Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.
BACKGROUND
As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it.


METHODS
Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required.


RESULTS
Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample.


CONCLUSIONS
Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Allison P
AP
Heath
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Matthew
M
Greenway
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Raymond
R
Powell
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Jonathan
J
Spring
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Rafael
R
Suarez
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
David
D
Hanley
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA.
Chai
C
Bandlamudi
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA Department of Pathology, University of Chicago, Chicago, Illinois, USA.
Megan E
ME
McNerney
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA Computation Institute, University of Chicago, Chicago, Illinois, USA Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
Kevin P
KP
White
Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA Computation Institute, University of Chicago, Chicago, Illinois, USA Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago Illinois, USA.
Robert L
RL
Grossman
D003199:Computer Systems; D066264:Datasets as Topic; D023281:Genomics; D006801:Humans; D020407:Internet; D010641:Phenotype; D012984:Software; D018511:Systems Integration
biomedical clouds; cloud computing; genomic clouds