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Big Data: Cloud computing in genomics applications

机译：大数据：基因组学应用中的云计算

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Healthcare applications typically require big data management as well as intensive computation. This is especially true with recently developed next generation sequencing technology which increases interests in processing the huge amount of information in a timely fashion. In this paper, we focus on testing whether the healthcare applications can scale well on commercial big data platforms that implement MapReduce framework. We selected short read sequence alignment and assembly workloads in genome analysis workloads, and chose Bowtie, Blast and Contrail-bio which are publically available applications designed to run on the Hadoop MapReduce framework. To speed-up the processes we compressed the intermediate data using various compression schemes the compression schemes are compared. The test results are very promising and indicate that the wide range of genomic analysis workflows can be optimized on MapReduce frameworks with great computational efficiency and scalability.
机译：医疗保健应用通常需要大数据管理以及密集的计算。最近开发了下一代测序技术尤其如此，这增加了在及时加工大量信息的利益。在本文中，我们专注于测试医疗保健应用是否可以在实现MapReduce框架的商业大数据平台上扩展。我们选择了基因组分析工作负载中的短读序列对齐和装配工作负载，并选择了Bowtie，Blast和Contrail-Bio，该Bio是旨在在Hadoop MapReduce框架上运行的公开可用应用程序。为了加速过程，我们使用各种压缩方案来压缩中间数据来比较压缩方案。测试结果非常有前途，并表明各种基因组分析工作流程可以以巨大的计算效率和可扩展性在MapReduce框架上进行优化。

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