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Functional genomics data: privacy risk assessment and technological mitigation.
Gürsoy, Gamze; Li, Tianxiao; Liu, Susanna; Ni, Eric; Brannon, Charlotte M; Gerstein, Mark B.
Afiliação
  • Gürsoy G; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
  • Li T; Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
  • Liu S; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
  • Ni E; Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA.
  • Brannon CM; Statistics and Data Science, Yale University, New Haven, CT, USA.
  • Gerstein MB; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
Nat Rev Genet ; 23(4): 245-258, 2022 04.
Article em En | MEDLINE | ID: mdl-34759381
ABSTRACT
The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Privacidade / Privacidade Genética Tipo de estudo: Etiology_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Privacidade / Privacidade Genética Tipo de estudo: Etiology_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article