IVEA: an integrative variational Bayesian inference method for predicting enhancer-gene regulatory interactions
Yasumasa Kimura1,2,3, Yoshimasa Ono1, Kotoe Katayama2
1DX Drug Discovery Department, Daiichi Sankyo RD Novare Co., Ltd., Edogawa-ku, Tokyo 134-8630, Japan.
Bioinformatics Advances
|August 28, 2024
Summary
We developed IVEA, a computational method to predict enhancer-gene interactions by estimating gene promoter and enhancer activities. This approach accurately identifies biologically relevant regulatory relationships, advancing our understanding of transcriptional control.
Area of Science:
- Genomics
- Molecular Biology
- Computational Biology
Background:
- Enhancers are crucial for cell-type-specific gene transcription.
- Identifying enhancer-gene regulatory relationships is a significant challenge in genomics.
- Computational methods are essential for accurate inference of these interactions.
Purpose of the Study:
- To propose a novel computational method, IVEA, for predicting enhancer-gene regulatory interactions.
- To estimate promoter and enhancer activities based on transcriptional bursting mechanisms.
- To calculate the contribution of enhancer-promoter pairs to target gene transcription.
Main Methods:
- Developed the IVEA method utilizing variational Bayesian inference.
- Integrated transcriptional readouts, chromatin accessibility, and chromatin contact data.
- Modeled gene regulation based on transcriptional bursting (burst size and frequency).
Main Results:
- The IVEA method achieves high prediction accuracy for enhancer-gene interactions.
- Identified biologically relevant enhancer-gene regulatory relationships.
- Demonstrated the effectiveness of estimating promoter and enhancer activities.
Conclusions:
- IVEA provides an accurate computational approach to infer enhancer-gene regulatory relationships.
- The method leverages transcriptional bursting principles for robust predictions.
- IVEA contributes to a deeper understanding of gene regulation.
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