Benchmarking undedicated cloud computing providers for analysis of genomic datasets

Seyhan Yazar; George E C Gooden; David A Mackey; Alex W Hewitt

doi:10.1371/journal.pone.0108490

Benchmarking undedicated cloud computing providers for analysis of genomic datasets

PLoS One. 2014 Sep 23;9(9):e108490. doi: 10.1371/journal.pone.0108490. eCollection 2014.

Authors

Seyhan Yazar¹, George E C Gooden¹, David A Mackey², Alex W Hewitt³

Affiliations

¹ Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, Western Australia, Australia.
² Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, Western Australia, Australia; School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia.
³ Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, Western Australia, Australia; School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia; Centre for Eye Research Australia, University of Melbourne, Department of Ophthalmology, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia.

Abstract

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.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Benchmarking* / economics
Cloud Computing* / economics
Cost-Benefit Analysis
Datasets as Topic*
Escherichia coli / genetics
Genome
Genome, Human
Humans
Male
Polymorphism, Single Nucleotide
Sequence Alignment*

Grants and funding

This work was supported by funding from the BrightFocus Foundation, the Ophthalmic Research Institute of Australia and a Ramaciotti Establishment Grant. CERA receives operational infrastructure support from the Victorian government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.