This article is part of the supplement: Selected articles from the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) 2011

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

Uday S Evani1, Danny Challis1, Jin Yu1, Andrew R Jackson23, Sameer Paithankar23, Matthew N Bainbridge1, Adinarayana Jakkamsetti1, Peter Pham1, Cristian Coarfa23, Aleksandar Milosavljevic23* and Fuli Yu13*

Author affiliations

1 The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA

2 Bioinformatics Research Laboratory, Epigenome Center, Department of Molecular and Human Genetics, Baylor College of Medicine, TX 77030, USA

3 Department of Molecular and Human Genetics, Baylor College of Medicine, TX 77030, USA

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Citation and License

BMC Genomics 2012, 13(Suppl 6):S19  doi:10.1186/1471-2164-13-S6-S19

Published: 26 October 2012



Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues.


We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set.


We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.