMs but introduce biased FDR estimates.

This study establishes quality control standards for single-cell proteomics through inter-laboratory benchmarking, identifying software and technical factors affecting data reliability.

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

Scop3P is updated to integrate phosphorylation sites with structural, functional, and mutational data, enhancing phosphosite interpretation and disease context.

MLMarker computes tissue similarity scores for proteomics data, identifies tissue origins in biofluids, and provides interpretable predictions using a Random Forest model.

gamdid is a statistical framework for differential distribution analysis in single-cell proteomics, detecting shape differences and outperforming existing methods for such cases.

The paper introduces a deep learning model to predict peptide retention times in liquid chromatography and identifies key factors influencing retention through saliency mapping.

The paper introduces Peptonizer2000, a tool that improves taxonomic identification in metaproteomics by combining peptide scores and taxon links with statistical models, showing higher specificity and confidence.

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

The paper presents the E. coli PeptideAtlas build, a comprehensive pan-proteome resource covering 4,755 proteins and over 10,000 PTM sites from 40 datasets.

MoDPA infers modification-dependent protein associations from reprocessed mass spectrometry data, identifying clusters of modified proteins involved in specific biological pathways.

quantms-rescoring improves proteomics analysis by integrating multiple search engines and machine learning, increasing identified spectra and quantifying more phosphorylated peptides and modification sites.

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

The paper evaluates AlphaFold models' ability to predict phosphorylation-induced structural changes in proteins using experimentally derived conformational ensembles.

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

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

The paper improves IM2Deep to predict collisional cross-sections for peptides with multiple conformations, using a comprehensive dataset and tailored architecture.

DeepLC uses transfer learning to improve LC retention time prediction across different experimental setups and peptide modifications.

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

omicsGMF integrates dimensionality reduction, batch correction, and imputation for bulk and single-cell proteomics data, improving statistical power for differential abundance detection.

The paper improves bacterial epitope discovery by integrating multiple search engines and rescoring, increasing peptide identification by 27% and detecting 18 new bacterial peptides.

MS2Rescore improves peptide identification and taxonomic specificity in metaproteomics by using machine learning-based rescoring, achieving lower false discovery rates with minimal sensitivity loss.

PathwayPilot is a web tool for visualizing and comparing metabolic pathways using metaproteomic data, demonstrating its effectiveness in analyzing enzyme expression shifts in gut microbiota under caloric restriction.

The paper updates the Unipept software to support analysis of non-tryptic and semi-tryptic peptides, improving performance while maintaining accuracy.

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