Biology subjects

Veber, P.

Publications and source records attributed to Veber, P..

14 recordsLinked to original sources

bioRxiv2025

Statistical detection of protein sites associated with continuous traits

Comparative genomic data can be used to look for substitutions in coding sequences that are associated with the variation of a particular phenotypic trait. A few statistical methods have been proposed to do so for phenotypes represented by discrete values. For continuous traits, no such statistical approach has been proposed, and researchers have resorted to sensible but uncharacterized criteria. Here, we investigate a phylogenetic model for coding sequences where amino acid preferences at a site are given by a continuous function of a quantitative trait. This function is inferred from the amino acids and the trait values in extant species and requires inferred point estimates of ancestral values of the trait at internal nodes. For detecting sites whose evolution is associated with this trait, we use a significance test against the hypothesis that amino acid preference does not depend on the trait. This procedure is compared to simpler strategies on simulated alignments. It displays an increased recall for low false positive rates, which is of special importance for performing whole-genome scans. This comes however at a much higher computational cost, and we suggest using a simple test to filter promising candidate sites. We then revisit a dataset of alignments for 62 species of mammals, using longevity as a phenotypic trait. We apply our method to three protein families that have previously been proposed to display sites associated with variation in lifespan in mammals. Using a graphical representation extracted from the detailed phylogenetic analysis of candidate sites, we suggest that the evidence for this in the sequence data alone is weak. The proposed method has been added to our Pelican software. It is available at https://gitlab.in2p3.fr/phoogle/pelican and can now be used with both discrete and continuous phenotypes to search for sites associated with phenotypic variation, on data sets with thousands of alignments.

bioinformatics↗

bioRxiv2025

Characterization of selective pressures acting on protein sites with Deep Learning

It is often useful, in the field of molecular evolution, to identify the selective pressures acting on a particular site of a protein to better understand its function. This is typically done with likelihood-based approaches applied to codon sequences in a phylogenetic context. However, these approaches are computationally costly. Here we adapt a linear transformer neural network architecture, which has been shown to be able to reconstruct accurate pairwise distances from sequence alignments, to identify selective pressures acting on individual amino acid sites. We design different versions of the architecture and train and test them on simulations. We compare the results of one of our best models to state-of-the-art likelihood-based methods and find that it outperforms it when it is applied to data that resemble its training data, but that it performs less well when applied to datasets that do not resemble the ones the model has been trained on. In all cases, our approach operates at a fraction of the computational cost of likelihood-based methods. These results suggest that such a neural network architecture can compare very favorably to state-of-the-art approaches to characterize selection pressures acting on coding sequences, but that it must be trained on datasets representative of empirical data.

bioinformatics↗

bioRxiv2024

Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge

Interpreting transcriptomic data presents significant challenges, particularly in non-targeted approaches. While modern functional enrichment methods are well-suited for experimental designs involving two conditions, they are less applicable to data series. In this context, we developed Cluefish, a free and open-source, semi-automated R workflow designed for untargeted, comprehensive biological interpretation of transcriptomic data series. Cluefish applies over-representation analysis on pre-clustered protein-protein interaction networks, using clusters as anchors to identify smaller, more specific biological functions. Innovative features, including cluster merging and recovery of isolated genes through shared biological contexts, enable a more complete exploration of the data. We applied Cluefish to an in-house dataset with zebrafish exposed to a dose-gradient of dibutyl phthalate, and to two published toxicology datasets featuring different organisms. Combined with DRomics, a tool for dose-response analysis--Cluefish identified gene clusters deregulated at low doses and linked to biological functions overlooked by the standard approach. Notably, it revealed that retinoid signalling disruption may be the most sensitive pathway affected by dibutyl phthalate during zebrafish development, potentially leading to morphological changes. The Cluefish workflow aims to provide valuable clues for biological hypothesis generation and experimental validation. It is freely available at https://github.com/ellfran-7/cluefish. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/627334v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@19d9903org.highwire.dtl.DTLVardef@1a8a118org.highwire.dtl.DTLVardef@1e84c3aorg.highwire.dtl.DTLVardef@1e4ac35_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Meta-analysis reveals limited reproducibility of lncRNA-related findings in hepatocellular carcinoma research

