Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud

Enis Afgan; Clare Sloggett; Nuwan Goonasekera; Igor Makunin; Derek Benson; Mark Crowe; Simon Gladman; Yousef Kowsar; Michael Pheasant; Ron Horst; Andrew Lonie

doi:10.1371/journal.pone.0140829

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud

PLoS One. 2015 Oct 26;10(10):e0140829. doi: 10.1371/journal.pone.0140829. eCollection 2015.

Authors

Affiliations

¹ Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia; Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America; Centre for Computing and Informatics (CIR), Rudjer Boskovic Institute (RBI), Zagreb, Croatia.
² Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia.
³ Research Computing Centre, University of Queensland, Brisbane, Queensland, Australia.
⁴ Queensland Facility for Advanced Bioinformatics (QFAB), University of Queensland, Brisbane, Queensland, Australia.

Abstract

Background: Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise.

Results: We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic.

Conclusions: This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

Publication types

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

MeSH terms

Animals
Cloud Computing*
Computational Biology / methods*
Databases, Genetic
Genomics / methods*
Humans
Software
User-Computer Interface*

Grants and funding

This work was supported by a grant from The National eResearch Collaboration Tools and Resources project (NeCTAR; http://nectar.org.au). NeCTAR is an Australian Government Super Science project, financed by the Education Investment Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript..