Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
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DOI：
10.1371/journal.pone.0026624
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Fricke WF
中科院分区：
文献类型：
--
作者：
Angiuoli SV;White JR;Matalka M;White O;Fricke WF
基因组应用的广泛普及受到了“生物信息学瓶颈”的威胁，这是由于满足下一代序列分析日益增长的需求所需的成本和基础设施的不确定性造成的。云计算服务已被讨论为潜在的新的生物信息学支持系统，但尚未进行彻底的评估。我们提出了常见微生物基因组学应用的基准成本和运行时间，包括16 S rRNA分析，微生物全基因组鸟枪（WGS）序列组装和注释，WGS宏基因组学和大规模BLAST。选择序列数据集类型和大小以对应于通常由配备有454和Illumina平台的中小型设施生成的输出，除了WGS宏基因组学，其中使用Illumina数据的采样。在CloVR虚拟机中实现的自动化分析管道用于保证不同操作系统之间的透明度，可重复性和可移植性，包括商业Amazon Elastic Compute Cloud（EC2），用于将真实的美元成本附加到每种分析类型。我们发现与不同微生物基因组学应用相关的计算要求、运行时间和成本存在相当大的差异。虽然所有16 S分析在单CPU台式机上完成不到三个小时，但微生物基因组和宏基因组分析在Amazon EC2上使用了多达120个CPU的多CPU支持，每个分析在24小时内完成，不到60美元。代表性的数据集被用来估计不同集群大小的最大数据吞吐量，并比较成本之间的EC2和可比的本地网格服务器。尽管微生物基因组学的生物信息学要求取决于数据集特征和应用的分析协议，但我们的研究结果表明，（最多三台Roche/454或一台Illumina GAIIx测序仪）投资于16 S rRNA扩增子测序，微生物单基因组和宏基因组WGS项目可以实现成本-使用CloVR与Amazon EC2结合作为本地计算中心的替代方案，提供高效的生物信息学支持。
The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.
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影响因子：
12.3
作者：
Dudley JT;Pouliot Y;Chen R;Morgan AA;Butte AJ
通讯作者：
Butte AJ
影响因子：
--
作者：
Galens K;Orvis J;Daugherty S;Creasy HH;Angiuoli S;White O;Wortman J;Mahurkar A;Giglio MG
通讯作者：
Giglio MG
CLOVR：使用云计算从桌面上自动化和便携式序列分析的虚拟机。
影响因子：
3
作者：
Angiuoli SV;Matalka M;Gussman A;Galens K;Vangala M;Riley DR;Arze C;White JR;White O;Fricke WF
通讯作者：
Fricke WF
影响因子：
46.9
作者：
Dudley, Joel T.;Butte, Atul J.
通讯作者：
Butte, Atul J.
影响因子：
14.9
作者：
Lagesen K;Hallin P;Rødland EA;Staerfeldt HH;Rognes T;Ussery DW
通讯作者：
Ussery DW