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Bioinformatics

Bioinformatics turns massive biological data — millions of reads or images — into usable knowledge. With high-throughput sequencing, multi-omic approaches and machine learning, this analytical layer is no longer an appendix : it determines the validity of the conclusions and often frames new hypotheses. Inovarion integrates it end to end, from quality control to statistics.

Inovarion deploys a full range of analysis methods. RNA-seq and single-cell sequencing (single-cell RNA-seq) hold a central place, alongside multi-omic approaches integrating genomics and exome, proteomics, transcriptomics, metabolomics and methylome. Chromatin analysis (ChIP-seq, ATAC-seq) and B-cell receptor repertoire analysis round out the arsenal. Added to these are more specialised techniques: cell deconvolution to estimate the composition of a tissue, spatial transcriptomics (Visium technology), co-expression networks (WGCNA), genome-wide association studies (GWAS) and polygenic risk scores, Mendelian randomisation, epidemiological models (Cox regression, Kaplan-Meier curves) and machine learning.

Principle and workflow

A typical analysis chain runs from read quality control (FastQC), alignment to a reference genome (STAR, Bowtie) and quantification, then, depending on the data: differential expression (DESeq2, edgeR), peak calling (MACS2, SEACR), clustering or interaction networks. Functional enrichment (GSEA, GO ontology, KEGG pathways), visualisation and inferential statistics (models, survival analysis, multiple-testing correction) follow.

Variants and options

The toolbox adapts to the type of data: DESeq2 for robust differential expression, deconvolution to estimate the cell fractions of a bulk profile, multi-omic integration (transcriptome, chromatin, proteome), hierarchical or graph-based clustering for single-cell, protein-protein network analysis, survival biostatistics for clinical cohorts. Reproducibility and traceability — versions, parameters, code — are part of the deliverable.

When and why these approaches

Data analysis intervenes wherever the data exceeds the human eye: extracting a signal from noise, comparing conditions with controlled statistical power, integrating several omic layers, or linking a molecular profile to a clinical outcome. The right tool depends on the question and the structure of the data.

Caution is required at every step. Batch effects, choice of normalisation, multiplicity of tests, overfitting, confusion between correlation and causation: so many pitfalls that silently distort a result. An analysis is only sound if the method is suited to the data and validated — and computational quality never compensates for an inadequate experimental design or an insufficient number of replicates.

Inovarion’s expertise

Inovarion handles the data analysis of its projects, from alignment to statistics — the right tool and the right pipeline being settled case by case, according to the data and the objective.

Several publications attest to this: ChIP-seq / RNA-seq integration and methylation analysis linked hypermethylation and immune escape in adrenocortical carcinoma[11] ; multi-omic integration (RNA-seq, ATAC-seq, CUT&Tag) revealed the reactivation of retroelements and immune pathways in chronic myelomonocytic leukaemia[12] ; single-cell exocytosis analysis, coupled with differential proteomics, shed light on a mechanism of catecholamine hypersecretion in phaeochromocytoma[13]. The same expertise carried susceptibility GWAS and the epidemiology of infections in cystic fibrosis[4], spatial-transcriptomic and deconvolution mapping of epileptogenesis[1] and juvenile dermatomyositis[2], the analysis of the clonal evolution of cancers by exome sequencing, the study of the memory B-cell repertoire after vaccination[9], and the proteogenomic integration of bladder cancer[6]. This diversity reflects a single requirement: making the data speak without over-interpreting it.

Bioinformatics is fundamental: it accompanies almost all sequencing work and naturally extends molecular biology — which produces the data — as well as quantitative imaging. Mastering both the production of the data and its analysis guarantees consistency, traceability and speed. Multi-omic analysis, biostatistics and machine learning: Inovarion puts them to work for its partners, from experimental design to the interpretation of results.

See also: “Bulk” RNA-seq & differential transcriptomics ; Single-cell RNA-seq ; ChIP-seq & epigenomics.

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Representative publications

  1. Dufour et al. Spatiotemporal transcriptomic mapping reveals region-specific glial activation and astrocyte shifts in epileptogenesis beyond the hippocampus. Acta Neuropathol Commun, 2026. PubMed
  2. Tragin et al. Muscle Spatial Transcriptomic Reveals Heterogeneous Profiles in Juvenile Dermatomyositis and Persistence of Abnormal Signature After Remission. Cells, 2025. Record → · PubMed
  3. Forand et al. Long-Term Dystrophin Replacement Therapy in Duchenne Muscular Dystrophy Causes Cardiac Inflammation. JACC Basic Transl Sci, 2025. Record → · PubMed
  4. Lin et al. Genome-wide association study of susceptibility to Pseudomonas aeruginosa infection in cystic fibrosis. Eur Respir J, 2024. Record → · PubMed
  5. Hirsinger et al. Limb connective tissue is organized in a continuum of promiscuous fibroblast identities during development. iScience, 2024. Record → · PubMed
  6. Groeneveld et al. Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors. European Urology, 2024. Record → · PubMed
  7. Deschamps et al. CXCL8 secreted by immature granulocytes inhibits WT hematopoiesis in chronic myelomonocytic leukemia. J Clin Invest, 2024. Record → · PubMed
  8. Shi et al. FGFR3 Mutational Activation Can Induce Luminal-like Papillary Bladder Tumor Formation and Favors a Male Sex Bias. European Urology, 2023. Record → · PubMed
  9. Chappert et al. Human anti-smallpox long-lived memory B cells are defined by dynamic interactions in the splenic niche and long-lasting germinal center imprinting. Immunity, 2022. Record → · PubMed
  10. Agopian et al. GlcNAc is a mast-cell chromatin-remodeling oncometabolite that promotes systemic mastocytosis aggressiveness. Blood, 2021. Record → · PubMed
  11. Kerdivel et al. DNA hypermethylation driven by DNMT1 and DNMT3A favors tumor immune escape contributing to the aggressiveness of adrenocortical carcinoma. Clinical Epigenetics, 2023. PubMed
  12. Hidaoui et al. Targeting heterochromatin eliminates chronic myelomonocytic leukemia malignant stem cells through reactivation of retroelements and immune pathways. Communications Biology, 2024. Record → · PubMed
  13. Houy et al. Dysfunction of calcium-regulated exocytosis at a single-cell level causes catecholamine hypersecretion in patients with pheochromocytoma. Cancer Letters, 2022. Record → · PubMed