Biology subjects

Bittremieux, W.

Publications and source records attributed to Bittremieux, W..

At least 19 recordsLinked to original sources

bioRxiv2026

mzQC: a versatile way to communicate quality information for biological mass spectrometry

Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.

bioinformatics↗

bioRxiv2026

CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo

De novo peptide sequencing detects peptides directly from tandem mass spectra without a protein sequence database, and deep learning has substantially advanced its performance. Casanovo, one such widely used model, is distributed as a Python command-line program. Consequently, installation, GPU and dependency configuration, and manual parameterization can be challenging for many bench scientists and are a recurring source of errors. Interpreting and validating the resulting predictions poses a further challenge. We present CasanovoGUI, an open-source Java-based desktop application that makes all of Casanovo's main analysis functions available through a point-and-click interface on Windows, macOS, and Linux. On first use, CasanovoGUI automatically installs a private Python environment and Casanovo with a GPU-matched build, requiring no prior software setup. The GUI provides access to Casanovo's analysis functions and configuration parameters, streams live progress, and integrates results interpretation: annotated spectra with per-residue confidence scores in the PDV viewer, and mismatch-tolerant mapping of de novo peptides back to a reference proteome. CasanovoGUI is available at https://github.com/Noble-Lab/CasanovoGUI.

bioinformatics↗

bioRxiv2026

Learning interpretable structural similarity from tandem mass spectra for small molecule analog discovery

Analog discovery remains a central bottleneck in mass spectrometry-based untargeted metabolomics, as conventional spectral similarity scores poorly reflect molecular structure. We introduce SIMBA, a transformer-based model that infers two interpretable graph-based distances, maximum common edge subgraph and substructure edit distance, directly from tandem mass spectra. SIMBA consistently retrieves structurally closer analogs than existing methods, enabling structure-aware small molecule identification beyond exact spectral matching.

bioinformatics↗

bioRxiv2026

Rapid Identification of Infections Directly from Isolates and Clinical Specimens with the MasSpec Pen Technology

The time-consuming nature of conventional bacterial identification methods, coupled with rising resistance to antibiotics, presents a serious challenge in the rapid diagnosis and treatment of patients suffering from severe infection and sepsis. In this study, we demonstrate that the MasSpec Pen (MSPen) technology enables rapid identification of the most frequently occurring bacterial species in human infections. A total of 755 clinical bacterial isolates prospectively collected from pediatric and adult patients were analyzed using the MSPen to create a library of molecular profiles. The mass spectra obtained presented distinct molecular patterns for bacterial species, with high relative abundances of bacterial metabolites and lipids, including unique quinolone signaling molecules in Pseudomonas aeruginosa, phosphoethanolamine (PE) lipids in Enterobacteriaceae, and phosphatidylglycerol (PG) lipids in Staphylococcus species. Logistic and multinomial lasso models using relative abundances or logarithmic-ratio abundance calculations achieved >95% accuracy for Gram stain typing and >92% for species-level identification in isolates. Random forest models yielded comparable performance, reinforcing the robustness of these findings across approaches. Many of the metabolite and lipid predictive features from each isolate classifier were detected when directly analyzing highly infected synovial fluid, tissue, pleural fluid, and breast aspirate specimens from patients. Notably, significant variations in the natural isotopic abundance of specific lipids were observed in infected patient samples, indicating presence of bacterial infection. This study demonstrates the potential of MSPen for the direct identification of bacterial-specific metabolites and lipids in clinical isolates and specimens, offering promise for future advancements in the MSPen technology for rapid bacterial identification. Teaser.MSPen shows potential for rapid, direct identification of bacterial metabolites and lipids, enabling accurate bacterial classification.

microbiology↗

bioRxiv2026

Fermentation-Induced Molecular Remodeling in African Indigenous Tubers: Cassava and Cocoyam

Cassava and cocoyam are major dietary staples in sub-Saharan Africa, commonly processed by natural fermentation before consumption. Although fermentation reduces antinutritional compounds and improves food quality, its molecular effects remain poorly characterized. We used untargeted mass spectrometry-based metabolomics with a computational annotation pipeline to compare fermentation-induced molecular remodeling in the two tubers, which showed distinct responses. In cassava, 718 of 773 significant features (92.9%) were depleted, indicating a predominantly catabolic process. In cocoyam, the response was more balanced, with 385 of 1,013 features (38.0%) enriched, including di- and tripeptides consistent with proteolytic processing. Class analysis, molecular networking, and pathway enrichment revealed tuber-specific signatures: cassava was dominated by purine metabolism, whereas cocoyam showed stronger enrichment of amino acid pathways. Cyanogenic glycoside-related features were depleted, consistent with detoxification. Biotransformation prediction also suggested putative fermentation products absent from current databases, highlighting the under-characterized chemistry of these tubers.