Background & AimsThe human genome comprises tens of thousands of long non-coding RNAs (lncRNAs), whose functionality is highly debated. In the field of hepatocellular carcinoma (HCC) research, as for other cancer types, lncRNAs are increasingly reported to act as oncogenes or tumor suppressors and are put forward as useful diagnostic or prognostic biomarkers. Here, we investigate the reliability of these claims by performing a meta-analysis of the associations between HCC and lncRNAs reported in the scientific literature, and by assessing lncRNA expression patterns in two HCC patient cohorts. Approach & ResultsWhile nowadays up to 6% of all HCC-related publications cite lncRNAs, we show that most reported associations between HCC and lncRNAs have not been reproduced. In general, lncRNAs are less often differentially expressed between HCC tissues and controls compared to protein-coding genes. However, HCC-associated lncRNAs are frequently up-regulated in tumor samples, consistent with the fact that they are often selected based on transcriptome-wide comparative analyses. We perform a detailed examination of the 25 lncRNAs that are most frequently cited in association with HCC. For 10 out of these 25 lncRNAs, including well known lncRNAs such as MALAT1, NEAT1, H19 and XIST, we identify important conflicts between the biological roles and expression patterns previously reported for them in HCC and the expression patterns that we observe here. Finally, we observe that HCC-associated publications that cite lncRNAs are retracted three times more often than publications that cite protein-coding genes. ConclusionsOur results thus highlight the poor reproducibility of lncRNA-related claims in association with HCC, which is problematic in a context where new biomarkers and molecular targets for therapy are greatly needed.

cancer biology↗

bioRxiv2024

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art Maximum likelihood and Bayesian inference methods limits their usability under realistic evolutionary models. Harnessing recent advances in likelihood-free inference and geometric deep learning, we introduce Phyloformer, a fast and accurate method for evolutionary distance estimation and phylogenetic reconstruction. Sampling many trees and sequences under an evolutionary model, we train the network to learn a function that enables predicting the former from the latter. Under a commonly used model of protein sequence evolution and exploiting GPU acceleration, it outpaces fast distance methods while matching maximum likelihood accuracy on simulated and empirical data. Under more complex models, some of which include dependencies between sites, it outperforms other methods. Our results pave the way for the adoption of sophisticated realistic models for phylogenetic inference.

bioinformatics↗

bioRxiv2022

Neural Networks beyond explainability: Selective inference for sequence motifs

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference--a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).

bioinformatics↗

bioRxiv2022

Evaluation of methods to detect shifts in directional selection at the genome scale

Identifying the footprints of selection in coding sequences can inform about the importance and function of individual sites. Analyses of the ratio of non-synonymous to synonymous sub-stitutions (dN /dS) have been widely used to pinpoint changes in the intensity of selection, but cannot distinguish them from changes in the direction of selection, i.e., changes in the fitness of specific amino acids at a given position. A few methods that rely on amino acid profiles to detect changes in directional selection have been designed, but their performance have not been well characterized. In this paper, we investigate the performance of 6 of these methods. We evaluate them on simulations along empirical phylogenies in which transition events have been annotated, and compare their ability to detect sites that have undergone changes in the direction or intensity of selection to that of a widely used dN /dS approach, codemls branch-site model A. We show that all methods have reduced performance in the presence of biased gene conversion but not CpG hypermutability. The best profile method, Pelican, a new implementation of [Tamuri et al., 2009], performs as well as codeml in a range of conditions except for detecting relaxations of selection, and performs better when tree length increases, or in the presence of persistent positive selection. It is fast, enabling genome-scale searches for site-wise changes in the direction of selection associated with phenotypic changes.

evolutionary biology↗

bioRxiv2022

GOntact: using chromatin contacts to infer Gene1Ontology enrichments for cis-regulatory elements

