Focus on the group's mission

The Vital-IT group, led by Mark Ibberson, provides bioinformatics expertise to national and international projects and initiatives in collaboration with academic and industry partners. Thanks to combined expertise in computational biology, data management and software engineering, the team develops custom-made solutions to answer complex biological questions. This includes harnessing biomedical and omics data to better diagnose, prevent and treat cancer, diabetes, and other major public health challenges. The value delivered to partners reflects the group’s deep commitment to advancing science effectively and independently.

Annual key figures

22 collaboration and service agreements

20 competitive grants

17 papers published

Partner of choice for large European data-driven projects

The group coordinates data management, analysis, and infrastructure design as a trusted partner in numerous Horizon Europe, ELIXIR and other large-scale European initiatives. These projects, many of which include pharma and biotech partners, often tackle major diseases and are shaping the future of life science data ecosystems. 

Our role spans from acting as Data Coordination Centre to developing innovative solutions to leverage sensitive data and foster the adoption of FAIR principles. Recent projects include: 

  • enabling precision oncology through a secure patient data atlas (IMMUcan; see more);
  • preventing childhood obesity by setting up federated databases and performing multi-omics analyses (Obelisk; see more);
  • advancing the European Open Science Cloud by harmonizing digital preservation and data curation practices and improving data lifecycle management (EOSC EDEN, FIDELIS, and EOSC Data Commons; see more). 

We also coordinate the Swiss node of the Federated European Genome-phenome Archive (FEGA), a secure national archive for human genomics data.

More about SIB’s role in European public-private partnerships

Making life-science data AI friendly through multidisciplinary expertise

Our data management competencies and innovative approaches to securely sharing sensitive data enable us to get novel and faster insights into complex datasets (e.g. RNA-Seq, metabolomics, lipidomics, proteomics, transcriptomics, microbiomics), including patient data. We organize, clean and control the quality of datasets for subsequent analysis. In addition, we process, transform and align datasets to existing standards and make them available through user-friendly interfaces

Our specialties include: 

  • FAIR data management (see case studies)
  • data and literature curation
  • federated technologies (e.g. federated analysis or secure dedicated servers for remote analysis)
  • knowledge representation

Projects include: 

  • connecting and curating vast quantities of heterogenous data on plant metabolic molecules and enabling AI-assisted querying of this knowledge to boost discovery of bioactive components (MetabolinkAI  and other related projects; see more);
  • accelerating the diagnosis and personalizing the management of inherited metabolic diseases through the reconstruction of a comprehensive and accurate human metabolic model, using the SIB-led platform MetaNetX to access, analyze and manipulate genome-scale metabolic networks (Recon4IMD; see more);
  • setting-up federated databases andsupporting tools for securely analysing data from patients sharing similar characteristics, including as part of the pan-European projects Obelisk, to tackle childhood obesity, and  IMI HIPPOCRATES to fight psoriatic arthritis. This capability relies on our strong expertise in data harmonization into FAIR formats and in making databases interoperable.

Fostering health discoveries with cutting-edge computational biology

We propose analysis pipelines adapted to each research context, including multi-omics models and results interpretation. Our specialties include: 

  • omics analysis;
  • data mining;
  • integrative analysis;
  • pathway analysis;
  • machine learning. 

Examples include: 

  • integrative analysis of omics data supported by machine learning in pan-European projects on diabetes, enabling the identification of organ-specific molecular changes tied to higher blood sugar and insulin resistance (IMI RHAPSODY project), and uncovering shared and cell-specific molecular changes tied to insulin resistance, paving the way for targeted therapies (IMI BEAt-DKD project). See more;
  • developing a method for interpreting gene expression data with metabolic models, which has helped to identify key genes in obesity-related inflammation in adipose tissue (see the paper). 

Let’s collaborate!

Enabling data access and insights through user-friendly interfaces and tools

We develop tailored web applications and software tools to present, analyze, visualize and interpret data and results, including from the federated databases we set up. We use various standards as appropriate to ensure interoperability and reusability (e.g., W3C standards such as RDF).

In practice:

  • setting up the secure storage and query infrastructure to support federated databases developed in various projects (see above) to enable research using sensitive datasets (e.g., IHI iCARE4CVD; Obelisk; IMI HIPPOCRATES);
  • maintaining and developing the MetaNetX platform as a leading repository of genome-scale metabolic networks and biochemical pathways.

