Sander Willems

This paper optimizes diagonal-PASEF mass spectrometry with retention time down-sampling, improving identifications and quantification in proteomics.

AlphaDIA enables end-to-end transfer learning for feature-free proteomics, improving identification and quantification of post-translational modifications.

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 introduces directLFQ, a label-free quantification method that scales linearly with sample numbers, enabling rapid analysis of large proteomics datasets.

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.

AlphaViz validates proteomics results by visualizing raw data and comparing with automated analysis outputs.

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

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.

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

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.

AlphaTims enables fast access and visualization of high-dimensional LC-TIMS-QTOF proteomics data through efficient indexing and just-in-time compilation.

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.

This study evaluates the use of Oxford Nanopore MinION sequencer for forensic STR profiling using standard primers and shows it can generate partial forensic profiles despite high error rates.