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Ontology-based representation and analysis of conditional vaccine immune responses using Omics data
Anthony Huffman1, Edison Ong1, Tim Brunson2
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
CEUR Workshop Proceedings
|November 25, 2024
Summary
An ontology-based pipeline enhances vaccine data analysis, revealing sex-based differences in immune responses to influenza vaccines. This approach aids understanding of vaccine efficacy across diverse conditions.
Area of Science:
- Immunology
- Bioinformatics
- Genomics
Background:
- ImmPort is a large repository of immunology and vaccine immune response data.
- Existing data lacks integrative analysis for understanding vaccine effects.
- Ontological representation can enhance vaccine study analysis.
Purpose of the Study:
- To develop an ontology-based pipeline for analyzing ImmPort vaccine data.
- To investigate the effects of vaccine types and host biological sex on vaccine response gene expression.
- To identify shared and differing gene profiles for influenza vaccines.
Main Methods:
- Utilized Vaccine Ontology (VO) and Vaccine Investigation Ontology (VIO) for data classification and modeling.
- Developed an ontology-based pipeline to extract, annotate, and analyze ImmPort and GEO data.
- Employed VIGET tool for omics data analysis, focusing on gene expression profiles.
Main Results:
- Classified 37 vaccines into bacterial, viral, and protozoan types.
- Identified distinct gene profiles for live and inactivated influenza vaccines.
- Revealed differential host responses to influenza vaccines between sexes, with females showing earlier and males later immune activity.
Conclusions:
- The ontology-based framework effectively integrates and analyzes vaccine data.
- Host biological sex significantly influences immune responses to influenza vaccines.
- The pipeline is expandable for analyzing other vaccine types and categories.
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