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Dessimoz, C.

Publications and source records attributed to Dessimoz, C..

At least 19 recordsLinked to original sources
bioRxiv2026

On the state of protein function prediction: a report on the fourth CAFA challenge

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). ResultsCAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

bioinformatics↗
bioRxiv2026

AmpliPhy improves gene trees by adding homologs without affecting alignments

In phylogenomics, gene tree reconstruction depends on multiple sequence alignment (MSA) and tree inference, and ongoing work continues to improve inference quality. Denser taxon sampling has been associated with improved gene tree inference, suggesting that adding homologs could be a practical route to higher accuracy as sequence databases continue to expand. However, adding sequences can influence multiple steps of typical inference pipelines, and little is known on its specific effect on the multiple sequence alignment, tree reconstruction, and rooting steps. We performed a large-scale empirical and simulated benchmarks to quantify how homolog enrichment affects alignment and phylogenetic inference. Using an enrichment-impoverishment design and a measure of tree accuracy based on taxonomic congruence, we found that enrichment consistently improves tree inference quality, while effects on alignment quality are marginal. We show that this improvement is associated with, but not restricted to accurate root placement on enriched trees when sensitive homolog search is accompanied. Notably, much of the benefit can be retained with relatively compact alignments produced by sequence addition. Building on these observations, we provide a tool, AmpliPhy, which efficiently improves phylogenetic reconstruction of protein families through homolog enrichment. The AmpliPhy open-source pipeline software is available at https://github.com/DessimozLab/ampliphy.

evolutionary biology↗
bioRxiv2025

PhytClust: efficient and optimal node clustering in phylogenetic trees

Phylogenetic trees play a fundamental role in elucidating evolutionary relationships among taxa. Clustering taxa remains a major challenge across diverse biological domains such as cancer genomics, microbial systematics, and phylogenomics. Several methods partition taxa in phylogenetic trees into clusters, but these approaches face key limitations. Many rely on user-defined distance thresholds, whose choice often depends on prior knowledge and may be difficult to justify consistently across datasets. More broadly, existing methods often differ in how clusters are defined and commonly rely on heuristics or user-specified criteria to make the search space tractable for large trees. Here, we present PhytClust, a threshold-free algorithm that partitions trees into monophyletic subtrees (clusters) by identifying groups of taxa with low within-cluster dispersion. For a fixed number of clusters, PhytClust finds the exact global optimum under this objective and then selects the optimal number of clusters using a cluster-validity index. The resulting partitions are reproducible and reflect both tree topology and branch lengths. In simulated datasets, PhytClust outperforms existing methods in both speed and accuracy and scales to trees with more than a hundred thousand taxa. We apply PhytClust across cancer genomics, avian phylogenomics, bacterial and archaea phylogenetics, and plant genomics to demonstrate PhytClusts varied applicability. By providing a standardized method for taxa clustering within phylogenetic trees, PhytClust yields reproducible, optimal and computationally efficient clusters.

evolutionary biology↗
bioRxiv2025

CDK4 Restricts Triple-Negative Breast Cancer Cell Migration via Phosphorylation-Driven Activation of Myo9b RhoGAP Function

Cyclin-Dependent Kinase 4 (CDK4) is a key regulator of cell cycle progression, driving the G0/G1-to-S phase transition through phosphorylation of Retinoblastoma 1 (RB1). Clinically, CDK4/6 inhibitors are under investigation in Triple Negative Breast Cancer (TNBC), a subtype characterized by invasiveness, aggressiveness and poor prognosis. While CDK4 is primarily targeted for its role in proliferation, emerging evidence suggests it may also regulate other cellular processes. In particular, the mechanisms by which CDK4 could influence cancer cell migration, remain largely unexplored, particularly in highly heterogenous cell line like MDA-MB-231. This study investigates whether CDK4 contributes to the regulation of TNBC cells migration and identifies the pathways involved in MDA-MB-231 cells, independently of its role in proliferation. We demonstrate that loss or inhibition of CDK4, using respectively CRISPR/Cas9 mediated CDK4 knockout and pharmacological CDK4/6 inhibitor, leads to enhanced migration capacities and reorganization of actin subcellular networks. Mechanistically, the absence of CDK4 results in decreased phosphorylation of Myo9b at serine 1935 (S1935), which enhances RhoA signaling, a key driver of cytoskeletal dynamics, leading to polarity defects and increased cell migration. These findings reveal a non-canonical function of CDK4 in limiting TNBC cell migration through the CDK4/CyclinD-Myo9b-RhoA signaling axis. This work highlights the broader cellular roles of CDK4 beyond its established function in proliferation and suggest that inhibition of Myo9b-RhoA pathway could reduce metastatic behaviour in TNBC treated with CDK4/6i, thereby informing future co-therapeutic strategies against aggressive cancer subtypes.

