Before I forget posting this year’s lab EuroBioc contribution, here are the abstract of the posters and talks we will present at the EuroBioc2025 in Barcelona

PSMatch: a Bioconductor Package for Handling Peptide-Spectrum Matches Data

Guillaume Deflandre, Sebastian Gibb and Laurent Gatto

Loading, exploring and analysing the resulting Peptide-Spectrum Matches (PSMs) from a database search in Mass Spectrometry (MS)-based proteomics can be time-consuming. PSMatch is an R/Bioconductor package designed to handle this process by offering functionalities to streamline exploration and visualisation of PSM data. It provides functions to load PSM data from mzId or tabular files, generate theoretical fragment ions, model peptide-protein relations and facilitate visualisations.

Recent developments in PSMatch have focused on extending these functionalities to support post-translational modifications, enabling more accurate characterisation of modified peptides. Effort in identifying modified peptides is needed as it is these peptides that are expected to constitute a significant proportion of unidentified spectra. In fields such as single-cell proteomics or metaproteomics, where the identification rates pale by comparison with bulk approaches, this becomes even more prominent. Enabling users to benefit from a powerful and flexible R ecosystem to further explore these unidentified spectra is therefore paramount.

PSMatch is part of the R for Mass Spectrometry initiative, that develops an open and collaborative ecosystem of MS-based proteomics and metabolomics, offering efficient, scalable, and stable infrastructure for MS-based proteomics.

And here’s the relevant preprint.

An Open Software Development-based Ecosystem of R Packages for Mass Spectrometry Data Analysis

Laurent Gatto, Sebastian Gibb and Johannes Rainer

A frequent problem with scientific research software is the lack of support, maintenance and further development. In particular, development by a single researcher can easily result in orphaned and dysfunctional software packages, especially if combined with poor documentation, missing unit tests or lack of adherence to open software development standards. The RforMassSpectrometry (https://www.rformassspectrometry.org/) initiative aims to develop an efficient, scalable, and stable infrastructure for mass spectrometry (MS) based proteomics and metabolomics data analysis. As part of this initiative, a growing ecosystem of R software packages is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems, community contributions are fostered, and open development, documentation and long-term support emphasised.

At the heart of the package ecosystem lies the Spectra package that provides the core infrastructure to handle, process and visualise MS data. Its design allows easy expansion to support existing and new file or data formats, including data representations with minimal memory footprint or remote data access. For proteomics data analysis, two packages in particular are dedicated to the analysis or quantitative and identification data. The PSMatch package handles and manages peptide identification data. It also provides functions to model and visualise peptide-protein relations to make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions, in conjunction with the Spectra package. The QFeatures package is the working horse for quantitative proteomics data. It builds on the familiar SummarizedExperiment and MultiAssayExperiment infrastructure and provides a familiar Bioconductor user experience to manage bulk and single-cell quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable way.

For metabolomics data analysis, xcms is one of the core software packages for the required preprocessing of LC-MS data. This Bioconductor package was recently updated to reuse the R for Mass Spectrometry infrastructure, enabling now also the analysis of very large, and/or remote, data. This integration simplifies in addition complete analysis workflows which can include functionality from the MsFeatures package for compounding, and from the MetaboAnnotation package facilitating annotation of untargeted metabolomics experiments. Public annotation resources can be easily accessed through packages such as MsBackendMassbank, MsBackendMsp or CompoundDb, the latter also allowing to create and manage lab-specific compound databases. These packages rely on the MsCoreUtils and MetaboCoreUtils packages for efficient implementations of commonly used algorithms, designed to be re-used by other R packages.

In contrast to a monolithic software design, the R for Mass Spectrometry ecosystem enables to build customised, modular, and reproducible analysis workflows. Future proteomics- and metabolomics-related development will focus on improved data structures and analysis methods, better support for third-party data import, and better interoperability with other open source software.

