The University of Bologna node contributes to ELIXIR at both the national and the European level, linking local research and service development to the broader ELIXIR Italy and ELIXIR Europe scientific programme.
ELIXIR Italy
ELIXIRxNextGenIT
The node participates in ELIXIRxNextGenIT, the PNRR-funded project consolidating the Italian infrastructure for omics data and bioinformatics within ELIXIR Italy, contributing predictive bioinformatics services, data resources and expertise in variant interpretation and protein modelling to the national infrastructure.
ELIXIR Europe — Platforms
Tools Platform
Active contribution through the registration, maintenance and benchmarking of predictors (e.g. BUSCA, DeepSig, CoCoNat, PhD-SNP family) in bio.tools and in community benchmarking efforts coordinated with the Tools Platform, including leading the FAIRification of the 3D-BioInfo PPI Benchmark and Single Cell Omics BE1 benchmark datasets — see Benchmark FAIRification.
Data Platform
Contribution to data interoperability and resource development, supporting the construction of a shared Rare Disease Research Ecosystem of interoperable, accessible and usable data resources and tools together with the Tools and Data Platforms.
ELIXIR Europe — Communities
Rare Disease Community
The node co-leads activities within the Rare Disease Community and contributes to the AMP4RD commissioned service, supporting the Rare Disease implementation study with predictive variant-effect and protein-structure methods, alongside participation in the Community's 2026–2028 roadmap actions, the HDTR work programme, and the ENIGMA and BioChef initiatives.
Federated Human Data Community
Active contribution to the development of interoperable, federated approaches for human genomic and phenotypic data, complementing the node's work on rare disease variant interpretation.
ELIXIR Europe — Focus Groups
AI Ecosystem Focus Group
Participation in the AI Ecosystem Focus Group, contributing deep-learning expertise in protein and variant prediction to ELIXIR's cross-platform effort to connect AI tools, data and infrastructure across the ELIXIR community.
Summary
| Level | Activity | Role |
|---|---|---|
| ELIXIR Italy | ELIXIRxNextGenIT | Participant – predictive bioinformatics & data services |
| ELIXIR Europe | Tools Platform | Active contributor – tool registration & benchmarking |
| ELIXIR Europe | Data Platform | Active contributor – ecosystem & interoperability building |
| ELIXIR Europe | Rare Disease Community | Co-lead / active contributor, incl. AMP4RD commissioned service |
| ELIXIR Europe | Federated Human Data Community | Active contributor |
| ELIXIR Europe | AI Ecosystem Focus Group | Active contributor |
Publications — ELIXIR collaborations
Selected publications co-authored by node members in the context of ELIXIR-wide collaborations (names in bold are current members of the node's core team):
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Farrell G, Adamidi E, Andrade Buono R, et al. (incl. Capriotti E) (2026). Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences. Nature Methods. DOI:10.1038/s41592-026-03037-6
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Attafi OA, Clementel D, Kyritsis K, et al. (incl. Capriotti E, Savojardo C) (2024). DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology. GigaScience 13. DOI:10.1093/gigascience/giae094
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Schweke H, Xu Q, Tauriello G, et al. (incl. Savojardo C, Martelli PL, Casadio R) (2023). Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 23(17):e2200323. DOI:10.1002/pmic.202200323 PMID: 37365936; PMCID: PMC10937251.
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Licata L, Via A, Turina P, et al. (incl. Martelli PL, Savojardo C) (2023). Resources and tools for rare disease variant interpretation. Frontiers in Molecular Biosciences 10. DOI:10.3389/fmolb.2023.1169109 *corresponding author: Capriotti E.
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Walsh I, Fishman D, Garcia-Gasulla D, et al. (incl. Capriotti E, Martelli PL, Savojardo C) (2021). DOME: recommendations for supervised machine learning validation in biology. Nature Methods 18. DOI:10.1038/s41592-021-01205-4