cell biology↗
bioRxiv2024

OMAnnotator: a novel approach to building an annotated consensus genome sequence

MotivationAdvances in sequencing technologies have enabled researchers to sequence whole genomes rapidly and cheaply. However, despite improvements in genome assembly, genome annotation (i.e. the identification of protein-coding genes) remains challenging, particularly for eukaryotic genomes: it requires combining several approaches (typically ab initio, transcriptomics, and homology search), each with its own pros and cons. Deciding which gene models to retain in a consensus is far from trivial, and automated approaches tend to lag behind laborious manual curation efforts in accuracy. ResultsHere, we present OMAnnotator, a novel approach to building a consensus annotation. OMAnnotator repurposes the OMA algorithm, originally designed to elucidate evolutionary relationships among genes across species, to integrate predictions from different annotation sources, using evolutionary information as a tie-breaker. We validated OMAnnotator by reannotating the Drosophila melanogaster reference genome and comparing it with the expert-curated reference and results from the automated pipelines BRAKER2 and EvidenceModeller. OMAnnotator produced a consensus annotation that outperformed each individual input and surpassed the existing pipelines. Finally, when applied to three recently published genomes, OMAnnotator gave substantial improvements in two cases, and mixed results in the third, which had already benefited from extensive expert curation. ConclusionWe introduce an original, flexible, and effective approach to annotating genomes by integrating multiple lines of evidence. The methods robustness is underlined by its successful implementation in re-annotating recently published genomes, opening up new avenues in eukaryotic genome annotation.

bioinformatics↗
bioRxiv2024

Unveiling the Functional Fate of Duplicated Genes Through Expression Profiling and Structural Analysis

Gene duplication is a major evolutionary source of functional innovation. Following duplication events, gene copies (paralogues) may undergo various fates, including retention with functional modifications (such as sub-functionalisation or neo-functionalisation) or loss. When paralogues are retained, this results in complex orthology relationships, including one-to-many or many-to-many. In such cases, determining which one-to-one pair is more likely to have conserved functions can be challenging. It has been proposed that, following gene duplication, the copy that diverges more slowly in sequence is more likely to maintain the ancestral function --referred to here as "the least diverged orthologue (LDO) conjecture". This study explores this conjecture, using a novel method to identify asymmetric evolution of paralogues and apply it to all gene families across the Tree of Life in the PANTHER database. Structural data for over 1 million proteins and expression data for 16 animals and 20 plants were then used to investigate functional divergence following duplication. This analysis, the most comprehensive to date, revealed that whilst the majority of paralogues display similar rates of sequence evolution, significant differences in branch lengths following gene duplication can be correlated with functional divergence. Overall, the results support the least diverged orthologue conjecture, suggesting that the least diverged orthologue (LDO) tends to retain the ancestral function, whilst the most diverged orthologue (MDO) may acquire a new, potentially specialised, role.

evolutionary biology↗
bioRxiv2024

EdgeHOG: fine-grained ancestral gene order inference at tree-of-life scale

Ancestral genomes are essential for studying the diversification of life from the last universal common ancestor to modern organisms. Methods have been proposed to infer ancestral gene order, but they lack scalability, limiting the depth to which gene neighborhood evolution can be traced back. We introduce edgeHOG, a tool designed for accurate ancestral gene order inference with linear time complexity. Validated on various benchmarks, edgeHOG was applied to the entire OMA orthology database, encompassing 2,845 extant genomes across all domains of life. This represents the first tree-of-life scale inference, resulting in 1,133 ancestral genomes. In particular, we reconstructed ancestral contigs for the last common ancestor of eukaryotes, dating back around 1.8 billion years, and observed significant functional association among neighboring genes. The method also dates gene adjacencies, revealing conserved histone clusters and rapid sex chromosome rearrangements, enabling computational inference of these features.