Benchmark of Module Detection Methods for Single Cell Proteomics

Enes Sefa Ayar and Laurent Gatto

Proteins are the key molecules in executing biological functions, and they cooperate as part of protein complexes or biological pathways. Correlation in their abundance suggests functional interdependence, offering insights into biological functions. Thus, identifying biologically meaningful protein groups (modules) is a critical step in understanding cellular processes. While many module detection methods exist, they were developed for bulk or transcriptomic data and rely on the assumption that gene expression levels can identify functionally related protein groups.

Single-cell proteomics (SCP) quantifies protein levels at single-cell resolution, eliminating this assumption and offering a more accurate view of functional protein relationships. Moreover, SCP preserves cellular heterogeneity, enabling the discovery of dynamic and context-specific protein modules are often masked in bulk measurements. Despite these advantages, existing module detection methods may not be well suited for SCP data, which presents unique challenges such as batch effects and missing values.

Moreover, these methods also differ by various features, for instance, whether they incorporate (or not) prior biological knowledge, whether they allow (or not) overlapping modules, or to what extent they use differential correlation analysis. Parameter choices further influence the identified modules, often leading to arbitrary decisions. However, all share a more critical limitation: they generate modules even when applied to random data. It is therefore essential to distinguish biologically relevant modules from artifacts.

In this work, we systematically evaluate module detection methods on SCP datasets. Our assessment framework integrates (1) internal clustering metrics to evaluate compactness and separation, (2) external validation against known biological annotations, and (3) network-based analyses incorporating protein-protein interaction data to enhance biological interpretability. Our findings reveal notable differences in the biological relevance of the identified modules and offer practical recommendations for selecting and validating module detection approaches for single-cell proteomics. Furthermore, we propose strategies for addressing missing values and batch effects, thereby improving the accuracy and reliability of module detection.

e-OMIX, a new visual interface for analyzing and managing omics data

Molka Anaghim Ftouhi, Loïc Guille, Jérôme Linden, Sébastien Jodogne and Laurent Gatto

The e-OMIX project (https://www.eomix.be) aims to lower the barrier of entry into omics research by providing an interactive platform where users will be able to perform analyses, as well as storing the resulting data and metadata, without the need for advanced coding skills. e-OMIX is developed under AGPL 3 license as an Angular/Java-based web-app, making use of several innovative technologies. Pre-built pipelines are implemented from nf-core, a repository of publicly available workflows, maximizing their reproducibility and ease of use. Result matrices are stored in a database optimized for fast querying of large datasets (TileDB) and can be exported in several objects notably ‘Bioconductor’s SingleCell Expriment (SCE) or other (anndata, or Seurat), while metadata are stored as per-sample individual documents in a document-oriented database (CouchDB). To increase the interoperability of metadata, e-OMIX also offers the possibility to manage and export them using Fast Healthcare Interoperability Resources (FHIR), a widely used standard in healthcare and clinical research. Finally, data visualization is made possible by using the iSEE R/Biocondutor package. As a first use case, we demonstrate the end-to-end execution of single-cell RNA-seq pipeline, starting from metadata and raw files upload, and leading to actionable data, such as annotated cell types, individual gene expression or marker gene identification.

Tools and Strategies for Systematic Benchmarking of R Packages: A Case Study with QFeatures

Léopold Guyot and Laurent Gatto

As bioinformatics continues to evolve, it must keep pace with experimental techniques that generate increasingly large volumes of data. In this context, optimizing the performance of code and packages that handle these data becomes essential. This work presents the optimization efforts carried out on the QFeatures R/Bioconductor package, which is used for the analysis of quantitative proteomics data. We also highlight a set of tools and methods that are valuable for performance optimization, with a particular focus on VerR, an R package designed to create isolated and reproducible environments. These environments allow for the installation of specific package versions, enabling systematic benchmarking to assess the performance impact of different versions. As a result of these optimization efforts, we observed a 90% reduction in the runtime of a classical single-cell proteomics (scp) workflow and a 50% decrease in memory usage, demonstrating the significant impact of targeted optimizations.