biochemistry↗

bioRxiv2026

Scalable mass-spectrometry-based molecular phylogeny with TreeMS2

Molecular phylogeny is a well-established method for inferring evolutionary relationships from DNA and RNA sequences. Here, we extend this concept beyond genetic information by applying phylogeny-like analysis to proteomic and metabolomic mass spectrometry data, capturing relationships based on the realized molecular phenotype. The resulting phenotype-derived trees can be directly compared with conventional genetic-based trees to identify where molecular phenotypes reflect evolutionary history and where they diverge due to functional adaptation, regulation, or environmental influence. To enable this analysis, we introduce TreeMS2, a computational tool that constructs similarity matrices by directly comparing tandem mass spectrometry (MS/MS) spectra between samples. By bypassing spectrum annotation, TreeMS2 enables rapid, unbiased comparisons. Across diverse datasets, TreeMS2 reconstructs biologically meaningful relationships. In proteomics, phenotype-derived trees recapitulate established taxonomy, with deviations pinpointing sample handling errors. In single-cell proteomics our method distinguishes cell types despite sparse and noisy measurements and in metabolomics it resolves major biochemical divisions and fine-scale compositional structure. Together, these results establish TreeMS2 as a scalable, annotation-independent framework for deriving molecular relationships from raw MS/MS data.

bioinformatics↗

bioRxiv2026

Positive-Unlabeled Learning for Predicting Small Molecule MS2 Identifiability from MS1 Context and Acquisition Parameters

MotivationThe quality of tandem mass spectra critically determines metabolite identifiability in untargeted metabolomics, yet optimizing MS2 acquisition parameters experimentally is costly, time-consuming, and infeasible across the full diversity of samples and instruments. A key obstacle to computational quality assessment is the absence of reliable negative labels: most MS2 spectra remain unannotated not because they are low quality, but because the corresponding compounds are absent from reference libraries. This label ambiguity fundamentally limits supervised learning approaches and complicates acquisition-time decision making. ResultsWe present a deep learning framework that predicts the probability that an MS2 scan will be identifiable using only the preceding MS1 spectrum and instrument acquisition parameters, without inspecting the MS2 spectrum itself. The problem is formulated as positive-unlabeled learning and addressed using a non-negative positive-unlabeled objective, enabling robust training despite missing negative labels. Trained on over eight million MS2 scans from public Orbitrap metabolomics datasets and evaluated on laboratory-disjoint benchmarks, the model recovered 90% of known identifiable spectra in a held-out test set. Predicted probabilities generalized to unseen laboratories and stratified unlabeled spectra in a physicochemically meaningful manner. Independent validation demonstrated that high predicted quality is associated with increased structural explainability, richer fragmentation patterns, canonical precursor charge states, and reduced spectral interference. These results indicate that MS2 identifiability can be anticipated from precursor context and acquisition settings alone. Availability and implementationCode is available at https://github.com/bittremieuxlab/pu_ms2_identifiability. Model weights and processed datasets are available at 10.5281/zenodo.18266932.

bioinformatics↗

bioRxiv2025

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they acquire differ substantially, thus requiring tailored analysis algorithms. Hence, many dedicated bioinformatics workflows are developed. These are in constant evolution, and the community lacks a centralized platform for comparing their performance. Here, we propose ProteoBench, a single platform that brings together software developers and software users to provide an ever-evolving comparison of state-of-the-art proteomics data processing tools. ProteoBench is an open-source resource that enables the community to evaluate data analysis workflows, develop benchmarking modules dedicated to specific comparisons, and discuss the best methods to compare software tools. The platform ensures that the benchmark evolves alongside advances in proteomics data analysis workflows. ProteoBench guides researchers towards the best-suited tool and parameters for their specific project and data according to their needs, and developers can test their newly developed tools or workflows privately, before adding them as public references. This community-driven effort will increase transparency and reproducibility between MS data analysis workflows, as well as facilitate the development and publication of software workflows in the field.

bioinformatics↗

bioRxiv2025

An open-source platform for reference data-driven analysis of untargeted metabolomics

