GeroInformatics Core

CORE OVERVIEW

The GeroInformatics Core provides support in statistics and bioinformatics for data analysis. The Core is experienced in the processing and analysis of data from microarrays, ChIP-seq, RNA-seq, and genomic sequencing and has Ingenuity Pathway Analysis licensed. More importantly, the Core has novel software, developed by Dr. Wren, to assist researchers in discovering and interpreting biological changes that accompany aging. These software and bioinformatics tools allow investigators to analyze data they have gathered as well as discover new genes and genomic regions relevant to aging using predictive methods. The core will also provide the following services using software developed by Dr. Wren’s group.

Bioinformatics & Statistical Analysis

The Discovery BioInformatics Core has a Ph.D.-level statistician (Dr. Constantin Georgescu) to assist with statistical issues, and we also have experience working with next-generation sequencing data (e.g., RNA-seq, CHiP-seq).  We have also developed software to identify genomic commonalities when given a set of genomic regions (Dozmorov et al., 2012).

Mining & Analysis of Literature Networks

We have developed large-scale text-mining software that has processed all 26 million MEDLINE records to create a weighted network of linked entities, e.g., genes, diseases, phenotypes, chemicals, lipids, metabolites, aging-related concepts, etc. (Wren et al., 2004; Wren & Garner, 2004). An example of how associations can be identified that are implied from published relationships is shown in the figure where we originally used our programs to search for drugs without any published relationship with cardiac hypertrophy, yet were predicted to affect cardiac hypertrophy based on the published relationships they shared (i.e., implicit relationships). Chlorpromazine was the top candidate identified, and when tested experimentally in a mouse model of isoproterenol-induced cardiac hypertrophy, it was found to significantly reduce cardiac hypertrophy (Wren, 2004).

 

References

Wren, J.D., Bekeredjian, R., Stewart, J.A., Shohet, R.V., and Garner, H.R. (2004). Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 20, 389-398.

 

Wren, J.D. and Garner, H.R. (2004), Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network. Bioinformatics  20,191-198.

 

Wren, J.D. (2004). Extending the mutual information measure to rank inferred literature relationships. BMC bioinformatics  5,145.

 


Gene Function Prediction

About 31% of human gene names do not appear in MEDLINE, nor do approximately 90% of human non-coding RNAs. And existing literature is heavily skewed towards well-known genes with 75% of effort focusing on 10% of genes (Edwards et al., 2011). We can predict what individual genes do and find genes related to phenotypes/diseases of interest (e.g., synaptic transmission, sarcopenia). We have developed software that analyzes over 75,000 human microarray experiments to identify genes correlated across heterogeneous conditions (Dozmorov & Wren, 2011a,b; Wren, 2009) that can then be linked back to what they have in common in MEDLINE (Figure). Phenotype prediction has been 85% accurate in wet-lab experiments (48 positive out of 56 tested) and several formerly uncharacterized genes successfully validated (Towner et al, 2013a; Towner et al., 2013b; Clemmensen et al., 2012; Lupu et al., 2011; Daum et al., 2009).

Detection of Transcriptional Correlation Modules

By analyzing gene-gene correlations in prior experiments, we can identify how many of your differentially expressed genes are normally regulated together and how many may be unique to your particular experimental question.

Gene Expression Changes with Age

We are working on meta-analytic approaches to extract the age of patients from GEO descriptions and then statistically control for as many co-variates as possible to determine whether or not a gene increases or decreases with age. As with any meta-analytic approach, it is not a single trend but the weight of evidence that we attempt to assess. Shown here is an analysis of how CD248 changes with age in human peripheral blood, which was identified in two studies on gene expression changes with age as being decreased in both. As the figure shows, in 10 different large studies that record age with their control samples, after controlling for gender and microarray platform, CD248 consistently decreases with age.

Core Leaders

Jonathan D. Wren, Ph.D.

GIC Lead

jonathan-wren@omrf.org

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of Excellence in Aging Research

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