Access our software stack

Software tools & web applications

Our approach

We strive to establish partnerships: even for one-off collaborations, we seek to deeply understand the data and the objectives of the project we work on. Our partners – from large consortia to individual researchers – appreciate our reliability, commitment and team spirit. 

We are recognized for our flexibility and independence, allowing us to tailor efficient solutions and propose advisory services to each situation. For example, collaborations can be customized to include our partner's own ontologies, provide support to validate internal approaches, or integrate large and complex amounts of data of various types and origins. 

We train both beginners and experts in bioinformatics methods, languages and best practices. 

Ducrest AL, San-Jose LM, Neuenschwander S, Schmid-Siegert E, Simon C, Pagni M, Iseli C, Richter H, Guex N, Cumer T, Beaudoing E, Dupasquier M, Charruau P, Ducouret P, Xenarios I, Goudet J, Roulin A. Melanin and Neurotransmitter Signalling Genes Are Differentially Co-Expressed in Growing Feathers of White and Rufous Barn Owls. Pigment Cell Melanoma Res 2025;38(2):e70001

  1. Bozzi D, Neuenschwander S, Cruz Dávalos DI, Sousa da Mota B, Schroeder H, Moreno-Mayar JV, Allentoft ME, Malaspinas AS. Towards predicting the geographical origin of ancient samples with metagenomic data. Sci Rep 2024;14(1):21794
  2. Cailleau G, Junier T, Paul C, Fatton M, Corona-Ramirez A, Gning O, Beck K, Vidal J, Bürgmann H, Junier P. Temporal and spatial changes in the abundance of antibiotic resistance gene markers in a wastewater treatment plant. Water Environ Res 2024;96(8):e11104
  3. Castillo-Armengol J, Marzetta F, Sanchez-Archidona AR, Fledelius C, Evans M, McNeilly A, McCrimmon RJ, Ibberson M, Thorens B. Correction to: Disrupted hypothalamic transcriptomics and proteomics in a mouse model of type 2 diabetes exposed to recurrent hypoglycaemia. Diabetologia 2024;67(2):403
  4. Decken Ivd, Gutiérrez DR, Sproll P, Opitz L, Stevenson B, Azimi H, Lang-Muritano M, Konrad D, Lenherr-Taube N, Kennedy U, L’Allemand D, Livshits L, Raafat S, Nef S, Biason-Lauber A. Maximizing the Benefits of WES Data for Clinical Diagnosis of individuals with Differences/Variations of Sex Development 2024
  5. Delfin C, Dragan I, Kuznetsov D, Tajes JF, Smit F, Coral DE, Farzaneh A, Haugg A, Hungele A, Niknejad A, Hall C, Jacobs D, Marek D, Fraser DP, Thuillier D, Ahmadizar F, Mehl F, Pattou F, Burdet F, Hawkes G, Arts ICW, Blanch J, Van Soest J, Fernández-Real JM, Boehl J, Fink K, van Greevenbroek MMJ, Kavousi M, Minten M, Prinz N, Ipsen N, Franks PW, Ramos R, Holl RW, Horban S, Duarte-Salles T, Tran VDT, Raverdy V, Leal Y, Lenart A, Pearson E, Sparsø T, Giordano GN, Ioannidis V, Soh K, Frayling TM, Le Roux CW, Ibberson M. A Federated Database for Obesity Research: An IMI-SOPHIA Study. Life (Basel) 2024;14(2):262
  6. Gaudry A, Pagni M, Mehl F, Moretti S, Quiros-Guerrero LM, Cappelletti L, Rutz A, Kaiser M, Marcourt L, Queiroz EF, Ioset JR, Grondin A, David B, Wolfender JL, Allard PM. A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery. ACS Cent Sci 2024;10(3):494-510
  7. Gloyn AL, Ibberson M, Marchetti P, Powers AC, Rorsman P, Sander M, Solimena M. Author Correction: Every islet matters: improving the impact of human islet research. Nat Metab 2024;6(7):1415
  8. Gouy A, Wang X, Kapopoulou A, Neuenschwander S, Schmid E, Excoffier L, Heckel G. Genomes of Microtus Rodents Highlight the Importance of Olfactory and Immune Systems in Their Fast Radiation. Genome Biol Evol 2024;16(11):evae233
