Relative Scalability of NoSQL Databases for Genotype Data Manipulation

Research initiative, treatment action : RITA Pub Date : 2018-07-17 DOI:10.22456/2175-2745.79334

Arthur Lorenzi, V. Schettino, Thiago Jesus Rodrigues Barbosa, P. F. Freitas, Pedro Gabriel Silva Guimarães, W. Arbex

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引用次数: 1

Abstract

Genotype data manipulation is one of the greatest challenges in bioinformatics and genomics mainly because of high dimensionality and unbalancing characteristics. These peculiarities explains why Relational Database Management Systems (RDBMSs), the "de facto" standard storage solution, have not been presented as the best tools for this kind of data. However, Big Data has been pushing the development of modern database systems that might be able to overcome RDBMSs deficiencies. In this context, we extended our previous works on the evaluation of relative performance among NoSQLs engines from different families, adapting the schema design in order to achieve better performance based on its conclusions, thus being able to store more SNP markers for each individual. Using Yahoo! Cloud Serving Benchmark (YCSB) benchmark framework, we assessed each database system over hypothetical SNP sequences. Results indicate that although Tarantool has the best overall throughput, MongoDB is less impacted by the increase of SNP markers per individual.

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基因型数据操作中NoSQL数据库的相对可扩展性

基因型数据的高维性和不平衡性是生物信息学和基因组学研究面临的最大挑战之一。这些特性解释了为什么关系数据库管理系统(rdbms)——“事实上的”标准存储解决方案——没有作为这类数据的最佳工具出现。然而，大数据一直在推动现代数据库系统的发展，这些系统可能能够克服rdbms的缺陷。在这种情况下，我们扩展了之前关于不同家族nosql引擎之间相对性能评估的工作，根据结论调整模式设计以获得更好的性能，从而能够为每个个体存储更多的SNP标记。使用雅虎云服务基准(YCSB)基准框架，我们在假设的SNP序列上评估每个数据库系统。结果表明，尽管Tarantool具有最佳的总体吞吐量，但MongoDB受单个SNP标记增加的影响较小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research initiative, treatment action : RITA

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