Abstract
Methylation patterns in bacterial genomes, such as those found in Eggerthella lenta, take roles in mediating microbial interactions with their environment, host, and external stressors. These patterns are formed by DNA methylation with diverse sequence specificities that provide insights into the DNA sequence regulation and defense against bacteriophages. We utilize computational approaches and machine learning models to identify and analyze 5mC and 6mA methylation motifs in E. lenta genomes. By integrating host characteristics such as age, birth country, gender, and medication history, we explore 1) the predictive relationships between 5mC & 6mA methylation types in E. lenta strains and these host factors, as well as 2) how these host factors may affect the presence of specific methylation patterns in the bacterial genome, using computational approaches and machine learning models. This study looks into the associations between host factors, specifically the dietary, medication history, race followed by age, and strain-specific methylation patterns, emphasizing the role of DNA sequence patterns in bacterial adaptation.
Advisor
Cecilia Noecker
Committee Member
Naseef Mansoor
Committee Member
Bukralia Rajeev
Date of Degree
2024
Language
english
Document Type
APP
Degree
Master of Science (MS)
Program of Study
Data Science
Department
Computer Information Science
College
Science, Engineering and Technology
Recommended Citation
Sherbeza, T. T. (2024). Machine learning-based analysis of DNA Methylation patterns in E.lenta [Master’s alternative plan paper, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1469/
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This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
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