Events

Seminar Series

Presenter:

Hung Nguyen

Date:

11-06-2017

Time:

11:00AM-12:00PM

Location:

2206A Student Center

Investigating genome composition in multiple bee species

The honey bee Apis mellifera was the first eusocial animal to have its genome assembled. Analysis of the complete draft sequence of the honey bee genome revealed several interesting features compared with the other metazoan genomes: a low but heterogeneous GC content, an overabundance of CpG dinucleotides and a lack of repetitive elements. The average GC content of the honey bee genome is only 33%, but GC content is highly heterogeneous, ranging from 11% to 67%, with a bimodal distribution. Furthermore, unlike genes in most other metazoans, honey bee genes are overly abundant in regions of low GC content (<30%). Some studies have suggested that the high GC-content regions of the honey bee genome are associated with areas of high meiotic recombination rates; indeed the honey bee exhibits the highest known recombination rate among eukaryotes. Other studies have suggested that honey bee genome nucleotide composition is associated with DNA methylation, which occurs at a low frequency at CpG sites within exons. However, reasons for the highly heterogeneous base composition are not well understood, and whether any of the unusual genome features are related to the emergence of eusociality in bees is not known. Since the publication of the honey bee genome, genomes of several other bee species have become available. I am investigating the composition and organization of genomes of multiple bee species with different levels of social complexity to identify features that are unique to eusocial bees. Results of this exploratory analysis will allow me to develop a hypothesis about the relationship of genome composition to the evolution of eusociality.

Seminar Series

Presenter:

Tim Haithcoat

Date:

10-30-2017

Time:

11:00AM-12:00PM

Location:

2206A Student Center

A Geospatial Health Context Table for Supporting Public Health Research

This project develops a Big Data table that allows researchers to query across and among multiple data sources integrated by location. The big table created in this way uses location as the fundamental linkage between data sets.  This is the power of geospatial analysis and forms the foundation for the development and interaction with the Health Context Table. The approach utilizes a dense point file populated with attribution derived or obtained directly from public data sources and associated geospatial analysis. The database created extends across the entire continental United States comprising over 300 million points. The data table has at its core, functional socio-demographic data that is pre-processed, cleaned, integrated and represented in its spatial context.  To this core, is being added environmental, infrastructure, cultural, physical, as well as geo-analytically derived layers (i.e. remoteness, isolation).  These data span multiple spatial scales (Census Block Group, Zip Code Tabulation Areas, County, etc.).  The interface to this Big Data table will allow a user to visualize, data mine, analyze uncertainty, and perform data analytics on these data. The Geospatial Health Context Table’s goal is to address the gap in health research and application for an underpinned spatial framework upon which real-world issues and research can be addressed in the context of place. This work is supported by the NIH T32 Training grant (5T32LM012410-02).

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