evolutionary biology↗
bioRxiv2024

Orthology inference at scale with FastOMA

The surge in genome data, with ongoing efforts aiming to sequence 1.5M eukaryotes in a decade, could revolutionise genomics, revealing the origins, evolution, and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets. Addressing this, "FastOMA" provides linear scalability, enabling the processing of thousands of eukaryotic genomes within a day. FastOMA maintains the high accuracy and resolution of the well-established OMA approach in benchmarks. FastOMA is available at https://github.com/DessimozLab/FastOMA/.

bioinformatics↗
bioRxiv2023

Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses

Recent advances in AI-based protein structure modeling have yielded remarkable progress in predicting protein structures. Since structures are constrained by their biological function, their geometry tends to evolve more slowly than the underlying amino acids sequences. This feature of structures could in principle be used to reconstruct phylogenetic trees over longer evolutionary timescales than sequence-based approaches, but until now a reliable structure-based tree building method has been elusive. Here, we introduce a rigorous framework for empirical tree accuracy evaluation and tested multiple approaches using sequence and structure information. The best results were obtained by inferring trees from sequences aligned using a local structural alphabet--an approach robust to conformational changes that confound traditional structural distance measures. We illustrate the power of structure-informed phylogenetics by deciphering the evolutionary diversification of a particularly challenging family: the fast-evolving RRNPPA quorum sensing receptors. We were able to propose a more parsimonious evolutionary history for this critical protein family which enables gram-positive bacteria, plasmids and bacteriophages to communicate and coordinate key behaviors. The advent of high-accuracy structural phylogenetics enables a myriad of applications across biology, such as uncovering deeper evolutionary relationships, elucidating unknown protein functions, or refining the design of bioengineered molecules.

bioinformatics↗
bioRxiv2023

Exploring Disease Model Mouse Using Knowledge Graphs: Combining Gene Expression, Orthology, and Disease Datasets

BackgroundThe RIKEN BRC develops and maintains the RIKEN BioResource MetaDatabase to help users explore appropriate target bioresources for their experiments and prepare precise and high-quality data infrastructures. The Swiss Institute of Bioinformatics develops two RDF datasets across multi species for the study of gene expression and orthology: Bgee and Orthologous MAtrix (OMA, an orthology database). MethodsThis study integrates the RIKEN BioResource knowledge graph with Resource Description Framework (RDF) datasets from Bgee, a gene expression database, the OMA, the DisGeNET, a human gene-disease association, Mouse Genome Informatics (MGI), UniProt, and four disease ontologies in the RIKEN BioResource MetaDatabase. Our aim is to explore which model organisms are most appropriate for specific medical science research applications, using SPARQL queries across the integrated datasets. More precisely, we attempt to explore disease-related genes, as well as anatomical parts where these genes are overexpressed and subsequently identify appropriate bioresource candidates available for specific disease research applications. ResultsWe illustrate the above through two use cases targeting either Alzheimers disease or melanoma. We identified two Alzheimers disease-related genes that were overexpressed in the prefrontal cortex (APP and APOE) and 21 RIKEN bioresources predicted to be relevant to Alzheimers disease research. Furthermore, executing a transitive search for the Uberon terms by using the Property Paths function, we identified two melanoma-related genes (HRAS and PTEN), and eight anatomical parts in which these genes were overexpressed, such as the "skin of limb" as an example. Finally, we compared the performance of the federated SPARQL query via the remote Bgee SPARQL endpoint with the performance of a centralized SPARQL query using the Bgee dataset as part of the RIKEN BioResource MetaDatabase. ConclusionsAs a result, we demonstrated that the performance of the federated approach degraded. We concluded that we improved the degradation of the query performance of the federated approach from the BioResource MetaDatabase to the SIB by refining the transferred data through the subquery and enhancing the server specifications, that is optimizing the triple store query evaluation.

bioinformatics↗
bioRxiv2023

Matreex: compact and interactive visualisation of large gene families provides evidence for loss of intraflagellar transport in a myxozoan

