Biology subjects

Dawyndt, P.

Publications and source records attributed to Dawyndt, P..

8 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↗

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↗

bioRxiv2022

Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping

Here we present SMAP, a software package that implements a suite of computational tools to extract multi-allelic haplotypes using read-backed haplotyping. SMAP tools first perform accurate read processing and analyze read mapping distributions across sample sets. Then, two complementary modules can be invoked for haplotype calling: SMAP haplotype-sites combines known Single Nucleotide Polymorphisms (SNPs) and/or read mapping position polymorphisms (SMAPs) to reconstruct compressed, read-reference-encoded haplotype strings. In contrast, SMAP haplotype-window works independent of prior knowledge of polymorphisms, groups reads by locus, defines a window enclosed between two custom border sequences, and retains the entire corresponding DNA sequence as haplotype. Haplotype-window is, among many applications, especially useful for high-throughput CRISPR/Cas mutation screens. Either way, SMAP creates a single integrated haplotype call table across all loci and samples. SMAP haplotyping is extremely versatile and can be applied to highly multiplex amplicon sequencing (HiPlex), Shotgun (e.g. whole genome shotgun (WGS) sequencing, probe capture and RNA-Seq), or Genotyping-by-Sequencing (GBS) data; and to Illumina short reads, PacBio and MinION long reads. SMAP creates discrete genotype calls for individuals of any ploidy or quantitative haplotype frequency spectra for Pool-Seq data, and can scale from tens to thousands of loci and/or samples. SMAP, including the source code written in Python is available at https://gitlab.com/truttink/smap, and a detailed user manual and guidelines for accurate read processing is available at https://ngs-smap.readthedocs.io/, under the GNU Affero General Public License v3.0.

genomics↗

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↗