Cis-regulatory elements (CREs) can be efficiently predicted genome-wide, but identifying their target genes remains challenging. Regulatory interactions between genes and CREs can take place over long genomic distances, often bypassing genes. Inferring CRE targets based on genomic proximity, as traditionally done in genomic studies, can thus be misleading. Thanks to chromosome conformation capture techniques, chromatin contacts between CREs and gene promoters can be assayed at the genome-wide scale, thus permitting more accurate predictions of CRE target genes. Here, we present a standalone computational tool and webserver named GOntact, which infers CRE target genes using chromosome conformation capture data. GOntact can be used to derive Gene Ontology (GO) enrichments for CRE sets, thus providing a basis for functional interpretation. We apply GOntact on enhancers active in several embryonic tissues, using Promoter Capture Hi-C data to infer chromatin contacts between genes and CREs. We show that GOntact predicts functional annotations that are coherent with enhancer activity. Compared to genomic proximity, GOntact predicts consistent but more specific functional annotations. With the increasing availability of high-resolution chromatin contact data, we believe that GOntact can provide better-informed target genes and functional predictions for CREs. GOntact is available at https://gontact.univ-lyon1.fr (webserver) and https://gitlab.in2p3.fr/anamaria.necsulea/GOntact (standalone command-line tool).

genomics↗

bioRxiv2022

A quantitative, genome-wide analysis in Drosophila reveals transposable elements' influence on gene expression is species-specific

Transposable elements (TEs) are parasite DNA sequences that are able to move and multiply along the chromosomes of all genomes. They can be controlled by the host through the targeting of silencing epigenetic marks, which may affect the chromatin structure of neighboring sequences, including genes. In this study, we used transcriptomic and epigenomic high-throughput data produced from ovarian samples of several Drosophila melanogaster and Drosophila simulans wild-type strains, in order to finely quantify the influence of TE insertions on gene RNA levels and histone marks (H3K9me3 and H3K4me3). Our results reveal a stronger epigenetic effect of TEs on ortholog genes in D. simulans compared to D. melanogaster. At the same time, we uncover a larger contribution of TEs to gene H3K9me3 variance within genomes in D. melanogaster, which is evidenced by a stronger correlation of TE numbers around genes with the levels of this chromatin mark in D. melanogaster. Overall, this work contributes to the understanding of species-specific influence of TEs within genomes. It provides a new light on the considerable natural variability provided by TEs, which may be associated with contrasted adaptive and evolutionary potentials. Significance StatementTransposable elements (TEs) are parasitic DNA sequences that are widespread components of all genomes. In this study, we combined genomic, transcriptomic and epigenomic high-throughput data produced from ovarian samples of Drosophila melanogaster and Drosophila simulans wild-type strains, in order to finely quantify the genome-wide influence of TE insertions on gene expression. Our results uncover contrasted patterns depending on the strain, which may have evolutionary impacts.

genomics↗

bioRxiv2021

Large-scale deregulation of gene expression by artificial light at night in tadpoles of common toads

Artificial light at night (ALAN) affects numerous physiological and behavioural mechanisms in various species by potentially disturbing circadian timekeeping systems. Although gene-specific approaches have already shown the deleterious effect of ALAN on the circadian clock, immunity and reproduction, large-scale transcriptomic approaches with ecologically relevant light levels are still lacking to assess the global impact of ALAN on biological processes. Moreover, studies have focused mainly on variations in gene expression during the night in the presence of ALAN but never during the day. In a controlled laboratory experiment, transcriptome sequencing of Bufo bufo tadpoles revealed that ALAN affected gene expression at both night and daytime with a dose-dependent effect and globally induced a downregulation of genes. ALAN effects were detected at very low levels of illuminance (0.1 lux) and affected mainly genes related to the innate immune system and, to a lesser extend to lipid metabolism. These results indicate that a broad range of physiological pathways is impacted at the molecular level by very low levels of ALAN potentially resulting in reduced survival under environmental immune challenges.

ecology↗

bioRxiv2021

LncRNA analyses reveal increased levels of non-coding centromeric transcripts in hepatocellular carcinoma

The search for new biomarkers and drug targets for hepatocellular carcinoma (HCC) has spurred an interest in long non-coding RNAs (lncRNAs), often proposed as oncogenes or tumor suppressors. Furthermore, lncRNA expression patterns can bring insights into the global de-regulation of cellular machineries in tumors. Here, we examine lncRNAs in a large HCC cohort, comprising RNA-seq data from paired tumor and adjacent tissue biopsies from 114 patients. We find that numerous lncRNAs are differentially expressed between tumors and adjacent tissues and between tumor progression stages. Although we find strong differential expression for most lncRNAs previously associated with HCC, the expression patterns of several prominent HCC-associated lncRNAs disagree with their previously proposed roles. We examine the genomic characteristics of HCC-expressed lncRNAs and reveal an enrichment for repetitive elements among the lncRNAs with the strongest expression increases in advanced-stage tumors. This enrichment is particularly striking for lncRNAs that overlap with satellite repeats, a major component of centromeres. Consistently, we find increased non-coding RNA transcription from centromeres in tumors, in the majority of patients, suggesting that aberrant centromere activation takes place in HCC.

