Biology subjects

Bocher, O.

Publications and source records attributed to Bocher, O..

7 recordsLinked to original sources

bioRxiv2026

BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning.

Background Multi-omics integration has emerged as a powerful strategy to decode the molecular complexity of biological systems. However, the diversity of available methods, each designed with distinct assumptions, objectives, and computational requirements, makes method selection, usage and interpretation challenging for nonexpert users. Here we present BiomiX 3.0, an updated version of the BiomiX platform that extends its integration capabilities with four additional methods: Similarity Network Fusion (SNF), NEighborhood-based Multi-Omics clustering (NEMO), Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), and PRAMIGO (Phenotyping netwoRk Application for Multi-omics InteGratiOn), a novel supervised heterogeneous graph transformer (HGT) introduced in this work. Results We benchmarked all five methods, MOFA, DIABLO, SNF, NEMO, and PRAMIGO, on two independent multiomics datasets derived from a Chronic Lymphocytic Leukemia (CLL) cohort comparing IGHV-mutated and unmutated patients, and a pulmonary tuberculosis (PTB) cohort versus healthy controls. Supervised methods (DIABLO, PRAMIGO) consistently achieved higher condition-specific discrimination as measured by the Adjusted Rand Index (ARI) and the Adjusted Mutual Information (AMI). In contrast, unsupervised methods (SNF, NEMO) revealed alternative patient stratifications driven by independent sources of biological variance while MOFA performed in a semi-supervised way occupies an intermediate position, capturing latent factors that explain both disease-associated and orthogonal sources of variance. Systematic gene-centric analysis of the top-ranked features prioritized by each method was supported by manual biological annotation of shared and method-specific signals. Across cohorts, we annotated 154 shared features (116 genes, 38 metabolites) and 60 method-unique features per cohort, demonstrating that no single integration strategy captures the full landscape of biologically relevant signals. In the CLL cohort, shared features spanned B-cell receptor biology, innate immune signaling, RAS/MAPK activation, and epigenetic regulation, while methodunique features revealed supervised-method-specific insights into vesicle trafficking (DIABLO), immune checkpoints (MOFA), and ncRNA regulation (PRAMIGO). In the PTB cohort, a convergent interferon/innate immune signature dominated shared features across all methods. Still, method-unique analysis uncovered DIABLO-specific acylcarnitine metabolic reprogramming, MOFA-specific restoration of lysosomal trafficking, and PRAMIGO-specific {gamma}{delta} T-cell and immunoglobulin repertoire diversity. Across both cohorts, SNF and NEMO proved useful for detecting biological and technical sources of variation that were orthogonal to the primary condition of interest. Ultimately, PRAMIGO uniquely enables the construction of heterogeneous graphs modeling cross-modal molecular interactions, uncovering epigenetic co-regulation programs in CLL and multi-omics inflammatory modules in PTB that are difficult to identify using conventional integration approaches. Conclusions BiomiX 3.0 provides a graphical user interface (GUI) multi-method integration environment that democratizes access to state-of-the-art multi-omics analysis. By combining both unsupervised and supervised integration strategies within a unified platform and introducing graph-based learning through PRAMIGO, BiomiX 3.0 enables researchers across disciplines with complementary tools to interrogate the biological sources of variation in their data, without requiring bioinformatics expertise.

bioinformatics↗

bioRxiv2026

Sequencing depth overcomes extraction bias: repurposing human WGS data for salivary microbiome profiling