Untargeted tandem mass spectrometry (MS/MS)-based metabolomics enables broad characterization of small molecules in complex samples, yet the majority of spectra in a typical experiment remain unannotated, limiting biological interpretation. Reference data-driven (RDD) metabolomics addresses this gap by contextualizing spectra through comparison to curated, metadata-annotated reference datasets, allowing inference of spectrum origins without requiring exact structural identification. Here, we present an open-source RDD metabolomics platform comprising a user-friendly web application and a Python software package that perform RDD analyses directly from molecular networking outputs generated by GNPS. The tools support visualization and statistical analysis of RDD results, including interactive bar plots, heat maps, principal component analysis, and Sankey diagrams. We illustrate the approach using a hierarchical reference dataset of 3,500 food items to derive dietary patterns from stool metabolomics data of omnivore and vegan participants. The analysis reveals clear dietary group separation, demonstrating how RDD metabolomics can extract biologically meaningful patterns from otherwise unannotated spectra. Thus, the RDD metabolomics platform removes technical barriers for the metabolomics community to adopting reference data-driven analysis, with the functionality freely available at https://github.com/bittremieuxlab/gnps-rdd and https://gnps-rdd.streamlit.app/.

bioinformatics↗

bioRxiv2025

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

bioinformatics↗

bioRxiv2025

Tracing the origins of molecular signals in food through integrative metabolomics and chemical databases

Foods contain thousands of chemical constituents beyond macronutrients, including bioactive metabolites, processing by-products, and contaminants that remain poorly characterized. The Periodic Table of Food Initiative (PTFI) is establishing a standardized global reference for food composition using untargeted mass spectrometry. We analyzed the first PTFI release ([~]24,000 molecular features across 500 foods) by linking annotated and unannotated signals to curated databases of pharmaceuticals, agrochemicals, food contact chemicals, and natural products. Annotated compounds revealed characteristic chemical patterns across food groups, while unannotated features exposed xenobiotic signatures and potential contamination pathways. A taxonomy-aware search identified unexpected natural products, such as biochanin A and phlorizin produced by Canada thistle. Together, these analyses show how agricultural practices, environmental exposures, and processing shape food chemistry and highlight the value of food metabolomics for advancing a One Health understanding of the molecular connections between the environment, food systems, and human health.

bioinformatics↗

bioRxiv2025

Improvements to Casanovo, a deep learning de novo peptidesequencer

Casanovo is a state-of-the-art deep learning model for de novo peptide sequencing from mass spectrometry proteomics data. Here we report on a series of enhancements to Casanovo, aimed at improving the interpretability of the scores assigned to predicted peptides, generalizing the software for use in database search, speeding up training and prediction runtimes, and providing workflows and visualization tools to facilitate adoption of Casanovo and interpretation of its results. Our goal is to make Casanovo accurate and easy to use for applications such as metaproteomics, antibody sequencing, immunopeptidomics, and discovery of novel peptide sequences in standard proteomics analyses. Casanovo is available as open source at https://github.com/Noble-Lab/casanovo.

molecular biology↗

bioRxiv2025

Temporal regulation of metabolic processes in the marine diazotroph Crocosphaera watsonii WH 8501

Marine diazotrophic cyanobacteria play a crucial role in oceanic nitrogen cycling, supporting primary production and ecosystem balance. Crocosphaera watsonii WH8501 exemplifies this ability by temporally separating photosynthesis and diazotrophy to sustain metabolism. To investigate the regulatory mechanisms underlying this process, we employed LC/MS-MS proteomics in a diel culturing experiment, revealing tightly coordinated protein abundance patterns. Our findings showed a sophisticated temporal regulation of metabolic processes categorized within six distinct protein abundance clusters: (1) nitrogen fixation and amino acid biosynthesis proteins peaked during the night, while (2) glycogen metabolism and dark reactions of photosynthesis were most abundant during the night and day-night transition, likely supporting carbon consumption and energy production. Midday (3 and 4) was dominated by proteins related to photosynthesis, cellular division, and lipid synthesis, whereas late-day peaks (5) in peptide biosynthesis may facilitate nitrogenase complex formation. Notably, the day-night transition (6) exhibited fine-tuned coordination of nitrogenase assembly, with FeS cluster proteins preceding peak nitrogenase iron protein abundance, implying a temporally ordered sequence for functional enzyme formation. Within these categories, sharp temporal patterns emerged in iron trafficking to heme and iron cluster biosynthetic systems, consistent with the need to maintain tight control of iron distribution to metalloproteins at each temporal transition. These results highlight the intricate diel regulation that enables Crocosphaera to balance nitrogen fixation and photosynthesis within a single cell. The observed coordination supports the existence of a complex regulatory system ensuring optimal metabolic performance, reinforcing the critical role of temporal control in sustaining these globally significant biological processes.

microbiology↗

bioRxiv2025

Improved open modification searching via unified spectral search with predicted libraries and enhanced vector representations in ANN-SoLo

