Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

<h4>Background</h4>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 h...

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Autores principales:	Samuel V Angiuoli, James R White, Malcolm Matalka, Owen White, W Florian Fricke
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ed3e6931a5a4900925e47768e9c78b7
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spelling	oai:doaj.org-article:5ed3e6931a5a4900925e47768e9c78b72021-11-18T07:36:13ZResources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.1932-620310.1371/journal.pone.0026624https://doaj.org/article/5ed3e6931a5a4900925e47768e9c78b72011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22028928/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.Samuel V AngiuoliJames R WhiteMalcolm MatalkaOwen WhiteW Florian FrickePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 10, p e26624 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samuel V Angiuoli James R White Malcolm Matalka Owen White W Florian Fricke Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
description	<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
format	article
author	Samuel V Angiuoli James R White Malcolm Matalka Owen White W Florian Fricke
author_facet	Samuel V Angiuoli James R White Malcolm Matalka Owen White W Florian Fricke
author_sort	Samuel V Angiuoli
title	Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
title_short	Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
title_full	Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
title_fullStr	Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
title_full_unstemmed	Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
title_sort	resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/5ed3e6931a5a4900925e47768e9c78b7
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Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

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