AltraBio combines over 20 years of expertise in bioinformatics, biostatistics, and systems biology to analyze complex biological datasets. Based in Lyon, France, and operating globally, we support biopharma, dermo-cosmetics, and clinical researchers in transcriptomics, proteomics, epigenomics, and multi-omics integration. Whether for target discovery, mechanism-of-action deciphering, or biomarker identification, we align our analytical pipelines with your biological endpoints.
Comprehensive Omics Analytics: From Raw Sequences to Biological Discovery
Bioinformatics & Biostatistical Core Expertise
Experimental design validation, batch-effect correction, normalization, and outlier detection (RNA-Seq, proteomics) to ensure data integrity before statistical modeling.
Complex experimental designs involving multiple variables (donor, cell type, treatment, time points). We build statistical models adapted to your specific R&D questions.
Leveraged AI and machine learning techniques to cross-analyze omics, cytometry, and clinical datasets for predictive molecular signatures.
Omics Analysis Capabilities by Modality
Identification of regulated genes, alternative splicing, cell-type heterogeneity, and spatial gene regulation. Extended services: Access to NGS data generation via trusted European platform partnerships.
Protein quantification, differential expression, and post-translational modification analysis for target identification and biomarker validation.
Identification of SNPs, insertions/deletions, and structural mutations linked to phenotypic traits or drug responses.
DNA methylation profiling and chromatin accessibility analysis (ATAC-Seq) to decipher gene regulation in disease models and aging.
Systems biology approaches combining transcriptomics, proteomics, and metabolomics datasets into cohesive biological networks.
Biological Interpretation & Functional Annotation
Gene Ontology (GO), KEGG, and Reactome pathway enrichment analysis to identify key biological processes.
Moving beyond raw gene lists to provide mechanistic insights, drug target validation, and testable biological hypotheses.
Interactive Tools & Deliverables
Comprehensive documentation featuring high-resolution visualizations (volcano plots, heatmaps, PCA) and clear biological conclusions.
Dynamic exploration of your statistical results (enrichment, pathway maps, clustering) through our proprietary web interface WikiBioPath.
Two Decades of Data Science Applied to Life Sciences
Bridging the gap between raw sequencing data and actionable biological mechanisms.
« 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. »
Accelerate Your Omics Research Today
Discuss your project requirements with our bioinformatics team.
Peer-Reviewed Publications in Omics & Systems Biology
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},
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}
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.},
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pubstate = {published},
tppubtype = {article}
}
Suveena, S.; Rekha, Akhiya Anilkumar; Rani, J. R.; Oommen, Oommen V.; Ramakrishnan, Reshmi
The translational impact of bioinformatics on traditional wet lab techniques Book Chapter
In: Advances in Pharmacology, pp. 287–311, Elsevier, 2025, ISBN: 9780443346491.
@inbook{Suveena2025,
title = {The translational impact of bioinformatics on traditional wet lab techniques},
author = {S. Suveena and Akhiya Anilkumar Rekha and J.R. Rani and Oommen V. Oommen and Reshmi Ramakrishnan},
doi = {10.1016/bs.apha.2025.01.012},
isbn = {9780443346491},
year = {2025},
date = {2025-00-00},
urldate = {2025-00-00},
booktitle = {Advances in Pharmacology},
pages = {287--311},
publisher = {Elsevier},
abstract = {Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.},
keywords = {},
pubstate = {published},
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}