Keywords

Enhancer-Promoter Interaction; Hi-C; HiChIP; Prediction Model

Abstract

Experimental mapping of enhancer-promoter interactions (EPIs) is resource-intensive, and current computational prediction methods struggle with intrinsic genomic complexity and reliance on limited training data. To address this bottleneck and provide insights for improved computational methods, this study systematically analyzed chromatin contact datasets to investigate the recurrence and co-occurrence of enhancer-promoter interactions across human samples. Putative interactions were evaluated across two HiChIP datasets comprising 218 total samples and one Hi-C dataset comprising 266 samples to assess recurrence across samples and assess sequencing depth related to unique EPIs. Additionally, a preliminary item-based collaborative filtering recommender model was developed to assess co-occurrence patterns across samples as a predictor of EPIs. The analysis revealed that enhancer-promoter interactions are highly recurrent across samples, and interactions appearing unique to a single sample are largely attributable to limited sequencing depth rather than true sample-specificity. Furthermore, the recommender model demonstrated that co-occurrence across samples is a promising predictive feature, achieving high precision in the model’s top rankings. This study is valuable because it suggests the human genome contains a finite, stable set of EPIs from which cells deploy coordinated co-occurring subsets. This finite nature supports a paradigm shift in computational prediction, allowing future algorithms to restrict their search space to known interactions and evaluate them as functional groups to significantly improve prediction accuracy.

Thesis Completion Year

2026

Thesis Completion Semester

Spring

Thesis Chair

Li, Xiaoman

College

College of Medicine

Department

Burnett School of Biomedical Sciences

Thesis Discipline

Computational Biology

Language

English

Access Status

Open Access

Length of Campus Access

None

Campus Location

Orlando (Main) Campus

Download

Share

COinS
 

Accessibility Statement

This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.

Rights Statement

In Copyright