Biology subjects

Dennler, O.

Publications and source records attributed to Dennler, O..

4 recordsLinked to original sources

bioRxiv2026

OrthoGather: a local platform for orthology-based proteome and proteomics comparisons and Gene Ontology enrichment

MotivationComparative proteomic analysis may reveal common and unique pathways regulated by the same stimulus across species using data from differential protein expression studies or curated protein sets. Functional annotations are key but vary in quality, as many proteins, particularly in prokaryotes and non-model eukaryotes, are poorly or inconsistently annotated, complicating comparative studies. Orthology inference provides a robust framework to address this, but existing tools require technical expertise, command-line use, and manual processing of complex outputs, creating barriers for researchers without computational training. ResultsWe developed OrthoGather, a locally hosted web application that streamlines comparative proteomic analysis by integrating homologous protein groups across species and Gene Ontology (GO) enrichment. It leverages functional annotations from any orthogroup member to enable functional inference even when individual species lack comprehensive annotation. Its flexible design supports cross-species exploration of conserved and unique orthogroups across proteomes or user-defined protein sets, revealing functional patterns through orthogroup relationships. OrthoGather generates publication-ready, easy-to-interpret outputs including downloadable graphs and data files, lowering barriers for researchers without computational expertise. Availability and implementationSource code, documentation and tutorials are available at Zenodo (https://doi.org/10.5281/zenodo.18603238) and GitHub (https://github.com/CarlosVivasR/OrthoGather). Supplementary materials, including the example dataset analysis are available online at Bioinformatics.

systems biology↗

bioRxiv2025

FUSE-PhyloTree: Linking functions and sequence conservation modules of a protein family through phylogenomic analysis

FUSE-PhyloTree is a phylogenomic analysis software for identifying local sequence conservation associated with the different functions of a multi-functional (e.g., paralogous or multi-domain) protein family. FUSE-PhyloTree introduces an original approach that combines advanced sequence analysis with phylogenetic methods. First, local sequence conservation modules within the family are identified using partial local multiple sequence alignment. Next, the evolution of the detected modules and known protein functions is inferred within the familys phylogenetic tree using three-level phylogenetic reconciliation and ancestral state reconstruction. As a result, FUSE-PhyloTree provides a gene tree annotated with both predicted sequence modules and ancestral gene functions, enabling the association of functions with specific sequence regions based on their co-emergence. Availability and ImplementationFUSE-PhyloTree is provided as Docker and Singularity images including all the required software tools. Images, source code, test data, and documentation are available at https://github.com/OcMalde/fuse-phylotree. Supplementary InformationAn illustration of the application of FUSE-PhyloTree to the fibulin protein family is presented in the Appendix.

bioinformatics↗

bioRxiv2024

Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions

Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over evolutionary time scales, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional similarity and can predict shared functions between paralogs as revealed by synthetic lethal experiments. However, the advent of alternative protein representations, including embeddings from protein language models (PLMs) and predicted structures from AlphaFold, raises the possibility that alternative similarity metrics could better capture functional similarity between paralogs. Here, using two species (budding yeast and human) and two different definitions of shared functionality (shared protein-protein interactions, synthetic lethality) we evaluated a variety of alternative similarity metrics. For some tasks, predicted structural similarity or PLM embedding similarity outperform sequence identity, but more importantly these similarity metrics are not redundant with sequence identity, i.e. combining them with sequence identity leads to improved predictions of shared functionality. By adding contextual features, representing similarity to homologous proteins within and across species, we can significantly enhance our predictions of shared paralog functionality. Overall, our results suggest that alternative similarity metrics capture complementary aspects of functional similarity beyond sequence identity alone. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/617835v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@21ed4org.highwire.dtl.DTLVardef@136184eorg.highwire.dtl.DTLVardef@75e0e5org.highwire.dtl.DTLVardef@1001932_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Paralog protein compensation preserves protein-protein interaction networks following gene loss in cancer

Proteins operate within dense interconnected networks, where interactions are necessary both for stabilising proteins and for enabling them to execute their molecular functions. Remarkably, protein-protein interaction networks operating within tumour cells continue to function despite widespread genetic perturbations. Previous work has demonstrated that tumour cells tolerate perturbations of paralogs better than perturbations of singleton genes, but the mechanisms behind this genetic robustness remains poorly understood. Here, we systematically profile the proteomic response of tumours and tumour cell lines to gene loss. We find many examples of active compensation, where deletion of one paralog results in increased abundance of another, and collateral loss, where deletion of one paralog results in reduced abundance of another. Compensation is enriched among sequence-similar paralog pairs that are central in the protein-protein interaction network and widely conserved across evolution. Compensation is also significantly more likely to be observed for gene pairs with a known synthetic lethal relationship. Our results support a model whereby loss of one gene results in increased protein abundance of its paralog, stabilising the protein-protein interaction network. Consequently, tumour cells may become dependent on the paralog for survival, creating potentially targetable vulnerabilities.

systems biology↗