Scientists from the Icahn School of Medicine at Mount Sinai have developed and publicly released new data analysis software that could help genomics researchers identify genetic drivers of disease with greater efficiency and accuracy. These tools were published yesterday in PLoS Computational Biology and on November 25th in Scientific Reports, a Nature publication.
MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis) projects gene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer's. Tested on data from The Cancer Genome Atlas (TCGA), MEGENA identified novel regulatory targets in breast and lung cancers, outperforming other co-expression analysis methods.
The second tool, SuperExactTest, establishes the very first theoretical framework for assessing the statistical significance of multi-set intersections and enables users to compare very large sets of data, such as gene sets produced from genome-wide association studies (GWAS) and differential expression analysis. Scientists ran SuperExactTest on existing TCGA and GWAS data, identifying a core set of cancer genes and detecting related patterns among complex diseases. Both tools come from the Multiscale Network Modeling Laboratory led by Bin Zhang, PhD, Associate Professor in the Department of Genetics and Genomic Sciences.
"These tools fill important and unmet needs in genomics," said Dr. Zhang, the senior author of the two papers. "MEGENA will help scientists flesh out novel pathways and key targets in complex diseases, while SuperExactTest will provide a clearer understanding of the genome by comparing a large number of gene signatures."
"Our team is dedicated to crafting high-performance analysis tools and to sharing those resources with the broader genomics community to help us all generate the best possible results," said Eric Schadt, PhD, the Jean C. and James W. Crystal Professor of Genomics at the Icahn School of Medicine at Mount Sinai, and Founding Director of the Icahn Institute for Genomics and Multiscale Biology. "These new tools demonstrate thoughtful and creative solutions to computational challenges faced by scientists around the world, and I look forward to seeing what the community will accomplish with them."
Won-Min Song and Bin Zhang. Multiscale Embedded Gene Co-expression Network Analysis. PLoS Computational Biology 11(11): e1004574. doi:10.1371/journal.pcbi.1004574
Minghui Wang, Yongzhong Zhao, and Bin Zhang. Efficient Test and Visualization of Multi-Set Intersections. Scientific Reports 5:16923. doi: 10.1038/srep16923
About the Mount Sinai Health System
The Mount Sinai Health System is an integrated health system committed to providing distinguished care, conducting transformative research, and advancing biomedical education. Structured around seven hospital campuses and a single medical school, the Health System has an extensive ambulatory network and a range of inpatient and outpatient services--from community-based facilities to tertiary and quaternary care.
The System includes approximately 6,100 primary and specialty care physicians; 12 joint-venture ambulatory surgery centers; more than 140 ambulatory practices throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and 31 affiliated community health centers. Physicians are affiliated with the renowned Icahn School of Medicine at Mount Sinai, which is ranked among the highest in the nation in National Institutes of Health funding per investigator. The Mount Sinai Hospital is nationally ranked as one of the top 25 hospitals in 8 specialties in the 2014-2015 "Best Hospitals" issue of U.S. News & World Report. Mount Sinai's Kravis Children's Hospital also is ranked in seven out of ten pediatric specialties by U.S. News & World Report. The New York Eye and Ear Infirmary of Mount Sinai is ranked nationally, while Mount Sinai Beth Israel, Mount Sinai St. Luke's, and Mount Sinai Roosevelt are ranked regionally.
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