The primary computational challenge in mass spectrometry-based proteomics is determining the peptide sequence responsible for generating each measured tandem mass spectrum. This task is traditionally addressed through sequence database searching, as well as alternative approaches such as spectral library searching. ANN-SoLo is a powerful spectral library search engine optimized for open modification searching, enabling the detection of peptides carrying any post-translational modification. Here, we present an enhanced version of ANN-SoLo that combines strengths of both spectral library searching and sequence database searching, by integrating with Prosit to generate predicted spectral libraries from protein sequence databases. Additionally, it provides functionality to generate decoys at both the spectrum and the peptide level, introduces an optimized internal file structure for large-scale analytics, and improves search accuracy by incorporating neutral loss information into spectrum vector representations. These advancements collectively address challenges associated with missing spectral libraries and enhance peptide identification in large-scale and complex proteomics workflows.

bioinformatics↗

bioRxiv2025

SquiDBase: a community resource of raw nanopore data from microbes

Experimental data-driven research relies on raw data, which consist of unprocessed experimental outputs, whereas derived data are transformed through a number of processing steps to reveal specific insights. Such processing, however, can potentially introduce biases or information loss, compromising transparency and reproducibility. In nucleic acid sequencing, nucleotide sequences stored in the FASTQ format are widely shared, but FASTQ files are generated from platform-specific raw data outputs, which vary depending on the sequencing platform used. The raw data produced by Oxford Nanopore Technologies (ONT) sequencing devices contain valuable biological information and are also useful to improve data processing methods, which includes basecaller optimisation and modification detection. Increasing attention goes to exploring these raw signals to develop algorithms that could improve ONT device portability and enhance target enrichment efficiency through adaptive sampling. Despite these benefits, the storage and sharing of raw nanopore data remain limited due to technical constraints and the lack of appropriate, standardised and centralised infrastructure. To address this challenge, we developed SquiDBase (https://squidbase.org), a dedicated repository to collect raw microbial nanopore sequencing data. To maximise the utility of SquiDBase from its inception, we built SquiDPipe, a Nextflow pipeline for the automated removal of human or unwanted reads from raw nanopore data. Additionally, we sequenced 24 clinically relevant viruses and incorporated them into SquiDBase, significantly expanding the diversity of publicly available reference datasets. By offering a centralised, open-access raw data collection platform, SquiDBase facilitates data sharing, enhances reproducibility, and supports the development and benchmarking of novel computational tools, reinforcing open science in nanopore sequencing research.

bioinformatics↗

bioRxiv2024

Zero-shot retention time prediction for unseen post-translational modifications with molecular structure encodings

Mass spectrometry-based proteomics relies on accurate peptide property prediction models to enhance peptide identification and characterization, especially when dealing with peptidoforms. However, current approaches are limited in their ability to generalize to peptides with novel post-translational modifications (PTMs) due to insufficient training data. To address this challenge, we introduce MoSTERT (Molecular Structure Transformer Encoder for Retention Time prediction) and its enhanced variant, MoSTERT-2S, two transformer-based models designed for zero-shot prediction of retention times of peptides with unseen PTMs. Unlike conventional models, MoSTERT encodes peptide residues at the molecular structure level, allowing it to handle diverse PTMs. MoSTERT-2S further improves accuracy by employing a two-step strategy: first predicting the retention time of the unmodified peptide, then estimating the retention time shift induced by the PTMs. Evaluation on an external dataset demonstrates that MoSTERT-2S achieves state-of-the-art performance, reducing prediction errors compared to existing methods. Its ability to accurately predict retention times for peptides with a wide variety of PTMs not seen during training highlights its potential for advancing proteomic workflows analyzing proteoforms and protein modifications.

bioinformatics↗

bioRxiv2024

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

bioinformatics↗

bioRxiv2024

Semi-Supervised Learning to Boost Cardiotoxicity Prediction by Mining a Large Unlabeled Small Molecule Dataset

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design process. Although conventional machine learning techniques have shown some success in this field, the scarcity of annotated toxicity data poses a significant challenge in enhancing models performance. In this study, we explore the potential of leveraging large unlabeled datasets using semi-supervised learning to improve predictive performance for cardiotoxicity across three targets: the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5). We extensively mined the ChEMBL database, comprising approximately 2 million small molecules, then employed semi-supervised learning to construct robust classification models for this purpose. We achieved a performance boost on highly diverse (i.e. structurally dissimilar) test datasets across all three targets. Using our built models, we screened the whole ChEMBL database and a large set of FDA-approved drugs, identifying several compounds with potential cardiac channel activity. To ensure broad accessibility and usability for both technical and non-technical users, we developed a cross-platform graphical user interface that allows users to make predictions and gain insights into the cardiotoxicity of drugs and other small molecules. The software is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred2.

pharmacology and toxicology↗