Large-scale human genomic projects have generated whole-genome sequencing (WGS) data from hundreds of thousands of individuals, primarily to study host genetic variation. When saliva is the DNA source, the resulting datasets also contain microbial reads that are routinely discarded. Here, we investigate whether these host-centric WGS workflows can yield reliable microbiome profiles, effectively doubling the research value of existing data without additional sampling. We compared non-human reads from 39 deeply sequenced saliva samples from the GAZEL cohort (miG dataset; median [~]43 million reads/sample) with 14 samples processed with microbiome-optimized extraction (ASAL; median [~]4.3 million reads/sample), using two complementary classifiers: meteor, a coverage-based mapper against a curated saliva-specific database, and sylph, a k-mer classifier against the Genome Taxonomy Database (GTDB). Despite the absence of microbial lysis optimization, miG samples showed up to 3-fold higher species richness, [~]10-fold greater sequencing depth, and significantly lower inter-sample variability (PERMANOVA R{superscript 2} = 0.10, p = 0.001; BETADISPER p = 0.0036). Rarefaction to 10 reads eliminated most compositional differences, demonstrating that sequencing depth is the primary driver of community stability. Only [~]2% of detected taxa (12 of 592) showed extraction-related differences. The two classifiers exhibited fundamentally different depth-sensitivity profiles, with sylph retaining systematic detection asymmetries even after depth normalization, highlighting that classifier choice introduces biases that affect cross-study comparisons. These results show that biobank WGS data from saliva can be repurposed for robust, population-scale oral microbiome analyses, enabling simultaneous investigation of host genomic variation and the microbiome from the same archived samples. ImportanceSaliva-based whole-genome sequencing datasets generated across various cohorts to study human genetics contain non-human reads that are routinely discarded, thereby overlooking valuable microbial information. We show that these reads are sufficient to reconstruct robust oral microbiome profiles -- without any additional sampling or laboratory work. This finding unlocks a vast archive of existing genomic data for retrospective microbiome research, enabling population-scale studies of oral microbial diversity, host-microbiome interactions, and disease associations at minimal additional cost. We further demonstrate that the choice of taxonomic classifier introduces systematic, depth-dependent biases that persist even after normalization, a practical consideration for any cross-cohort or multi-platform microbiome study.

genomics↗

bioRxiv2025

Diet-responsive proteogenomic effects following short-term restriction of animal products in humans

The effect of diet on genetic regulation in humans remains largely unexplored. Here, we investigated gene-by-diet interactions in a unique group of apparently healthy individuals (N=200) who alternate between omnivory and dietary restriction of animal products for religious reasons. Using longitudinal plasma proteomic and genotypic data, we identified diet-responsive cis-protein quantitative trait loci (cis-pQTLs) including gain of genetic regulatory effects for LBR and MSRA, proteins linked to cholesterol and methionine metabolism respectively. LBR-associated diet-responsive cis-pQTL rs74148404 showed suggestive evidence of colocalization with obesity exclusively under dietary restriction, suggesting diet-dependent differential genetic risk for disease. Additionally, a novel dietary restriction-associated cis-pQTL for metabolic regulator FGF21 colocalized with eosinophil and platelet traits pointing to diet-sensitive immunometabolic signalling. By parallel profiling of a continuously omnivorous control group (N=211), we also uncovered seasonally dynamic gain and loss of genetic regulation for proteins linked to apoptosis in immune system pathways (MAVS, CASP3, PDLIM7 IL12RB1), effects likely masked by animal product restriction. These findings reveal dynamic diet- and season-sensitive regulatory mechanisms with implications for precision nutrition and individualized disease prevention strategies, and underscore the need to integrate environmental context into genetic studies of health and disease.

genomics↗

bioRxiv2024

Insights into the metabolic consequences of type 2 diabetes

Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which these are affected by T2D and the involvement of genetics in mediating these relationships remain to be elucidated. In this study, we investigate the interplay between genetics, metabolomics and T2D risk in the UK Biobank dataset. We find 79 metabolites with a causal association to T2D, mostly spanning lipid-related classes, while twice as many metabolites are causally affected by T2D liability, including branched-chain amino acids. Secondly, using an interaction quantitative trait locus (QTL) analysis, we describe four metabolites, consistently replicated in an independent dataset from the Estonian Biobank, for which genetic loci in two different genomic regions show attenuated regulation in T2D cases compared to controls. The significant variants from the interaction QTL analysis are significant QTLs for the corresponding metabolites in the general population, but are not associated with T2D risk, pointing towards consequences of T2D on the genetic regulation of metabolite levels. Finally, we find 165 metabolites associated with microvascular, macrovascular, or both types of T2D complications, with only a few discriminating between complication classes. Of the 165 metabolites, 40 are not causally linked to T2D in either direction, suggesting biological mechanisms specific to the occurrence of complications. Overall, this work provides a map of the metabolic consequences of T2D and of the genetic regulation of metabolite levels and enable to better understand the trajectory of T2D leading to complications.

genomics↗

bioRxiv2024

PSAP-genomic-regions: a method leveraging population data to prioritize coding and non-coding variants in whole genome sequencing for rare disease diagnosis

