The Development of a Data Integration Approach for the Analysis of Complex Microbiomes
Advisor Information
Hesham Ali
Location
MBSC 308
Presentation Type
Oral Presentation
Start Date
6-3-2020 12:45 PM
End Date
6-3-2020 2:00 PM
Abstract
The impact of microbiome composition and characteristics on the health of organisms in its environment has been gaining much attention recently. This is mainly due to the advancements of various data collection mechanisms. Variabilities associated with microbiomes, along with properties of various omics data, play a significant role in the health of organisms in their environment. For example, recent research of inflammatory bowel diseases (IBD) has increased our understanding of the role of the microbiome as well as the importance of genetic variances in these diseases. While current studies tend to focus on analyzing one type of data and look for associations between phenotypes and the variability of single dataset, it is critical to have a full view of the pathogenesis of such diseases and have a comprehensive understanding of the relationships between the human host and the gut microbiome. In this study, we explore the use of graph models to integrate a number of different, yet relevant, datasets and overlay multiple types of relationships. We introduce a new data integration model that makes it possible to study whole metagenomic data and incorporate both microbiome data and genetic variants of IBD patients. Early results show that this holistic approach provides a more accurate view of biological systems under study and leads to a better understanding of the complex relationships between microbiomes and organisms in their environment.
The Development of a Data Integration Approach for the Analysis of Complex Microbiomes
MBSC 308
The impact of microbiome composition and characteristics on the health of organisms in its environment has been gaining much attention recently. This is mainly due to the advancements of various data collection mechanisms. Variabilities associated with microbiomes, along with properties of various omics data, play a significant role in the health of organisms in their environment. For example, recent research of inflammatory bowel diseases (IBD) has increased our understanding of the role of the microbiome as well as the importance of genetic variances in these diseases. While current studies tend to focus on analyzing one type of data and look for associations between phenotypes and the variability of single dataset, it is critical to have a full view of the pathogenesis of such diseases and have a comprehensive understanding of the relationships between the human host and the gut microbiome. In this study, we explore the use of graph models to integrate a number of different, yet relevant, datasets and overlay multiple types of relationships. We introduce a new data integration model that makes it possible to study whole metagenomic data and incorporate both microbiome data and genetic variants of IBD patients. Early results show that this holistic approach provides a more accurate view of biological systems under study and leads to a better understanding of the complex relationships between microbiomes and organisms in their environment.