2205 A&B Student Center
Effects of evolutionary pressure on histone modifications
With the advent of next-generation sequencing technologies, a considerable effort has been put into sequencing the epigenomes of different species. The efforts such as “Encode” and “Roadmap” epigenomics projects provide an opportunity to compare epigenomes across species (especially between human and mouse). This study is an effort to understand how different histone modifications vary/co-appear between orthologous regions of the two species. In this work, we have used various measures of orthologous similarity between each pair of orthologous genes and explore how histone modifications are conserved with respect to changes in these similarity measures. These measures of similarity include “codon usage frequency similarity” (CUFS), Ka/Ks ratio and gene expression similarity. Our simulation indicates that evolutionary selection pressure of an orthologous pair (Ka/Ks ratio) is more strongly correlated with its histone modification than any other similarity measure. We also found that genes with low Ka/Ks have more similar histone profiles across species than the ones with high Ka/Ks, suggesting more differential regulation for genes with higher selection pressure.
CE705 CS&E Building
An Interventional Informatics Approach to Development and Evaluation of Population-based Health and Web Technologies
Interventional informatics is the use of health information technology (HIT) which drives evidence-based and evidence-generating practices to inform an improved health delivery system. Current HIT lacks movement towards data-driven infrastructures designed to promote information gathering, sharing, and new knowledge discovery in several areas. This thesis undertakes three specific areas where gaps exist. First, in undertaking quality improvement initiatives aligned with fidelity to program models, a web-based practice exchange was designed, built, tested and launched. Second, a systematic review of eHealth technology instruments for outcomes and evaluation components geared towards patient outcomes was conducted, uncovering gaps in the availability of psychometrically sound measures to evaluate eHealth technologies. Third, a study was conducted to establish a baseline of satisfaction and usability, among medical care providers with the current advance care planning process (ACP) and documentation within the electronic medical record (EMR). This study discovered barriers to use of the EMR to retrieve ACP documents and prioritization areas for improvements to begin.
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.
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).
240 Naka Hall
Contrast mining to discover combinations of genetic factors associated with autism subgroups
Autism is characterized by a complex set of behavioral, social, and cognitive deficits. Extensive variation of these phenotypes suggests the existence of autism subtypes that likely have distinct genetic etiologies. The lack of unifying genotypes common to autism patients supports this subtype structure, and suggests that the onset of autism is due to combinations of genetic factors. The ability to precisely diagnose autism subtypes using genetic markers would lead to earlier and more specific treatments and improve outcomes, stressing the need for research which increases our understanding of the genetic etiologies of autism subtypes. In this research, we identify combinations of genetic factors that are associated with groups of autism patients with unifying behavioral profiles, yielding candidate genes to be investigated for their role in the development of these potential autism subtypes. Utilizing methods that combine bioinformatics strategies with data mining practices, we pursue three goals: the discovery of genetic combinations associated to a disease subgroup, the exploration of disease subgroups to find potential subtypes, and the analysis of relationships between genes and subgroups to identify relevant functional interactions.
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