Ralf Gabriels

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

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

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

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

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.

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

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 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.

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

This study benchmarks 12 DIA analysis workflows for shotgun proteomics samples, finding DIA-NN and Spectronaut in library-free mode offer highest sensitivity with good reproducibility and accuracy.

The paper introduces TargetDecoy, a Bioconductor package to assess and control the quality of the target-decoy approach for peptide identification in proteomics.

MS2Rescore improves immunopeptide identification rates by integrating retrained MS2PIP, DeepLC, and Percolator, achieving higher spectrum identification rates and unique peptide counts.

The paper evaluates consensus spectrum generation methods in proteomics, finding BEST and BIN to be the most reliable for peptide identification.

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

DeepLC predicts retention times for modified peptides, improving identification accuracy in LC-MS workflows by handling unseen modifications.

ionbot improves peptide identification in mass spectrometry by integrating machine learning and open modification searches, increasing PSM confidence and identification rates.

The paper introduces the Universal Spectrum Identifier (USI) to standardize referencing mass spectra in proteomics repositories, enabling transparent traceability of spectral evidence in publications.

The paper compares proteogenomics and open variant search approaches for detecting single amino acid variants in shotgun proteomics data using NA12878 cell line data.

The paper evaluates integrating Percolator into COSS for improved peptide identification sensitivity and specificity using multiple datasets and search tools.

COSS is a spectral library search tool that identifies more peptides faster than existing tools and supports multiple data formats.

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 updates the MS2PIP web server to improve prediction accuracy for various fragmentation methods and labeling techniques, while reducing computational resources.