TY - JOUR
T1 - The human toxome collaboratorium
T2 - A shared environment for multi-omic computational collaboration within a consortium
AU - Fasani, Rick A.
AU - Livi, Carolina B.
AU - Choudhury, Dipanwita R.
AU - Kleensang, Andre
AU - Bouhifd, Mounir
AU - Pendse, Salil N.
AU - McMullen, Patrick D.
AU - Andersen, Melvin E.
AU - Hartung, Thomas
AU - Rosenberg, Michael
N1 - Publisher Copyright:
© 2016 Fasani, Livi, Choudhury, Kleensang, Bouhifd, Pendse, McMullen, Andersen, Hartung and Rosenberg.
PY - 2015
Y1 - 2015
N2 - The Human Toxome Project is part of a long-term vision to modernize toxicity testing for the 21st century. In the initial phase of the project, a consortium of six academic, commercial, and government organizations has partnered to map pathways of toxicity, using endocrine disruption as a model hazard. Experimental data is generated at multiple sites, and analyzed using a range of computational tools. While effectively gathering, managing, and analyzing the data for high-content experiments is a challenge in its own right, doing so for a growing number of -omics technologies, with larger data sets, across multiple institutions complicates the process. Interestingly, one of the most difficult, ongoing challenges has been the computational collaboration between the geographically separate institutions. Existing solutions cannot handle the growing heterogeneous data, provide a computational environment for consistent analysis, accommodate different workflows, and adapt to the constantly evolving methods and goals of a research project. To meet the needs of the project, we have created and managed The Human Toxome Collaboratorium, a shared computational environment hosted on third-party cloud services. The Collaboratorium provides a familiar virtual desktop, with a mix of commercial, open-source, and custom-built applications. It shares some of the challenges of traditional information technology, but with unique and unexpected constraints that emerge from the cloud. Here we describe the problems we faced, the current architecture of the solution, an example of its use, the major lessons we learned, and the future potential of the concept. In particular, the Collaboratorium represents a novel distribution method that could increase the reproducibility and reusability of results from similar large, multi-omic studies.
AB - The Human Toxome Project is part of a long-term vision to modernize toxicity testing for the 21st century. In the initial phase of the project, a consortium of six academic, commercial, and government organizations has partnered to map pathways of toxicity, using endocrine disruption as a model hazard. Experimental data is generated at multiple sites, and analyzed using a range of computational tools. While effectively gathering, managing, and analyzing the data for high-content experiments is a challenge in its own right, doing so for a growing number of -omics technologies, with larger data sets, across multiple institutions complicates the process. Interestingly, one of the most difficult, ongoing challenges has been the computational collaboration between the geographically separate institutions. Existing solutions cannot handle the growing heterogeneous data, provide a computational environment for consistent analysis, accommodate different workflows, and adapt to the constantly evolving methods and goals of a research project. To meet the needs of the project, we have created and managed The Human Toxome Collaboratorium, a shared computational environment hosted on third-party cloud services. The Collaboratorium provides a familiar virtual desktop, with a mix of commercial, open-source, and custom-built applications. It shares some of the challenges of traditional information technology, but with unique and unexpected constraints that emerge from the cloud. Here we describe the problems we faced, the current architecture of the solution, an example of its use, the major lessons we learned, and the future potential of the concept. In particular, the Collaboratorium represents a novel distribution method that could increase the reproducibility and reusability of results from similar large, multi-omic studies.
KW - Big data
KW - Cloud computing
KW - Computational toxicology
KW - Systems toxicology
KW - Virtual machines
KW - Virtualization
UR - http://www.scopus.com/inward/record.url?scp=84997170963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997170963&partnerID=8YFLogxK
U2 - 10.3389/fphar.2015.00322
DO - 10.3389/fphar.2015.00322
M3 - Article
C2 - 26924983
AN - SCOPUS:84997170963
SN - 1663-9812
VL - 6
JO - Frontiers in Pharmacology
JF - Frontiers in Pharmacology
M1 - 322
ER -