Biology subjects

Mesuere, B.

Publications and source records attributed to Mesuere, B..

12 recordsLinked to original sources

bioRxiv2026

Evaluation of Protein Reference Database Reduction and Its Impact on Peptide-Centric Metaproteomics

Metaproteomics identifies the proteins present in a microbial community and, from them, which organisms are active and what functions they carry out. Tools such as Unipept assign peptides to taxa and functions by matching them to UniProtKB proteins and taking the lowest common ancestor of the matching taxa, deriving functional annotations from the same matches. This analysis therefore depends directly on the content of UniProtKB, which was substantially restructured in 2025-2026, reducing it by more than 100 million protein entries. We investigated how these changes affect taxonomic interpretation and how restricting the database to sample-specific taxa identified by rRNA profiling modifies that effect. Fixed, non-redundant peptide lists from human-gut and marine-hatchery studies were reanalysed with Unipept against UniProtKB releases 2025_03, 2025_04, and 2026_02. Peptide mapping coverage fell from 85.9% to 73.3% (gut) and from 82.3% to 68.7% (marine) between release 2025_03 and 2026_02, yet dominant taxonomic profiles remained robust: family- and genus-level distributions changed little and all 15 dominant gut species were retained. Root-level assignments dropped from 18.7% to 7.0% (gut) and 25.8% to 14.3% (marine). Restricting UniProtKB 2026_02 to taxa detected by large-subunit ribosomal RNA (LSU rRNA) profiling reduced coverage to 64.4% (gut) and 41.8% (marine), while genus- and species-level proportions changed by less than one percentage point. The major UniProtKB restructuring of 2025-2026 therefore narrowed mapping breadth without destabilizing the dominant taxonomic profiles in these two datasets.

bioinformatics↗

bioRxiv2025

Direct construction of sparse suffix arrays with Libsais

Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets. While sparse suffix arrays offer significant memory savings compared to full suffix arrays, they typically still require the construction of a full suffix array prior to a sampling step, resulting in substantial memory overhead during the construction phase. We present an alternative method to directly construct the sparse suffix array using a simple, yet powerful text encoding, in combination with the widely used Libsais library. This approach bypasses the need for constructing a full suffix array, reducing memory usage by 63% and construction time by 55% when building a sparse suffix array with sparseness factor 3 for the entire UniProt knowledgebase. The method is particularly effective for applications with small alphabets, such as a nucleotide or amino acid alphabet. An open-source implementation of this method is available on GitHub, enabling easy adoption for large-scale bioinformatics applications.

bioinformatics↗

bioRxiv2025

Boosting metaproteomics identification rates and taxonomic specificity with MS2Rescore

BackgroundMetaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the identification sensitivity of current identification algorithms, which were primarily designed for single-species analyses. Addressing this, we evaluated the machine learning-driven MS{superscript 2}Rescore post-processing tool on multiple metaproteomics datasets from diverse microbial environments and benchmark studies. ResultsWe demonstrate that machine learning-driven rescoring outperforms traditional metaproteomics identification workflows. It significantly increases peptide identification rates compared to Sage, which itself already implements basic rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal to no sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater confidence in downstream taxonomic annotation. ConclusionsOur findings show that MS{superscript 2}Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved confidence in taxonomic annotation. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.

bioinformatics↗

bioRxiv2024

Unipept 6.0: Expanding metaproteomics analysis with support for missed cleavages, semi-tryptic and non-tryptic peptides

Unipept, a pioneering software tool in metaproteomics, has significantly advanced the analysis of complex ecosystems by facilitating both taxonomic and functional insights from environmental samples. From the onset, Unipepts capabilities focused on tryptic peptides, utilizing the predictability and consistency of trypsin digestion to efficiently construct a protein reference database. However, the evolving landscape of proteomics and emerging fields like immunopeptidomics necessitate a more versatile approach that extends beyond the analysis of tryptic peptides. In this article, we present a significant update to the underlying index structure of Unipept, which is now powered by a Sparse Suffix Array index. This advancement enables the analysis of semi-tryptic peptides, peptides with missed cleavages, and non-tryptic peptides such as those encountered in other research fields such as immunopeptidomics (e.g. MHC- and HLA-peptides). This new index benefits all tools in the Unipept ecosystem such as the web application, desktop tool, API and command line interface. A benchmark study highlights significantly improved performance in handling missed cleavages, preserving the same level of accuracy. For TOC Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/615136v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b8fe2org.highwire.dtl.DTLVardef@1435321org.highwire.dtl.DTLVardef@106a568org.highwire.dtl.DTLVardef@15563e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

