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Benchmarking undedicated cloud computing providers for analysis of genomic datasets

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posted on 2023-05-18, 05:56 authored by Yazar, S, Gooden, GEC, David MackeyDavid Mackey, Alexander HewittAlexander Hewitt
A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

History

Publication title

PLoS ONE

Volume

9

Issue

9

Article number

e108490

Number

e108490

Pagination

1-5

ISSN

1932-6203

Department/School

Tasmanian School of Medicine

Publisher

Public Library of Science

Place of publication

United States

Rights statement

Licensed under Creative Commons Attribution 3.0 Unported (CC BY 3.0) http://creativecommons.org/licenses/by/3.0/

Repository Status

  • Open

Socio-economic Objectives

Clinical health not elsewhere classified

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