A hybrid cloud read aligner based on MinHash and kmer voting that preserves privacy

Nature Communications
Victoria Popic, Serafim Batzoglou

Abstract

Low-cost clouds can alleviate the compute and storage burden of the genome sequencing data explosion. However, moving personal genome data analysis to the cloud can raise serious privacy concerns. Here, we devise a method named Balaur, a privacy preserving read mapper for hybrid clouds based on locality sensitive hashing and kmer voting. Balaur can securely outsource a substantial fraction of the computation to the public cloud, while being highly competitive in accuracy and speed with non-private state-of-the-art read aligners on short read data. We also show that the method is significantly faster than the state of the art in long read mapping. Therefore, Balaur can enable institutions handling massive genomic data sets to shift part of their analysis to the cloud without sacrificing accuracy or exposing sensitive information to an untrusted third party.

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Citations

May 10, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Yongan ZhaoHaixu Tang
May 16, 2018·Bioinformatics·Will P M Rowe, Martyn D Winn
Jul 14, 2020·Bioinformatics·Chirag JainAdam M Phillippy
May 1, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Chirag JainAdam M Phillippy
Jun 15, 2018·PLoS Computational Biology·Vivek Navale, Philip E Bourne
Mar 13, 2020·Genome Biology·Tom MokveldMarcel Reinders
Mar 14, 2020·BMC Bioinformatics·Robin KobusBertil Schmidt
Apr 22, 2021·Journal of Gastroenterology and Hepatology·Tao ZengZhangran Chen
May 23, 2021·Briefings in Bioinformatics·Dandan LuHongmin Cai

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Software Mentioned

Bowtie2
MEM
wgsim
Azure
BWA
Amazon Elastic Compute Cloud
Balaur
SW
kmer
OpenMP

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