基于局部敏感哈希和MapReduce的可扩展蛋白质序列相似性搜索
Scalable Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce
摘要：
宏基因组学是通过对环境中的微生物群进行遗传取样来研究环境的。宏基因组学研究产生的大型数据集估计以比可用计算能力更快的速度增长。宏基因组数据研究中的一个关键步骤是序列相似性搜索，这在大数据集上是计算密集型的。诸如BLAST之类的工具需要大型专用计算基础设施来执行此类分析，并且可能不适用于每个研究人员。在本文中，我们提出了一种称为ScalLoPS的新方法，该方法使用MapReduce分布式框架实现的LSH（局部敏感哈希）对蛋白质序列数据集进行搜索。ScalLoPS旨在跨来自云计算提供商的计算资源进行扩展。我们提出了ScalLoPS的设计和实现，然后用来自传统和宏基因组研究的数据集进行评估。实验结果表明，该方法在提高蛋白质序列搜索可扩展性的同时，近似于BLAST结果的质量。
英文摘要：
Metagenomics is the study of environments through genetic sampling of their microbiota. Metagenomic studies produce large datasets that are estimated to grow at a faster rate than the available computational capacity. A key step in the study of metagenome data is sequence similarity searching which is computationally intensive over large datasets. Tools such as BLAST require large dedicated computing infrastructure to perform such analysis and may not be available to every researcher. In this paper, we propose a novel approach called ScalLoPS that performs searching on protein sequence datasets using LSH (Locality-Sensitive Hashing) that is implemented using the MapReduce distributed framework. ScalLoPS is designed to scale across computing resources sourced from cloud computing providers. We present the design and implementation of ScalLoPS followed by evaluation with datasets derived from both traditional as well as metagenomic studies. Our experiments show that with this method approximates the quality of BLAST results while improving the scalability of protein sequence search.
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