Functional genomics data: privacy risk assessment and technological mitigation (Nov, 10.1038/s41576-021-00428-7, 2021)

被引:0
作者
Guersoy, Gamze
Li, Tianxiao
Liu, Susanna
Ni, Eric
Brannon, Charlotte M.
Gerstein, Mark B.
机构
[1] Yale University,Computational Biology and Bioinformatics Program
[2] Yale University,Molecular Biophysics and Biochemistry
[3] Yale University,Molecular, Cellular, and Developmental Biology
[4] Yale University,Statistics and Data Science
[5] Yale University,Computer Science
[6] Stanford University,Department of Biology
基金
美国国家卫生研究院;
关键词
D O I
10.1038/s41576-021-00440-x
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:259 / 259
页数:1
相关论文
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[1]  
Gursoy G, 2022, NAT REV GENET, V23, P245, DOI 10.1038/s41576-021-00428-7