RNA2Fun

Research Units of the project

Partners

RNA2Fun is implemented by three operational units. Each partner contributes complementary expertise spanning computational RNA structural biology and experimental validation in cellular models.

Università degli Studi di CAMERINO (UNICAM) — Coordinator

The UNICAM team is based at the School of Sciences and Technology of the University of Camerino and includes the Bioshape and Data Science Lab (BDSLab), which develops open source tools available from the Lab GitHub organization.

Team members

Role in RNA2Fun
UNICAM leads WP1 (dataset creation and management) and WP5 (management & dissemination). UNICAM also contributes methods for structural abstraction, comparison, and data analysis in collaboration with UCBM and SAPIENZA across WP2–WP4.


Università “Campus Bio-Medico” di ROMA (UCBM) — Research Unit

The UCBM team is part of the Research Unit of Non Linear Physics and Mathematical Modeling (Engineering Department, Campus Bio-Medico University of Rome).
The group develops theoretical and computational multiscale methods, with strong applications in biological and biomedical systems.

Team members

Role in RNA2Fun
UCBM supports WP1 and leads core activities in WP2 (prediction and benchmarking of RNA secondary structures). UCBM also co-leads WP3 with UNICAM and SAPIENZA to determine reliable secondary structures of pCharme and other lncRNAs of interest.


Università degli Studi di ROMA “La Sapienza” (SAPIENZA) — Research Unit

The SAPIENZA team operates at the Department of Biology and Biotechnology “Charles Darwin” (BBCD, Sapienza University of Rome). Prof. Monica Ballarino leads the ncRNA lab (established in 2012) focused on physiologically and pathologically relevant ncRNAs.

Team members

  • Prof. Monica Ballarino — Research Unit Lead
  • Marco Simula — PhD Student (Genetics and Molecular Biology)
  • Daniele Durante — PhD Student (Genetics and Molecular Biology)

Role in RNA2Fun
SAPIENZA leads the experimental validation in WP4, contributing biological expertise for selecting functionally relevant RNA modules, generating edited hiPSC models, and measuring phenotypic outcomes. SAPIENZA also supports dataset enrichment (WP1) and integrates experimental evidence to refine computational modeling (WP2–WP3).