cancer biology↗

bioRxiv2019

Automated reconstruction of all gene histories in large bacterial pangenome datasets and search for co-evolved gene modules with Pantagruel

The availability of bacterial pangenome data grows exponentially, requiring efficient new methods of analysis. Currently popular approaches for the fast comparison of genomes have the drawback of not being based on explicit evolutionary models of diversification. Making sense of bacterial genome evolution, and notably in the accessory genome, requires however to take into account the complex processes by which the genomes evolve. Here we present the Pantagruel bioinformatic software pipeline, which enables the construction of a complete bacterial pangenome database geared towards the inference of gene evolution scenarios using gene tree/species tree reconciliation. Pantagruel is a modular pipeline that combines state-of-the-art external software with unique new methods. It can be executed with no supervision to perform a standard pangenome analysis, or be configured by advanced users to integrate methods of choice. A relational database underlies its data structure, allowing efficient retrieval of the large-scale data generated by integrative analyses of pangenome evolutionary history. From the reconstructed gene evolution scenarios, two main outputs are derived: firstly the gene tree-aware assignation of orthology, allowing the fine analysis of gene gain and loss history over the species phylogeny, and secondly a network of gene-to-gene association based on correlated events in scenarios of gene evolution, leading to the definition of co-evolved gene modules. Pantagruel is available as an open source software package at https://github.com/flass/pantagruel.

bioinformatics↗

bioRxiv2019

Detecting convergent adaptive amino acid evolution

In evolutionary genomics, researchers have taken an interest in identifying in the genomes substitutions that subtend convergent phenotypic adaptations. This is a difficult question to address, because genomes contain billions of sites, many of which have substituted in the lineages where the adaptations took place, and yet are not linked to them. Those extra substitutions may be linked to other adaptations, may be neutral, or may be linked to mutational biases. Furthermore, one can think of various ways of defining substitutions of interest, and various methods that match those definitions have been used, resulting in different sets of candidate substitutions. In this manuscript we first clarify how adaptation to convergent phenotypic evolution can manifest itself in coding sequences. Second, we review methods that have been proposed to detect convergent adaptive evolution in coding sequences and expose the assumptions that underlie them. Finally, we examine their power on simulations of convergent changes, including in the presence of a confounding factor.

bioinformatics↗

bioRxiv2017

MaxTiC: Fast Ranking Of A Phylogenetic Tree By Maximum Time Consistency With Lateral Gene Transfers

Lateral gene transfers between ancient species contain information about the relative timing of species diversification. Specifically, the ancestors of a donor species must have existed before the descendants of the recipient species. Hence, the detection of a transfer event can be translated into a time constraint between nodes of a phylogeny if the donor and recipient can be identified. When a set of transfers is detected by interpreting the phylogenetic discordance between gene trees and a species tree, the set of all deduced time constraints can be used to rank the species tree, i.e. order totally its internal nodes. Unfortunately lateral gene transfer detection is challenging and current methods produce a significant proportion of false positives. As a result, often, no ranking of the species tree is compatible with the full set of time constraints deduced from predicted transfers. Here we propose a method, implemented in a software called MaxTiC (Maximum Time Consistency), which takes as input a species tree and a series of (possibly inconsistent) time constraints between its internal nodes, weighted by confidence scores. MaxTiC outputs a ranked species tree compatible with a subset of constraints with maximum cumulated confidence score. We extensively test the method on simulated datasets, under a wide range of conditions that we compare to measures on biological datasets. In most conditions the obtained ranked tree is very close to the real one, confirming the potential of dating the history of life with transfers by maximizing time consistency. MaxTiC is freely available, distributed along with a documentation and several examples: https://github.com/ssolo/ALE/tree/master/maxtic.

evolutionary biology↗