Bioinformatics For Immunology

Characterize cell types, immune repertoires and mechanisms of immune evasion at the highest resolution.

Research into our complex and dynamic defense system benefits from the latest developments in genomic measurement technologies.

What are the key cell types that bring about an immune response? How do they develop? How do they malfunction in autoimmune diseases? And how can the immune system be leveraged to treat cancer? We apply advanced computational analyses to your high-throughput data to help answer questions like these.

Learn more about our expertise and some of the typical computational analyses in immunology, immunobiology and immuno-oncology below. If you would like to benefit from our bioinformatics support, leave us a message!

Leave us a short description of your bioinformatics needs and we will be in touch very soon!

Discover cell types

Studying the composition and functions of the immune system often relies on transcriptomic and epigenomic sequencing.

We analyze RNA-sequencing, single-cell RNA-sequencing and single-cell ATAC sequencing data to

  • identify, quantify and compare the types of immune cells in across developmental stages, niches and other conditions,
  • characterize the intracellular pathways of immune cells in response to stimulation, and
  • identify interactions between cells of the immune system and those between immune cells and other tissues.

Characterize immune repertoires

More targeted immune sequencing approaches can be used to study the repertoire of lymphocyte receptors across conditions or before vs after infection or vaccination. Such data is commonly produced in conjunction with single-cell RNA-seq, enabling associating gene expression to clonal identities.

Uncover host-pathogen interactions

RNA-sequencing of immune cells from pathogen-infected models, in vivo or vitro, allows for characterizing the mechanisms in which the various cell types of the immune system react to antigens, rely information, and neutralize pathogens.

Study tumor immune evasion

In cancer research, (single-cell) RNA-sequencing of tumors enables characterizing their immune microenvironments. Identifying tumor-infiltrating immune cells helps studying the mechanisms and therapeutic opportunities of immune evasion.

Tumor RNA- or DNA-sequencing data can also be used to identify potential neo-antigens for cancer vaccine development.

Learn more

All analyses

References and case studies

All references

Selected publications from our customers

  • Mezheyeuski, A. et al. (2023). An immune score reflecting pro- and anti-tumoural balance of tumour microenvironment has major prognostic impact and predicts immunotherapy response in solid cancers. EBioMedicine, 88, 104452. Advance online publication.
  • Tusup, M. et al. (2022). Epitranscriptomics modifier *** indirectly triggers Toll-like receptor 3 and can enhance immune infiltration in tumors. Molecular therapy : the journal of the American Society of Gene Therapy, 30(3), 1163–1170.
  • Cramer, M. et al. (2022). Transcriptomic Regulation of Macrophages by Matrix-Bound Nanovesicle-Associated Interleukin-33. Tissue engineering. Part A, 28(19-20), 867–878.
  • Ribeiro, R. et al. (2022). Synchronous Epidermodysplasia Verruciformis and Intraepithelial Lesion of the Vulva is Caused by Coinfection with α-HPV and β-HPV Genotypes and Facilitated by Mutations in Cell-Mediated Immunity Genes. Preprint at
  • Wullt, B. et al. (2021). Immunomodulation-A Molecular Solution to Treating Patients with Severe Bladder Pain Syndrome?. European urology open science, 31, 49–58.
  • Åvall-Jääskeläinen, S. et al. (2021). Genomic Analysis of Staphylococcus aureus Isolates Associated With Peracute Non-gangrenous or Gangrenous Mastitis and Comparison With Other Mastitis-Associated Staphylococcus aureus Isolates. Frontiers in microbiology, 12, 688819.
  • Madonna, G. et al. (2021). Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy. Cancers, 13(16), 4164.
  • Gurvich, O. L. et al. (2020). Transcriptomics uncovers substantial variability associated with alterations in manufacturing processes of macrophage cell therapy products. Scientific reports, 10(1), 14049.
  • Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599.

