Omics Working group

SMDA’s Omics working group focuses on enhancing training and fostering collaboration within genomics, proteomics, and metabolomics. Recent advances in sequencing, chromatography, and mass spectrometry have enabled comprehensive measurement of gene expression, protein expression, and metabolite concentrations, significantly disrupting traditional laboratory practices. These new “-Omic” datasets are highly analogous to traditional laboratory methods and data (e.g., RT-PCR, western blot, flow cytometry, TLC, LC-MS). This similarity makes it increasingly important for computational and experimental investigators to work closely together due to their complementary expertise.

For instance, as illustrated in the figure to the right, experimental biologists are very comfortable with the mechanisms underlying the “Central Dogma,” while computational biologists are more familiar with the “normal range” of protein and mRNA expression within a cell. Overall, experimental science provides us with concepts and pathways, while computational science allows us to analyze these pathways and their components at scale. The goal of SMDA is to bring together computational and experimental expertise on these “-Omics” datasets here at UGA.

 

University of Georgia Focus-Areas

A major focus of the SMDA is to better coordinate Omic research across different animal models of disease (see figure below). Traditionally, biomedical research is often siloed based on the animal models used, due to practical variabilities in studying diseases in rodents, dogs, cats, and human patients. At UGA, we possess significant scientific and clinical expertise in all these areas, which we aim to leverage to improve patient outcomes in both veterinary and human medicine. Currently, a key focus of the Omics Working Group is on new single-cell and spatial transcriptomic technologies, which specifically require the combined expertise of both communities.

 

Computational and Experimental Protocols:

In addition to the data itself, understanding the process of generating that data is critical for addressing different scientific questions. These procedures, conducted by experimental investigators, can be highly significant to computational investigators because they may introduce biases and gaps in the dataset due to technical variations in data generation, rather than the underlying biology.

For example, the cellular organization of molecules is often studied through a combination of microscopy and serial centrifugation, which separates different cellular compartments based on their physical properties. Variations in these procedures can directly impact the measurement of nuclei-specific proteins and RNA. On the other hand, Genomic data generation (DNA/RNA) is based on separation by chemical properties through serial extraction techniques, enabling the separation of this material based on their chemical properties. These procedures separate proteins, lipids, DNA, and different types of RNA for downstream analysis.

Proteomic data generation typically relies on the physical separation of proteins by size. Size exclusion chromatography can enrich proteins of a specific size by over 20 times, while affinity purification can concentrate specific proteins by approximately 1000 times. Subsequent mass spectrometry has revealed that the majority of the cellular proteome is actually devoted to housekeeping functions like translation, protein folding, cellular metabolism, and the cytoskeleton. Finally, metabolomics involves chemical separation techniques similar to those used for DNA/RNA and proteins, such as serial extraction and chromatography. This enables the separation of lipids, sugars, amino acids, and nucleic acids, which constitute the majority of small molecules within a cell.

 

 

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Tactical Priorities Recruit Workforce:  with interests in bioinformatics and medicine Train Workforce:  to use emerging tools of bioinformatics data-science artificial intelligence Build Collaborations:  between relevant disciplines clinicians scientists data scientists Automate:   laborious and iterative processes to: increase efficiency of research increase access to advanced computational methods Map Community Datasets: cancer genomics infectious disease genomics immunology

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Strategic Priorities Single-Cell Transcriptomic Technologies: spatial transcriptomics single-cell RNAseq Cross-species analyses infrastructure: rodent models canine models human data Clinical Collaborations: veterinary medicine Industrial Partnerships: post-graduate training doctoral student internships collaborations … …

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Current Members

• Eugene Douglass
• Jonathan Mochel
• Karin Allenspach
• Blake Billmyre
• LeighAnne Clark
• Lok Joshi
• Tatum Mortimer
• Natarajan Kannan
• Shaying Zhao
• Kaixiong Ye
• Lohitash Karumbaia
• Aditya Mishram
• Fabio Santori
• Anthony Ruberto
• Jarrod Call

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Software Tools

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Cleaned Datasets

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Key Performance Indicators: summary statistics

 

KPI Category	KPI	Current Value
Research and Publications	Number of Published Papers
	Impact Factor of Journals
	Citations
	Conference Presentations
Collaboration and Engagement	Interdisciplinary Projects
	External Collaborations
	Collaborative Publications	%
	Workshops and Seminars
Funding and Grants	Research Grants Received	$
	Grant Applications Submitted
	Grant Success Rate	%
Data and Tools	Datasets Published
	Software Tools Developed
	Tool Adoption	downloads
Training and Development	Students Supervised
	Training Programs
	Skill Development	certificates
Impact and Outreach	Societal Impact
	Media Mentions
	Public Engagement	events
Operational Efficiency	Project Completion Rate
	Data Management Practices	% compliance
	Resource Utilization	% efficiency
Innovation and Excellence	Awards and Recognitions
	Innovative Solutions	breakthroughs
Feedback and Improvement	Stakeholder Feedback	satisfaction
	Continuous Improvement	# iterations

 

 

 

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Key Performance Indicators: specific items list

 

KPI List: password protected

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