Enabling Atlas2 personal genome analysis on the cloud

Abstract

Abstract--Until recently, sequencing has primarily been carried out in large genome centers who also invested heavily in developing the computational infrastructure to enable post sequencing analysis. The recent advancements in sequencing technologies have lead to a wide dissemination of sequencing and we are now seeing many sequencing projects being undertaken in small laboratories. However, the limited accessibility to the computational infrastructure and high quality bioinformatic tools needed to enable analysis remains a serious road-block. The cloud computing and Software-as-a-Service (SaaS) technologies can help address this barrier. We deploy the Atlas2 Cloud Pipeline for personal genome analysis via the Genboree Workbench using software-as-a-service model. We report on a successful case study of personal genome analysis using this pipeline. BACKGROUNDThe revolutionary development of massively parallel DNA sequencing has enabled identification of biomedically relevant genomic variants via whole genome [1] or exome resequencing [2]. Information relevant for personalized medicine such as assessment of longitudinal disease risks, and personalized treatment [3] are now within reach. In a few very recent personal genomic studies, results have directly led to targeted treatment and dramatic improve- [4]. These examples are paving the way to soon turn genomic sequencing into a routine diagnostic procedure and to enable personalized medicine. Currently, analysis of sequencing data on a genomic scale requires bioinformatic expertise and access to extensive computational resources, presenting a significant barrier. Most cutting-edge genome analysis applications [5,6] are still limited to a command line interface and require at least moderate informatics expertise to operate. In addition, large scale genomic data analysis requires routine access to a high performance compute cluster. Such requirements are entirely unsuitable for the operational models of smaller research/diagnostic laboratories due to the excessive investment requirements of computing infrastructure and personnel. The deployment of genomic analysis software as a service within a cloud computing framework offers a unique solution for these problems. The concept behind cloud computing is to outsource computation to third-party servers or clusters at a remote location. This allows small laboratories to take advantage of external computational resources without having to maintain an in-house compute cluster. This software as a service model removes the upfront investment requirement and any delays associated with building local computing infrastructure. Earlier solutions involved providing access to analysis pipeline on a large cloud service provider like Amazon EC2 [7]. Though this solution eliminates the need for access to large compute clusters, the user would still need to have considerable bioinformatic skills due to the steep learning curve involved in working with clusters.

Atlas2 Cloud: a framework for personal genome analysis in the cloud

“…Based on “Enabling Atlas2 personal genome analysis on the cloud”, by Uday S Evani, Danny Challis, Jin Yu, Andrew R Jackson, Sameer Paithankar, Matthew N Bainbridge, Cristian Coarfa, Aleksandar Milosavljevic and Fuli Yu which appeared in Genomic Signal Processing and Statistics (GENSIPS), 2011 IEEE International Workshop on. © 2011 IEEE [ 17 ].…”