  9. Hurcombe JA, Dayalan L, Barrington F, Burdet F, Ni L, Coward JT, Brinkkoetter PT, Holzenberger M, Jeffries A, Oltean S, Welsh GI, Coward RJ. The insulin / IGF axis is critically important controlling gene transcription in the podocyte 2024
  10. Keller F, Denicolò S, Leierer J, Kruus M, Heinzel A, Kammer M, Ju W, Nair V, Burdet F, Ibberson M, Menon R, Otto E, Choi YJ, Pyle L, Ladd P, Bjornstad PM, Eder S, Rosivall L, Mark PB, Wiecek A, Heerspink HJL, Kretzler M, Oberbauer R, Mayer G, Perco P. Association of Urinary Epidermal Growth Factor, Fatty Acid-Binding Protein 3, and Vascular Cell Adhesion Molecule 1 Levels with the Progression of Early Diabetic Kidney Disease. Kidney Blood Press Res 2024;49(1):1013-1025
  11. Lay AC, Tran VDT, Nair V, Betin V, Hurcombe JA, Barrington AF, Pope RJ, Burdet F, Mehl F, Kryvokhyzha D, Ahmad A, Sinton MC, Lewis P, Wilson MC, Menon R, Otto E, Heesom KJ, Ibberson M, Looker HC, Nelson RG, Ju W, Kretzler M, Satchell SC, Gomez MF, Coward RJM, BEAt-DKD consortium. Profiling of insulin-resistant kidney models and human biopsies reveals common and cell-type-specific mechanisms underpinning Diabetic Kidney Disease. Nat Commun 2024;15(1):10018
  12. Li S, Dragan I, Tran VDT, Fung CH, Kuznetsov D, Hansen MK, Beulens JWJ, Hart LM', Slieker RC, Donnelly LA, Gerl MJ, Klose C, Mehl F, Simons K, Elders PJM, Pearson ER, Rutter GA, Ibberson M. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study. Front Endocrinol (Lausanne) 2024;15:1350796
  13. Mehl F, Sánchez-Archidona AR, Meitil I, Gerl M, Cruciani-Guglielmacci C, Wigger L, Le Stunff H, Meneyrol K, Lallement J, Denom J, Klose C, Simons K, Pagni M, Magnan C, Ibberson M, Thorens B. A multiorgan map of metabolic, signaling, and inflammatory pathways that coordinately control fasting glycemia in mice. iScience 2024;27(11):111134
  14. Niarakis A, Laubenbacher R, An G, Ilan Y, Fisher J, Flobak Å, Reiche K, Rodríguez Martínez M, Geris L, Ladeira L, Veschini L, Blinov ML, Messina F, Fonseca LL, Ferreira S, Montagud A, Noël V, Marku M, Tsirvouli E, Torres MM, Harris LA, Sego TJ, Cockrell C, Shick AE, Balci H, Salazar A, Rian K, Hemedan AA, Esteban-Medina M, Staumont B, Hernandez-Vargas E, Martis B S, Madrid-Valiente A, Karampelesis P, Sordo Vieira L, Harlapur P, Kulesza A, Nikaein N, Garira W, Malik Sheriff RS, Thakar J, Tran VDT, Carbonell-Caballero J, Safaei S, Valencia A, Zinovyev A, Glazier JA. Immune digital twins for complex human pathologies: applications, limitations, and challenges. NPJ Syst Biol Appl 2024;10(1):141
  15. Palmieri F, Diserens J, Gresse M, Magnin M, Helle J, Salamin B, Bisanti L, Bernasconi E, Pernot J, Shanmuganathan A, Trompette A, von Garnier C, Junier T, Neuenschwander S, Bindschedler S, Pagni M, Koutsokera A, Ubags N, Junier P. One-Step Soft Agar Enrichment and Isolation of Human Lung Bacteria Inhibiting the Germination of Aspergillus fumigatus Conidia. Microorganisms 2024;12(10):2025
  16. Sempach L, Doll JPK, Limbach V, Marzetta F, Schaub AC, Schneider E, Kettelhack C, Mählmann L, Schweinfurth-Keck N, Ibberson M, Lang UE, Schmidt A. Examining immune-inflammatory mechanisms of probiotic supplementation in depression: secondary findings from a randomized clinical trial. Transl Psychiatry 2024;14(1):305
  17. Slieker RC, Münch M, Donnelly LA, Bouland GA, Dragan I, Kuznetsov D, Elders PJM, Rutter GA, Ibberson M, Pearson ER, 't Hart LM, van de Wiel MA, Beulens JWJ. An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study. Diabetologia 2024;67(5):885-894

Members

View our group members here