Studying gene family evolution strongly benefits from insightful visualisations. However, the evergrowing number of sequenced genomes is leading to increasingly larger gene families, which challenges existing gene tree visualisations. Indeed, most of them present users with a dilemma: display complete but intractable gene trees, or collapse subtrees, thereby hiding their childrens information. Here, we introduce Matreex, a new dynamic tool to scale-up the visualisation of gene families. Matreexs key idea is to use "phylogenetic" profiles, which are dense representations of gene repertoires, to minimise the information loss when collapsing subtrees. We illustrate Matreex usefulness with three biological applications. First, we demonstrate on the MutS family the power of combining gene trees and phylogenetic profiles to delve into precise evolutionary analyses of large multi-copy gene families. Secondly, by displaying 22 intraflagellar transport gene families across 622 species cumulating 5500 representatives, we show how Matreex can be used to automate large-scale analyses of gene presence-absence. Notably, we report for the first time the complete loss of intraflagellar transport in the myxozoan Thelohanellus kitauei. Finally, using the textbook example of visual opsins, we show Matreexs potential to create easily interpretable figures for teaching and outreach. Matreex is available from the Python Package Index (pip install matreex) with the source code and documentation available at https://github.com/DessimozLab/matreex.

evolutionary biology↗
bioRxiv2022

Multifaceted quality assessment of gene repertoire annotation with OMArk

Assessing the quality of protein-coding gene repertoires is critical in an era of increasingly abundant genome sequences for a diversity of species. State-of-the-art genome annotation assessment tools measure the completeness of a gene repertoire, but are blind to other types of errors, such as gene over-prediction or contamination. We developed OMArk, a software relying on fast, alignment-free sequence comparisons between a query proteome and precomputed gene families across the tree of life. OMArk assesses not only the completeness, but also the consistency of the gene repertoire as a whole relative to closely related species. It also reports likely contamination events. We validated OMArk with simulated data, then performed an analysis of the 1805 UniProt Eukaryotic Reference Proteomes, illustrating its usefulness for comparing and prioritizing proteomes based on their quality measures. In particular, we found strong evidence of contamination in 59 proteomes, and identified error propagation in avian gene annotation resulting from the use of a fragmented zebra finch proteome as reference. OMArk is available on GitHub (https://github.com/DessimozLab/OMArk), as a Python package on PyPi, and as an interactive online tool at https://omark.omabrowser.org/.

bioinformatics↗
bioRxiv2022

Reconstructing protein interactions across time using phylogeny-aware graph neural networks

MotivationGenes which are involved in the same biological processes tend to co-evolve. Thus, metabolic pathways, protein complexes, and other kinds of protein-protein interactions can be inferred by looking for correlated patterns of gene retention and loss across the tree of life--a technique called phylogenetic profiling. Recent methodological developments on phylogenetic profiling have focused on scalability improvements to take advantage of the rapidly accumulating genomic data. However, state-of-the-art methods assume that the correlation resulting from co-evolving proteins is uniform across all species considered. This is reasonable for interactions already present at the root of the species considered, but less so for ones that emerge in more recent lineages. To address this challenge and take advantage of recent developments in deep learning methods, we introduce a phylogenetic profiling method which processes large gene co-phylogenies using neural networks. ResultsWe show that post-processing conventional phylogenetic profiles using deep neural networks can improve predictions, but requires onerous training on specific phylogenies. Overcoming this limitation by taking the topology of the species tree as an input, Graph Neural Networks are shown to outperform all other methods when interaction detection is not centered on just one species of interest, while also predicting when interactions appeared and in which taxa they are present. ConclusionGraph Neural Networks constitute a promising new approach for phylogenetic profiling. Our work is a first foray into "dynamic phylogenetic profiling"--the reconstruction of pairwise protein interaction across time. AvailabilityAll of the code is available on the project Git at https://github.com/DessimozLab/HogProf/tree/master/pyprofiler/notebooks/Graphnet. Datasets used are hosted at http://humap2.proteincomplexes.org/download and https://string-db.org/cgi/download. Contactdmoi@unil.ch

bioinformatics↗
bioRxiv2022

Control of PHYTOCHROME KINASE SUBSTRATE subcellular localization and biological activity by protein S-acylation

