Date of Award:
12-2024
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Plants, Soils, and Climate
Committee Chair(s)
Rakesh Kaundal
Committee
Rakesh Kaundal
Committee
Jeanette M. Norton
Committee
Yoshimitsu Chikamoto
Committee
Zachariah Gompert
Committee
Curtis Dyreson
Abstract
In agriculture, multi-omics can aid in addressing the growing demand for food amidst an increasing global population. One area of agricultural research that is particularly important is plant-microbe interactions. Plants and microbes interact in many ways, some beneficial to plants, helping them grow and thrive. Other microbes can be harmful, causing diseases. Understanding these interactions could help us to develop disease-resilient crops. One of the most important technologies in genomics is Next-Generation Sequencing (NGS). NGS allows us to sequence DNA and RNA quickly and study complex biological mechanisms on a genome-wide scale and across different species. The cost of sequencing is decreasing rapidly, and we are now generating enormous amounts of sequence data. However, analyzing this data remains a challenge. We developed tools to assist researchers in analyzing complex biological data on plant-microbe interactions, enabling a deeper understanding of the relationships between plants and microbes. Researchers can use these tools to analyze gene expression, identify microRNAs and enzymes, and predict the location of proteins in the cells. In addition to these tools, we also developed a user-friendly comprehensive database of protein-protein interactions between cereal crops and microbes, facilitating the study of plant-microbe interactions. The tools and databases developed in this project will serve as valuable resources for the biological research community. By offering deeper insights into plant-microbe interactions, these tools will support researchers in advancing future crop improvement efforts.
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Recommended Citation
Duhan, Naveen, "Machine Learning and Data Mining in Complex Genomics Big Data: Developing Efficient Tools to Advance Computational Systems Biology" (2024). All Graduate Theses and Dissertations, Fall 2023 to Present. 386.
https://digitalcommons.usu.edu/etd2023/386
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