The introduction of next generation sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The PSAP (Population Sampling Probability) method was developed to meet this aim but only for coding variants in exome data. To address the challenge of the analysis of non-coding variants in whole genome sequencing data, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially-generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large datasets of exomes and genomes from the general population. We found that PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than fifty percent of non-coding ClinVar variants, especially those involved in splicing, were among the top 10 variants of the genome. In addition, our approach gave similar results compared to PSAP-genes regarding the scoring of coding variants. On real sequencing data from 6 patients with Cerebral Small Vessel Disease and 9 patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases. PSAP-genomic-regions is implemented as a user-friendly Snakemake workflow, accessible to both researchers and clinicians which can easily integrate up-to-date annotation from large databases. Author summaryIn recent years, improvement in DNA sequencing technologies has allowed the identification of many genes involved in rare diseases. Nonetheless, the molecular diagnosis is still unknown for more than half of rare diseases cases. This is in part due to the large heterogeneity of molecular causes in rare diseases. This also highlights the need for the development of new methods to prioritize pathogenic variants from DNA sequencing data at the scale of the whole genome and not only coding regions. With PSAP-genomic-regions, we offer a strategy to prioritize coding and non-coding variants in whole-genome data from a single individual in need of a diagnosis. The PSAP-genomic-regions combines information on the predicted pathogenicity and frequency of variants in the context of functional regions of the genome. In this work, we compare the PSAP-genomic-regions strategy to other variant prioritization strategies on simulated and real data. We show the better performance of PSAP-genomic-regions over a classical approach based on variant pathogenicity scores alone. PSAP-genomic-regions provides a straightforward approach to prioritize causal pathogenic variants, especially non-coding ones, that are often missed with other strategies and could explain the cause of undiagnosed rare diseases.

genetics↗

bioRxiv2022

Genetic population structure across Brittany and the downstream Loire basin provides new insights on the demographic history of Western Europe

European genetic ancestry originates from three main ancestral populations - Western hunter-gatherers, early European farmers and Yamnaya Eurasian herders - whose edges geographically met in present-day France. Despite its central role to our understanding of how the ancestral populations interacted and gave rise to modern population structure, the population history of France has remained largely understudied. Here, we analysed the high-coverage whole-genome sequences and genome-wide genotype profiles of respectively 856 and 3,234 present-day individuals from the northern half of France, and merged them with publicly available present-day and ancient Europe-wide genotype datasets. We also explored, for the first time, the whole-genome sequences of six mediaeval individuals (300-1100 CE) from Western France to gain insights into the genetic impact of what is commonly known as the Migration Period in Europe. We found extensive fine-scale population structure across Brittany and the downstream Loire basin, emphasising the need for investigating local populations to better understand the distribution of rare and putatively deleterious variants across space. Overall, we observed an increased population differentiation between the northern and southern sides of the river Loire, which are characterised by different proportions of steppe vs. Neolithic-related ancestry. Samples from Western Brittany carry the largest levels of steppe ancestry and show high levels of allele sharing with individuals associated with the Bell Beaker complex, levels that are only comparable with those found in populations lying on the northwestern edges of Europe. Together, our results imply that present-day individuals from Western Brittany retain substantial legacy of the genetic changes that occurred in Northwestern Europe following the arrival of the Bell Beaker people c. 2500 BCE. Such genetic legacy may explain the sharing of disease-related alleles with other present-day populations from Western Britain and Ireland.

genetics↗

bioRxiv2021

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the GnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 that is enriched for rare variants in early-onset patients and that was that was missed by standard sliding windows procedures. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages. Author SummaryTechnological progresses have made possible whole genome sequencing at an unprecedented scale, opening up the possibility to explore the role of genetic variants of low frequency in common diseases. The challenge is now methodological and requires the development of novel methods and strategies to analyse sequencing data that are not limited to assessing the role of coding variants. With RAVA-FIRST, we propose a novel strategy to investigate the role of rare variants in the whole-genome that takes benefit from biological information. Especially, RAVA-FIRST relies on testing units that go beyond genes to gather rare variants in the association tests. In this work, we show that this new strategy presents several advantages compared to existing methods. RAVA-FIRST offers an easy and straightforward analysis of genome-wide rare variants, especially the intergenic ones which are frequently left behind, making it a promising tool to get a better understanding of the biology of complex diseases.

genetics↗