Content area

Abstract

This paper outlines the technical and organizational measures implemented by the Italian supercomputing center, CINECA, to efficiently collect, process, and store sensitive-omics data in compliance with GDPR regulations. Indeed, the explosion of High Throughput Sequencing in medicine has raised tremendous opportunities for large-scale genomic data analysis. Cohort studies involving the processing of hundreds or thousands of input samples, combined with the integration of diverse diagnostic data, enable researchers to conduct integrative analyses at an unprecedented level of detail would have been impossible to achieve through single sample studies. To analyse such amount of data, centres that have access to High Performance Computing or extensive cloud resources have become crucial both for storage and efficient execution of data analysis pipelines. Nevertheless, since genomic data are considered sensitive personal data according to the EU General Data Protection Regulation, computational centres with high resource capabilities must prioritize data security and protection. This solution has been successfully applied to the Network for Italian Genomes use-case, demonstrating scalability to other hospitals and universities involved in research projects dealing with sensitive genomic data.

Details

1009240
Business indexing term
Title
A GDPR-compliant solution for analysis of large-scale genomics datasets on HPC cloud infrastructure
Publication title
Volume
12
Issue
1
Pages
31
Publication year
2025
Publication date
Feb 2025
Publisher
Springer Nature B.V.
Place of publication
Heidelberg
Country of publication
Netherlands
e-ISSN
21961115
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-02-09
Milestone dates
2024-12-14 (Registration); 2023-09-22 (Received); 2024-12-14 (Accepted)
Publication history
 
 
   First posting date
09 Feb 2025
ProQuest document ID
3164863206
Document URL
https://www.proquest.com/scholarly-journals/gdpr-compliant-solution-analysis-large-scale/docview/3164863206/se-2?accountid=208611
Copyright
Copyright Springer Nature B.V. Feb 2025
Last updated
2025-11-14
Database
ProQuest One Academic