A hybrid cloud read aligner based on MinHash and kmer voting that preserves privacy.
A hybrid cloud read aligner based on MinHash and kmer voting that preserves privacy.
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DOI：
10.1038/ncomms15311
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发表时间：
2017-05-16
影响因子：
16.6
通讯作者：
Batzoglou S
中科院分区：
文献类型：
--
作者：
Popic V;Batzoglou S
低成本的云可以减轻基因组测序数据爆炸的计算和存储负担。然而，将个人基因组数据分析转移到云端可能会引发严重的隐私问题。在这里，我们设计了一种基于位置敏感散列和kmer投票的混合云隐私保护读映射器Balaur。Balaur可以安全地将相当大一部分计算外包给公共云，同时在精确度和速度方面与非私有的、最先进的短读取数据读校准器具有很强的竞争力。我们还表明，在长读映射方面，该方法的速度明显快于现有技术。因此，Balaur可以使处理海量基因组数据集的机构能够将部分分析转移到云中，而不会牺牲准确性或将敏感信息暴露给不受信任的第三方。将计算外包给基因组数据处理提供了按需分配大量计算能力和存储空间的能力。在这里，Ptopic和Batzoglou开发了一种用于序列读取映射的混合云对齐器，它以具有竞争力的精度和速度保护隐私。
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. Outsourcing computation for genomic data processing offers the ability to allocate massive computing power and storage on demand. Here, Popic and Batzoglou develop a hybrid cloud aligner for sequence read mapping that preserves privacy with competitive accuracy and speed.