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Scientists say they have developed 2 types of data analysis software that could help genomics researchers identify genetic drivers of disease with greater efficiency and accuracy.
Details on these tools were published in PLOS Computational Biology and Scientific Reports.
The first tool, MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis), projects gene expression data onto a 3-dimensional sphere.
This allows scientists to study hierarchical organizational patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer’s disease.
When 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 first theoretical framework for assessing the statistical significance of multi-set intersections and enables users to compare 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, an associate professor at Icahn School of Medicine at Mount Sinai in New York, New York.
“These tools fill important and unmet needs in genomics,” Dr Zhang said. “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.”
MEGENA and SuperExactTest are available as R packages on Dr Zhang’s website and CRAN (the Comprehensive R Archive Network), a repository of open-source software. 
Photo by Darren Baker
Scientists say they have developed 2 types of data analysis software that could help genomics researchers identify genetic drivers of disease with greater efficiency and accuracy.
Details on these tools were published in PLOS Computational Biology and Scientific Reports.
The first tool, MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis), projects gene expression data onto a 3-dimensional sphere.
This allows scientists to study hierarchical organizational patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer’s disease.
When 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 first theoretical framework for assessing the statistical significance of multi-set intersections and enables users to compare 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, an associate professor at Icahn School of Medicine at Mount Sinai in New York, New York.
“These tools fill important and unmet needs in genomics,” Dr Zhang said. “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.”
MEGENA and SuperExactTest are available as R packages on Dr Zhang’s website and CRAN (the Comprehensive R Archive Network), a repository of open-source software.
Photo by Darren Baker
Scientists say they have developed 2 types of data analysis software that could help genomics researchers identify genetic drivers of disease with greater efficiency and accuracy.
Details on these tools were published in PLOS Computational Biology and Scientific Reports.
The first tool, MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis), projects gene expression data onto a 3-dimensional sphere.
This allows scientists to study hierarchical organizational patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer’s disease.
When 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 first theoretical framework for assessing the statistical significance of multi-set intersections and enables users to compare 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, an associate professor at Icahn School of Medicine at Mount Sinai in New York, New York.
“These tools fill important and unmet needs in genomics,” Dr Zhang said. “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.”
MEGENA and SuperExactTest are available as R packages on Dr Zhang’s website and CRAN (the Comprehensive R Archive Network), a repository of open-source software.