Biology subjects

Nevers, Y.

Publications and source records attributed to Nevers, Y..

5 recordsLinked to original sources

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

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↗

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↗

bioRxiv2020

A Linear-Time Solution to the Labeled Robinson-Foulds Distance Problem

MotivationComparing trees is a basic task for many purposes, and especially in phylogeny where different tree reconstruction tools may lead to different trees, likely representing contradictory evolutionary information. While a large variety of pairwise measures of similarity or dissimilarity have been developed for comparing trees with no information on internal nodes, very few address the case of inner node-labeled trees. Yet such trees are common; for instance reconciled gene trees have inner nodes labeled with the type of event giving rise to them, typically speciation or duplication. Recently, we proposed a formulation of the Labeled Robinson Foulds edit distance with edge extensions, edge contractions between identically labeled nodes, and node label flips. However, this distance proved difficult to compute, in particular because shortest edit paths can require contracting "good" edges, i.e. edges present in the two trees. ResultsHere, we report on a different formulation of the Labeled Robinson Foulds edit distance -- based on node insertion, deletion and label substitution -- which we show can be computed in linear time. The new formulation also maintains other desirable properties: being a metric, reducing to Robinson Foulds for unlabeled trees and maintaining an intuitive interpretation. The new distance is computable for an arbitrary number of label types, thus making it useful for applications involving not only speciations and duplications, but also horizontal gene transfers and further events associated with the internal nodes of the tree. To illustrate the utility of the new distance, we use it to study the impact of taxon sampling on labeled gene tree inference, and conclude that denser taxon sampling yields better trees.

bioinformatics↗