Selected publications from our team

  • Saralahti, A. K. et al. (2023). Characterization of the innate immune response to Streptococcus pneumoniae infection in zebrafish. PLoS genetics, 19(1), e1010586. Advance online publication.
  • Armaka, M. et al. (2022). Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis. Genome medicine, 14(1), 78.
  • Papadopoulou, A. et al. (2022). Robust SARS-COV-2-specific T-cell immune memory persists long-term in immunocompetent individuals post BNT162b2 double shot. Heliyon, 8(7), e09863.
  • Pellegrinelli, V. et al. (2022). Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance. Nature metabolism, 4(4), 476–494.
  • Detsika, M. G., et al. (2022) Upregulation of CD55 complement regulator in distinct PBMC subpopulations of COVID-19 patients is associated with suppression of interferon responses. bioRxiv 2022.10.07.510750; doi:
  • Zannikou, M. et al. (2021). MAP3K8 Regulates Cox-2-Mediated Prostaglandin E2 Production in the Lung and Suppresses Pulmonary Inflammation and Fibrosis. Journal of immunology (Baltimore, Md. : 1950), 206(3), 607–620.
  • Rajamäki, K. et al. (2021). Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease-Associated Colorectal Cancer. Gastroenterology, 161(2), 592–607.
  • Cerqueira, J. et al. (2021). Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes. Journal of human genetics, 66(6), 613–623.
  • Loppi, S. et al. (2021). Peripheral inflammation preceeding ischemia impairs neuronal survival through mechanisms involving miR-127 in aged animals. Aging cell, 20(1), e13287.
  • Jacome Sanz, D. et al. (2021). Proprotein convertase subtilisin/kexin type 9 regulates the production of acute-phase reactants from the liver. Liver international : official journal of the International Association for the Study of the Liver, 41(10), 2511–2522.
  • Lu, Y. et al. (2020). Interleukin-33 Signaling Controls the Development of Iron-Recycling Macrophages. Immunity, 52(5), 782–793.e5.
  • Harjula, S. E. et al. (2020). Characterization of immune response against Mycobacterium marinum infection in the main hematopoietic organ of adult zebrafish (Danio rerio). Developmental and comparative immunology, 103, 103523.
  • Lindfors, K. et al. (2020). Metagenomics of the faecal virome indicate a cumulative effect of enterovirus and gluten amount on the risk of coeliac disease autoimmunity in genetically at risk children: the TEDDY study. Gut, 69(8), 1416–1422.
  • Mehtonen, J. et al. (2020). Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome medicine, 12(1), 99.
  • Harjula, S. E. et al. (2020). Characterization of immune response against Mycobacterium marinum infection in the main hematopoietic organ of adult zebrafish (Danio rerio). Developmental and comparative immunology, 103, 103523.
  • Morianos, I. et al. (2020). Activin-A limits Th17 pathogenicity and autoimmune neuroinflammation via CD39 and CD73 ectonucleotidases and Hif1-α-dependent pathways. Proceedings of the National Academy of Sciences of the United States of America, 117(22), 12269–12280.
  • Dufva, O. et al. (2020). Immunogenomic Landscape of Hematological Malignancies. Cancer cell, 38(3), 380–399.e13.
  • Lehtipuro, S. et al. (2019). Modes of immunosuppression in glioblastoma microenvironment. Oncotarget, 10(9), 920–921.
  • Luoto, S. et al. (2018). Computational Characterization of Suppressive Immune Microenvironments in Glioblastoma. Cancer research, 78(19), 5574–5585.
  • Harjula, S. E. et al. (2018). Interleukin 10 mutant zebrafish have an enhanced interferon gamma response and improved survival against a Mycobacterium marinum infection. Scientific reports, 8(1), 10360.
  • Havunen, R. et al. (2018). Abscopal Effect in Non-injected Tumors Achieved with Cytokine-Armed Oncolytic Adenovirus. Molecular therapy oncolytics, 11, 109–121.
  • Semitekolou, M. et al. (2018). Dendritic cells conditioned by activin A-induced regulatory T cells exhibit enhanced tolerogenic properties and protect against experimental asthma. The Journal of allergy and clinical immunology, 141(2), 671–684.e7.
  • Georgolopoulos, G. et al. (2019). Unbiased phenotypic identification of functionally distinct hematopoietic progenitors. Journal of biological research (Thessalonike, Greece), 26, 4.
  • Tousa, S. et al. (2017). Activin-A co-opts IRF4 and AhR signaling to induce human regulatory T cells that restrain asthmatic responses. Proceedings of the National Academy of Sciences of the United States of America, 114(14), E2891–E2900.
  • Heinäniemi, M. et al. (2016). Transcription-coupled genetic instability marks acute lymphoblastic leukemia structural variation hotspots. eLife, 5, e13087.

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Antti Ylipää
Antti Ylipää CEO, co-founder Genevia Technologies Oy +358 40 747 7672