Author ORCID Identifier

https://orcid.org/0000-0001-9312-9847

Date Available

8-6-2024

Year of Publication

2024

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Agriculture, Food and Environment

Department/School/Program

Plant and Soil Sciences

Advisor

Dr. Carlos M. Rodríguez López

Abstract

Global agricultural faces numerous challenges, such as climate change, resource limitations, novel pests and diseases, increasing costs, and the ever-increasing human population. To tackle these challenges, we need innovative strategies that combine new technologies and data analytics approaches to enhance agricultural output, promote sustainable methods, and optimize resource allocation. The key to this innovation lies in understanding the complex molecular web within plants that governs their growth, defense, and adaptability mechanisms. By mastering this molecular network, we can cultivate crops that are more resilient, sustainable, and suitable for different climatic terrains. Moreover, studying the symbiotic relationship between plants and microorganisms can help us develop unique agricultural strategies that promote higher yield and robustness. In recent times, multi-omics research has gained popularity as it aims to provide a comprehensive understanding of plant molecular biology by combining various fields such as genomics, transcriptomics, metabolomics, proteomics, epigenomics and metagenomics. However, the complex and extensive data generated by these studies can be both advantageous and challenging. To derive meaningful insights from this data, bioinformatics plays a crucial role. Additionally, the increase in open-source bioinformatics software has further fueled this revolution, providing solutions to specific research inquiries. Artificial Intelligence (AI) is a potential game-changer in agriculture. It has already made significant contributions to the industry, but its application on agricultural multi-omics data is less explored. With insightful utilization of AI, we can develop more effective strategies to combat agricultural challenges such as abiotic and biotic stress and even better understand the interactions between plants and microbes. This dissertation revolves around the utilization of these two domains , bioinformatics and AI, and showcases their potential in the realm of agricultural omics.

This thesis presents chromoMap, an open-source bioinformatics tool that offers interactive visualizations of chromosomes and their genomic features. It displays ideograms, allowing comparison across species, and is versatile in its application to any organism with a genome assembly. It is capable of producing both publication-ready, high-resolution, static images as well as web-viewable interactive HTML documents. Additionally, I present the application of chromoMap R to one agricultural model and a JavaScript variant of this tool to another.

Finally, this thesis explores the ability of several machine learning (ML) algorithms, including deep learning methods, to predict the planted cultivar (genotype of both scion and rootstock) of a given vineyard, irrespective of its geographic location, using soil microbiome data.

Collectively, this dissertation describes two major contributions to the field of agricultural omics using the interdisciplinary fields of bioinformatics and AI, hence showcasing their potential in agriculture.

Digital Object Identifier (DOI)

https://doi.org./10.13023/etd.2024.24

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