Tailored Omics Data Analysis Solutions for Your Research Projects
Based in Lyon, AltraBio specializes in omics data analysis, combining 20 years of expertise in bioinformatics, biostatistics, and biology to analyze your omics data (transcriptomics, proteomics, epigenomics, etc.). Our collaborative approach ensures results aligned with your research goals, whether for biomarker discovery, biological mechanism deciphering, or multi-omics data integration.
Expertise in Bioinformatics for Omics Data Analysis
Our team evaluates data quality (RNA-Seq, proteomics, etc.) and ensures consistency with experimental design. We address outliers and non-design-related effects to guarantee meaningful omics data analysis.
Experimental designs often involve multiple factors (donor, cell type, treatment, dose, time points). We identify the optimal statistical model for your omics data (e.g., batch effect corrections, multi-factor analysis).
Specializing in data integration (transcriptomics, cytometry, medical data), we leverage AI to uncover biomarkers and molecular signatures.
Omics Data Analysis Services by Type
Transcriptomics studies all RNA in a cell to reveal active genes and expression levels. In Lyon, AltraBio uses this approach to identify biomarkers and gene regulation mechanisms, including RNA-Seq, single-cell, and spatial transcriptomics.
Extended services: Partnerships with european NGS platforms for data generation.
Proteomics quantifies proteins and their modifications, complementing transcriptomic insights. Our team identifies therapeutic targets and validates protein biomarkers.
Genomics explores genetic variations (SNPs, mutations) and their phenotypic associations.
Extended services: Partnerships with european NGS platforms for data generation.
Epigenomics examines DNA modifications (methylation, chromatin) that regulate gene expression without altering sequences. We analyze these to understand mechanisms like aging or treatment responses.
Extended services: Partnerships with european NGS platforms for data generation.
Multi-omics integration combines datasets (transcriptomics + proteomics) for systemic biological insights. We cross-reference data to identify unique molecular signatures.
Biological Expertise
We analyze your omics data (transcriptomics, proteomics, epigenomics) in biological context to extract actionable insights.
Beyond gene lists, we integrate literature and database knowledge to understand biological mechanisms and formulate testable hypotheses.
Reports and Tools
Our reports for researchers and industries include visualizations (volcano plots, heatmaps) and clear recommendations.
Each project concludes with a meeting to clarify methodologies and results.
Explore statistical results via our WikiBioPath web interface for dynamic omics data visualization (PCA, enrichment analysis, etc.).
Discover WikiBioPath
Why Choose AltraBio?
With two decades of expertise in maths, stats, biology, and medical science, AltraBio delivers actionable insights without hype. A trusted partner in Lyon for omics data analysis.
« Even in the age of generative AI, Altrabio’s two decades of expertise in maths, stats, biology, and medical science remain invaluable. They don’t just talk, they do. No flashy marketing, no inflated costs, just solid, thoughtful work from study design to actionable insights. A trusted partner, for twenty years, in a world full of noise. Highly recommend working with them to make real sense of your complex biomedical and omics data. »
Discover how our tailored solutions in omics data analysis can accelerate your R&D projects.
Publications
Discover our peer-reviewed publications on omics data analysis, recognized by the scientific community.
2025
Ribeiro, Sara; Alves, Karine; Nourikyan, Julien; Lavergne, Jean-Pierre; de Bernard, Simon; Buffat, Laurent
Identifying potential novel widespread determinants of bacterial pathogenicity using phylogenetic-based orthology analysis Journal Article
In: Front. Microbiol., vol. 16, 2025, ISSN: 1664-302X.
