The National Institutes of Health (NIH) recently awarded Robert W. Williams, PhD, chair of the Department of Genetics, Genomics and Informatics, and Saunak Sen, PhD, professor and chief of Biostatistics in the Department of Preventive Medicine at the University of Tennessee Health Science Center (UTHSC), a $1,920,056 grant for their project titled, “A Unified High Performance Web Service for Systems Genetics and Precision Medicine.”
The proposal is funding the development of a high-performance database and open source software for web-based genetics. The project, called GeneNetwork, will provide researchers with data access and a sophisticated set of online tools used to study genetic differences and to evaluate disease risk in model organisms and human cohorts.
GeneNetwork was launched in 2001 as part of a NIH Human Brain Project grant to UTHSC, and was one of the first websites designed for gene mapping. This new NIH grant supports major upgrades for the software infrastructure for gene mapping and analysis. The system is open source, and both the code and data is available to users. The system enables direct access to experimental data and statistical analysis tools to a wide range of users— from students and teachers to research scientists.
“Our goal has been to develop methods to analyze and integrate massive omics data sets with data on disease risks, all in the context of predictive modeling and more targeted or personalized treatment,” Dr. Williams said. “In the long run, the goal is to enable researchers and even clinicians working in predictive medicine to identify appropriate therapeutics, drugs, lifestyles, etc. for patients.”
“Omic” data is collected using modern high-throughput technologies such as microarrays, sequencers, and mass spectrometers. These technologies have revolutionized biology by providing researchers with comprehensive data on biological entities (“omes”) such as proteins (proteome), metabolic markers (metabolome) or microbes (microbiome). While comprehensive, the sheer volume and complexity of the data present significant challenges for analysis and interpretation. GeneNetwork seeks to solve those challenges.
GeneNetwork will also be a resource for data scientists by providing access to data, software implementations of complex algorithms, and high performance computing. It will be easier to compare algorithms on the same data, and process large complex datasets.
“GeneNetwork will facilitate reproducible research because researchers will have open access to both the data and the software code used to process it,” Dr. Sen said. “Reproducibility is essential to the scientific method, and we are proud to be part of the open science movement.”
Other key members of the GeneNetwork team include Pjotr Prins, PhD, a computer programmer based in the Netherlands, who is responsible for the software architecture; Karl Broman, PhD, a leading statistical geneticist from the University of Wisconsin-Madison, who is contributing to computational methods for genetic analysis of high-throughput data; and Yan Cui, PhD, a computational biologist in the Department of Microbiology, Immunology and Biochemistry at UTHSC, who is contributing to the implementation of Bayesian Networks to GeneNetwork.
Better methods to combine statistical and genetic methods and handle large omics data sets will help researchers predict disease risk and the best methods of treatment. Investigators can access the second generation of the service, GeneNetwork 2, at http://gn2.genenetwork.org. The project is funded by NIH through 2021.