A Multi-omics Graph Database System for Microbiome Data Integration in Studying Early Childhood Development
Advisor Information
Hesham Ali
Location
MBSC Dodge Room 302B - G
Presentation Type
Oral Presentation
Start Date
4-3-2022 9:00 AM
End Date
4-3-2022 10:15 AM
Abstract
New biomedical technologies have revolutionized the way of understanding the human microbiome and ignited new research for establishing important associations between microbial species and human health. Numerous communities of microorganisms, or microbiota, reside in and on different parts of the human body and play an important role in shaping our health. Although several studies have revealed strong relationships between the composition of these microbiomes and a variety of clinical conditions, there is now a pressing need for rigorous modeling and analytical tools to take advantage of the available data and advance this line of research. Such tools would allow researchers to further understand the complexity of how microbiomes evolve and contribute to the conditions of the host organisms. In this study, we propose an efficient and flexible graph database model that captures the dynamics of the complex microbial ecological systems and allows researchers to extract valuable biological knowledge. We demonstrate a working prototype of the proposed graph database by modeling the data from early child development study. We show how the developed database model can be used to identify relationships between multi-omics data elements and childhood cognitive development. In addition, we highlight how the proposed approach provide an informatics platform to study different types of microbiomes.
Scheduling Link
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A Multi-omics Graph Database System for Microbiome Data Integration in Studying Early Childhood Development
MBSC Dodge Room 302B - G
New biomedical technologies have revolutionized the way of understanding the human microbiome and ignited new research for establishing important associations between microbial species and human health. Numerous communities of microorganisms, or microbiota, reside in and on different parts of the human body and play an important role in shaping our health. Although several studies have revealed strong relationships between the composition of these microbiomes and a variety of clinical conditions, there is now a pressing need for rigorous modeling and analytical tools to take advantage of the available data and advance this line of research. Such tools would allow researchers to further understand the complexity of how microbiomes evolve and contribute to the conditions of the host organisms. In this study, we propose an efficient and flexible graph database model that captures the dynamics of the complex microbial ecological systems and allows researchers to extract valuable biological knowledge. We demonstrate a working prototype of the proposed graph database by modeling the data from early child development study. We show how the developed database model can be used to identify relationships between multi-omics data elements and childhood cognitive development. In addition, we highlight how the proposed approach provide an informatics platform to study different types of microbiomes.