@article {818, title = {A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice.}, journal = {J Transl Med}, volume = {23}, year = {2025}, month = {2025 Jan 20}, pages = {86}, abstract = {

BACKGROUND: Despite the use of Next-Generation Sequencing (NGS) as the gold standard for the diagnosis of rare diseases, its clinical implementation has been challenging, limiting the cost-effectiveness of NGS and the understanding, control and safety essential for decision-making in clinical applications. Here, we describe a personalized NGS-based strategy integrating precision medicine into a public healthcare system and its implementation in the routine diagnosis process during a five-year pilot program.

METHODS: Our approach involved customized probe designs, the generation of virtual panels and the development of a personalized medicine module (PMM) for variant prioritization. This strategy was applied to 6500 individuals including 6267 index patients and 233 NGS-based carrier screenings.

RESULTS: Causative variants were identified in 2061 index patients (average 32.9\%, ranging from 12 to 62\% by condition). Also, 131 autosomal-recessive cases could be partially genetically diagnosed. These results led to over 5000 additional studies including carrier, prenatal and preimplantational tests or pharmacological and gene therapy treatments.

CONCLUSION: This strategy has shown promising improvements in the diagnostic rate, facilitating timely diagnosis and gradually expanding our services portfolio for rare diseases. The steps taken towards the integration of clinical and genomic data are opening new possibilities for conducting both retrospective and prospective healthcare studies. Overall, this study represents a major milestone in the ongoing efforts to improve our understanding and clinical management of rare diseases, a crucial area of medical research and care.

}, keywords = {Algorithms, Female, Genomics, High-Throughput Nucleotide Sequencing, Humans, Precision Medicine, rare diseases}, issn = {1479-5876}, doi = {10.1186/s12967-025-06069-2}, author = {M{\'e}ndez-Vidal, Cristina and Bravo-Gil, Nereida and Perez-Florido, Javier and Marcos-Luque, Irene and Fern{\'a}ndez, Raquel M and Fernandez-Rueda, Jose Luis and Gonz{\'a}lez-del Pozo, Mar{\'\i}a and Mart{\'\i}n-S{\'a}nchez, Marta and Fern{\'a}ndez-Su{\'a}rez, Elena and Mena, Marcela and Carmona, Rosario and Dopazo, Joaquin and Borrego, Salud and Anti{\v n}olo, Guillermo} } @article {795, title = {Visualization of automatically combined disease maps and pathway diagrams for rare diseases.}, journal = {Front Bioinform}, volume = {3}, year = {2023}, month = {2023}, pages = {1101505}, abstract = {

Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

}, issn = {2673-7647}, doi = {10.3389/fbinf.2023.1101505}, author = {Gawron, Piotr and Hoksza, David and Pi{\~n}ero, Janet and Pe{\~n}a-Chilet, Maria and Esteban-Medina, Marina and Fernandez-Rueda, Jose Luis and Colonna, Vincenza and Smula, Ewa and Heirendt, Laurent and Ancien, Fran{\c c}ois and Grou{\`e}s, Valentin and Satagopam, Venkata P and Schneider, Reinhard and Dopazo, Joaquin and Furlong, Laura I and Ostaszewski, Marek} }