Wout Bittremieux

The paper updates the spectrum_utils Python package to integrate mass spectrometry standards, enhance data processing, and unify visualization across proteomics and metabolomics applications.

The paper reanalyzes public mass spectrometry data to identify post-translational modifications in SARS-CoV-2-host protein interactions, revealing novel modification sites and their potential functional roles.

This study uses multi-omics analysis to show that aneuploidy in Leishmania causes proportional changes in transcript and protein abundance, with post-transcriptional regulation compensating for some effects.

The paper compares cosine, modified cosine, and neutral loss methods for MS/MS spectrum alignment to find structurally related molecules, finding modified cosine performs best.

This study uses native metabolomics to identify a family of protease inhibitors from cyanobacteria, revealing their structure and potent biological activity.

MassQL, an open-source query language, lets users search raw mass spectrometry data from any vendor for isotope, fragmentation and neutral-loss patterns, from one file to entire public repositories.

This paper introduces a deep learning model called GLEAMS to embed mass spectra into a low-dimensional space for efficient clustering and analysis of large-scale proteomics data.

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

Casanovo, a transformer encoder-decoder, translates unbinned tandem mass spectrum peaks into peptide sequences, beating DeepNovo and PointNovo on peptide-level precision across nine held-out species while running faster than both.

The paper uses a random forest classifier to predict which drugs are detectable on skin using metabolomics, finding that nervous system drugs are more likely to be detected.

The paper introduces ppx, a Python package for programmatic access to proteomics data repositories, demonstrating its utility in reproducible research workflows.

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 introduces falcon, a tool for efficient clustering of large-scale mass spectrometry data, which produces larger and purer clusters than existing tools.

The paper identifies challenges in predicting TCR-epitope interactions and introduces ImRex, a feature representation method that improves model performance for similar epitopes.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

A web service renders tandem mass spectra held in metabolomics repositories as publication-ready figures and consistent exports, addressed through Universal Spectrum Identifiers extended to metabolomics.

MESSAR recommends metabolite substructures from tandem mass spectra without requiring predefined metabolite candidates.

spectrum_utils provides a Python package for efficient mass spectrometry data processing and visualization, enabling rapid prototyping and publication-quality figure generation.

TCRex identifies epitope-specific T cell receptor sequences in repertoires using machine learning and statistical analysis, validated on vaccine and infectious disease datasets.

The paper introduces an optimized version of the ANN-SoLo tool for faster and more accurate open modification spectral library searching using GPUs and feature hashing.

This paper introduces a tool that uses expert knowledge in machine learning to analyze dynamic metabolomics data, showing it performs as well as statistical methods.

ANN-SoLo improves open modification spectral library searching by using approximate nearest neighbor indexing and a cascade search strategy, achieving faster speeds and more identifications than existing tools.