Using bioinformatics to probe gut microbial taxonomic data.
School Name
Socastee High School
Grade Level
12th Grade
Presentation Topic
Microbiology
Presentation Type
Non-Mentored
Abstract
This research project investigates a subset (n = 17) of publicly available human gut microbiota metagenomes from the American Gut Project to explore the prevalence of gut bacterial taxa associated with neurological dysfunction. Specifically, the sample metadata category of body mass index (BMI) is examined to determine if gut microbiota associated with neurological dysfunction exhibit differentially partitioned abundance patterns among BMI categories. A bioinformatic pipeline was used to analyze the metagenomic sequence data and to generate comprehensive taxonomic profiles examining both within-sample alpha diversity and between-sample beta diversity. This pipeline begins with data filtering using DADA2 for Illumina amplicon detection and correlation, then analyzing sample composition through PERMANOVA in relation to categorical metadata, and ultimately assessing beta diversity to determine statistical separation between gut microbiota communities of high versus normal BMI individuals. Although a statistical difference was not found between samples of normal and high BMI individuals, but certain bacterial taxa are found in greater abundance within the different BMI categories, meriting further study.
Recommended Citation
Cevasco, Harrison, "Using bioinformatics to probe gut microbial taxonomic data." (2025). South Carolina Junior Academy of Science. 33.
https://scholarexchange.furman.edu/scjas/2025/all/33
Location
PENNY 311
Start Date
4-5-2025 9:30 AM
Presentation Format
Oral Only
Group Project
No
Using bioinformatics to probe gut microbial taxonomic data.
PENNY 311
This research project investigates a subset (n = 17) of publicly available human gut microbiota metagenomes from the American Gut Project to explore the prevalence of gut bacterial taxa associated with neurological dysfunction. Specifically, the sample metadata category of body mass index (BMI) is examined to determine if gut microbiota associated with neurological dysfunction exhibit differentially partitioned abundance patterns among BMI categories. A bioinformatic pipeline was used to analyze the metagenomic sequence data and to generate comprehensive taxonomic profiles examining both within-sample alpha diversity and between-sample beta diversity. This pipeline begins with data filtering using DADA2 for Illumina amplicon detection and correlation, then analyzing sample composition through PERMANOVA in relation to categorical metadata, and ultimately assessing beta diversity to determine statistical separation between gut microbiota communities of high versus normal BMI individuals. Although a statistical difference was not found between samples of normal and high BMI individuals, but certain bacterial taxa are found in greater abundance within the different BMI categories, meriting further study.