@article{Ribeiro2025,
title = {Identifying potential novel widespread determinants of bacterial pathogenicity using phylogenetic-based orthology analysis},
author = {Sara Ribeiro and Karine Alves and Julien Nourikyan and Jean-Pierre Lavergne and Simon de Bernard and Laurent Buffat},
doi = {10.3389/fmicb.2025.1494490},
issn = {1664-302X},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {Front. Microbiol.},
volume = {16},
publisher = {Frontiers Media SA},
abstract = {<jats:sec><jats:title>Introduction</jats:title><jats:p>The global rise in antibiotic resistance and emergence of new bacterial pathogens pose a significant threat to public health. Novel approaches to uncover potential novel diagnostic and therapeutic targets for these pathogens are needed.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this study, we conducted a large-scale, phylogenetic-based orthology analysis (OA) to compare the proteomes of pathogenic to humans (HP) and non-pathogenic to humans (NHP) bacterial strains across 734 strains from 514 species and 91 families.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Using a dedicated workflow, we identified 4,383 hierarchical orthologous groups (HOGs) significantly associated with the HP label, many of which are linked to critical factors such as stress tolerance, metabolic versatility, and antibiotic resistance. Both known virulence factors (VFs) and potential novel widespread pathogenicity determinants were uncovered, supported by both statistical testing and complementary protein domain analysis.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>By integrating curated strain-level pathogenicity annotations from BacSPaD with phylogeny-based OA, we introduce a novel approach and provide a novel resource for bacterial pathogenicity research.</jats:p></jats:sec>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bonduelle, Olivia; Delory, Tristan; Moscardini, Isabelle Franco; Ghidi, Marion; Bennacer, Selma; Wokam, Michele; Lenormand, Mathieu; Petrier, Melissa; Rogeaux, Olivier; de Bernard, Simon; Alves, Karine; Nourikyan, Julien; Lina, Bruno; Combadiere, Behazine; Janssen, Cécile
Boosting effect of high-dose influenza vaccination on innate immunity among elderly: a randomized-control trial Journal Article
In: JCI Insight, 2025, ISSN: 2379-3708.
@article{pmid40036077,
title = {Boosting effect of high-dose influenza vaccination on innate immunity among elderly: a randomized-control trial},
author = {Olivia Bonduelle and Tristan Delory and Isabelle Franco Moscardini and Marion Ghidi and Selma Bennacer and Michele Wokam and Mathieu Lenormand and Melissa Petrier and Olivier Rogeaux and Simon de Bernard and Karine Alves and Julien Nourikyan and Bruno Lina and Behazine Combadiere and Cécile Janssen},
doi = {10.1172/jci.insight.184128},
issn = {2379-3708},
year = {2025},
date = {2025-03-01},
urldate = {2025-03-01},
journal = {JCI Insight},
abstract = {BACKGROUND: The high-dose quadrivalent influenza vaccine (QIV-HD) showed superior efficacy against laboratory-confirmed illness than the standard-dose quadrivalent influenza vaccine (QIV-SD) in randomized-controlled trials with elderly. However, specific underlying mechanism remains unclear.nnMETHODS: This Phase-IV randomized control trial compared early innate responses induced by QIV-HD and QIV-SD in 59 subjects aged >65 years. Systemic innate cells and gene signatures at Day (D) 0 and D1, hemagglutinin inhibition antibody (HIA) titers at D0 and D21 post-vaccination were assessed.nnRESULTS: QIV-HD elicited robust humoral response with significantly higher antibody titers and seroconversion rates than QIV-SD. At D1 post-vaccination, QIV-HD recipients showed significant reduction in innate cells, including conventional dendritic cells and natural killer cells than QIV-SD, correlating with significantly increased HIA titers at D21. Blood transcriptomic analysis revealed greater amplitude of gene expression in QIV-HD arm, encompassing genes related to innate immune response, interferons, and antigen processing and presentation and correlated with humoral responses. Interestingly, comparative analysis with a literature dataset from young adults vaccinated with influenza standard-dose vaccine highlighted strong similarities in gene expression patterns and biological pathways with the elderly vaccinated with QIV-HD.nnCONCLUSION: QIV-HD induces higher HIA titers than QIV-SD, a youthful boost of the innate gene expression significantly associated with high HIA titers.nnTRIAL REGISTRATION: EudraCT Number: 2021-004573-32.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2024
Ribeiro, Sara; Chaumet, Guillaume; Alves, Karine; Nourikyan, Julien; Shi, Lei; Lavergne, Jean-Pierre; Mijakovic, Ivan; de Bernard, Simon; Buffat, Laurent
BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata Journal Article
In: Pathogens, vol. 13, no. 8, 2024, ISSN: 2076-0817.
@article{pmid39204272,
title = {BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata},
author = {Sara Ribeiro and Guillaume Chaumet and Karine Alves and Julien Nourikyan and Lei Shi and Jean-Pierre Lavergne and Ivan Mijakovic and Simon de Bernard and Laurent Buffat},
doi = {10.3390/pathogens13080672},
issn = {2076-0817},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Pathogens},
volume = {13},
number = {8},
abstract = {The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.},
keywords = {},
pubstate = {published},
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}