PHYTOCHROME KINASE SUBSTRATE (PKS) proteins are involved in light-regulated growth orientation responses. They act downstream of phytochromes to control hypocotyl gravitropism in the light and act early in phototropin signaling. Despite their importance for plant development, little is known about their molecular mode of action except that they belong to a protein complex comprising the phototropins at the plasma membrane. Identifying evolutionarily conservation is one approach to reveal biologically important protein motifs. Here, we show that PKS sequences are restricted to seed plants and that these proteins share 6 motifs (A to F from the N- to the C-terminus). While motif D is also found in BIG GRAIN proteins the remining domains are PKS specific. We provide evidence that motif C is S-acylated on highly conserved cysteines, which mediates PKS protein association with the plasma membrane. This motif is also required for PKS4-mediated phototropism and control of hypocotyl gravitropism in the light. Finally, our data suggests that the mode of PKS4 plasma membrane association is important for its biological activity. Our work identifies the mode of plasma membrane association of PKS proteins and strongly suggests that this is their site of action to modulate environmentally regulated organ positioning.

plant biology↗
bioRxiv2022

Read2Tree: scalable and accurate phylogenetic trees from raw reads

The inference of phylogenetic trees is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate--the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution. Applied to Coronaviridae samples, Read2Tree accurately classified highly diverse animal samples and near-identical SARS-CoV-2 sequences on a single tree--thereby exhibiting remarkable breadth and depth. The speed, accuracy, and versatility of Read2Tree enables comparative genomics at scale.

bioinformatics↗
bioRxiv2021

Protein length distribution is remarkably consistent across Life

In every living species, the function of a protein depends on its organisation of structural domains, and the length of a protein is a direct reflection of this. Because every species evolved under different evolutionary pressures, the protein length distribution, much like other genomic features, is expected to vary across species. Here we evaluated this diversity by comparing protein length distribution across 2,326 species (1,688 bacteria, 153 archaea and 485 eukaryotes). We found that proteins tend to be on average slightly longer in eukaryotes than in bacteria or archaea, but that the variation of length distribution across species is low, especially compared to the variation of other genomic features (genome size, number of proteins, gene length, GC content, isoelectric points of proteins). Moreover, most cases of atypical protein length distribution appear to be due to artifactual gene annotation, suggesting the actual variation of protein length distribution across species is even smaller. These results open the way for developing a genome annotation quality metric based on protein length distribution to complement conventional quality measures. Overall, our findings show that protein length distribution between living species is more consistent than previously thought, and provide evidence for a universal purifying selection on protein length, whose mechanism and fitness effect remain intriguing open questions.

evolutionary biology↗
bioRxiv2021

OMAMO: orthology-based model organism selection

SummaryThe conservation of pathways and genes across species has allowed scientists to use non-human model organisms to gain a deeper understanding of human biology. However, the use of traditional model systems such as mice, rats, and zebrafish is costly, time-consuming and increasingly raises ethical concerns, which highlights the need to search for less complex model organisms. Existing tools only focus on the few well-studied model systems, most of which are complex animals. To address these issues, we have developed Orthologous Matrix and Model Organisms, a software and a website that provide the user with the best simple organism for research into a biological process of interest based on orthologous relationships between the human and the species. The outputs provided by the database were supported by a systematic literature review. Availability and implementationhttps://omabrowser.org/omamo/, https://github.com/DessimozLab/omamo Contactchristophe.dessimoz@unil.ch and sina.majidian@unil.ch

bioinformatics↗
bioRxiv2021

Archaeal origins of gamete fusion

Sexual reproduction consists of genome reduction by meiosis and subsequent gamete fusion. Presence of meiotic genes in prokaryotes suggests that DNA repair mechanisms evolved toward meiotic recombination; however, fusogenic proteins resembling those found in eukaryotes were not identified in prokaryotes. Here, we identify archaeal proteins that are homologs of fusexins, a superfamily of fusogens that mediate eukaryotic gamete and somatic cell fusion, as well as virus entry. The crystal structure of a trimeric archaeal Fusexin1 reveals novel features such as a six-helix bundle and an additional globular domain. Ectopically expressed Fusexin1 can fuse mammalian cells, and this process involves the additional domain and a conserved fusion loop. Archaeal fusexin genes exist within integrated mobile elements, potentially linking ancient archaeal gene exchanges and eukaryotic sex. One-Sentence SummaryCell membrane fusion proteins of viruses and eukaryotes are also present in archaea.

evolutionary biology↗