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Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing

BACKGROUND: 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 potenti...

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Autores principales: Angiuoli, Samuel V., White, James R., Matalka, Malcolm, White, Owen, Fricke, W. Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3197577/
https://www.ncbi.nlm.nih.gov/pubmed/22028928
http://dx.doi.org/10.1371/journal.pone.0026624
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author Angiuoli, Samuel V.
White, James R.
Matalka, Malcolm
White, Owen
Fricke, W. Florian
author_facet Angiuoli, Samuel V.
White, James R.
Matalka, Malcolm
White, Owen
Fricke, W. Florian
author_sort Angiuoli, Samuel V.
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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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spelling pubmed-31975772011-10-25 Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing Angiuoli, Samuel V. White, James R. Matalka, Malcolm White, Owen Fricke, W. Florian PLoS One Research Article BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. Public Library of Science 2011-10-19 /pmc/articles/PMC3197577/ /pubmed/22028928 http://dx.doi.org/10.1371/journal.pone.0026624 Text en Angiuoli et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Angiuoli, Samuel V.
White, James R.
Matalka, Malcolm
White, Owen
Fricke, W. Florian
Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3197577/
https://www.ncbi.nlm.nih.gov/pubmed/22028928
http://dx.doi.org/10.1371/journal.pone.0026624
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