RT Journal Article SR Electronic T1 Privacy-Preserving Microbiome Analysis Using Secure Computation JF bioRxiv FD Cold Spring Harbor Laboratory SP 025999 DO 10.1101/025999 A1 Justin Wagner A1 Joseph N. Paulson A1 Xiao-Shaun Wang A1 Bobby Bhattacharjee A1 Héctor Corrada YR 2015 UL http://biorxiv.org/content/early/2015/09/02/025999.abstract AB Motivation Developing targeted therapeutics and identifying biomarkers relies on large amounts of patient data. Beyond human DNA, researchers now investigate the DNA of micro-organisms inhabiting the human body. An individual’s collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving analysis tools does not meet the requirements for microbiome sequencing studies.Results We augment an existing categorization of genomic-privacy attacks to incorporate microbiome sequencing and provide an implementation of metagenomic analyses using secure computation. Our implementation allows researchers to perform analysis over combined data without revealing individual patient attributes. We implement three metagenomic analyses and perform an evaluation on real datasets for comparative analysis. We use our implementation to simulate sharing data between four policy-domains and measure the increase in significant discoveries. Additionally, we describe an application of our implementation to form patient pools of data to allow drug companies to query against and compensate patients for the analysis.Availability The software is freely available for download at: http://cbcb.umd.edu/∼hcorrada/projects/secureseq.html