PathwayPilot: A User-Friendly Tool for Visualizing and Navigating Metabolic Pathways

BackgroundMetaproteomics, the study of collective proteomes in environmental communities, plays a crucial role in understanding microbial functionalities affecting ecosystems and human health. Pathway analysis offers structured insights into the biochemical processes within these communities. However, no existing tool effectively combines pathway analysis with peptide- or protein-level data. ResultsThis manuscript introduces PathwayPilot, a user-friendly web application for exploring and visualizing metabolic pathways. PathwayPilot can compare functional annotations across different samples or organisms within a sample. A case study on the impact of caloric restriction on gut microbiota demonstrated the tools efficacy in deciphering complex metaproteomic data. The re-analysis revealed significant shifts in enzyme expressions related to short-chain fatty acid biosynthesis, aligning with existing research findings and showcasing PathwayPilots capability for accurate functional annotation and comparison across different microbial communities. ConclusionsPathwayPilot represents a significant advancement in metaproteomic data analysis, offering a user-friendly interface for exploring and visualizing metabolic pathways. This study not only validates the tools applicability in real-world scenarios but also highlights its potential for broader research implications in microbial ecology and health sciences.

bioinformatics↗

bioRxiv2023

Unipept Desktop 2.0: construction of targeted reference protein databases for proteogenomics analyses

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of proteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is now also possible to perform (meta)proteogenomics analyses. Since the protein reference database now lives on the users local machine, they have complete control over the database used during an analysis. Data does no longer need to be transmitted over the internet, decreasing the time required for an analysis and better safeguarding privacy sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

bioinformatics↗

bioRxiv2021

Pout2Prot: an efficient tool to create protein (sub)groups from Percolator output files

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can not only be present in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies, and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

bioinformatics↗

bioRxiv2021

FragGeneScanRs: better and faster gene prediction for short reads

FragGeneScanRs is a better and faster Rust implementation of the FragGeneScan gene prediction model for short and error-prone reads. Its command line interface is backward compatible and adds extra features for more flexible usage. Compared to the original C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. Availability and implementationThe Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license, with instructions for installation, usage and other documentation.

bioinformatics↗

bioRxiv2021

UMGAP: the Unipept MetaGenomics Analysis Pipeline

Shotgun metagenomics is now commonplace to gain insights into communities from diverse environments, but fast, memory-friendly, and accurate tools are needed for deep taxonomic analysis of the metagenome data. To meet this need we developed UMGAP, a highly versatile open source command line tool implemented in Rust for taxonomic profiling of shotgun metagenomes. It differs from state-of-the-art tools in its use of protein code regions identified in short reads for robust taxonomic identifications, a broad-spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, a non-monolithic design, and support for interactive visualizations of complex biodiversities.

bioinformatics↗

bioRxiv2021

Critical Assessment of Metaproteome Investigation (CAMPI): a Multi-Lab Comparison of Established Workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carried out the first community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluated the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observed that variability at the peptide level was predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappeared at the protein group level. While differences were observed for predicted community composition, similar functional profiles were obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

microbiology↗

bioRxiv2020

MegaGO: a fast yet powerful approach to assess functional similarity across meta-omics data sets

The study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license, and is available at https://github.com/MEGA-GO/.

bioinformatics↗

bioRxiv2020

Survey of metaproteomics software tools for functional microbiome analysis.

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional role of expressed microbial genes/proteins. This can be accomplished through metaproteomics, which characterizes the protein complement of the microbiome. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, so that researchers can make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental carie plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! Tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN6, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the Universal Protein Knowledgebase revealed that the sensitivity and specificity of functional annotation differed between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated the most precise GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

bioinformatics↗