Evidence map›Paper›PMID 41766646›Full record

ArticleBriefings in bioinformatics2026

EpGAT: integrating epigenetics and 3D genome structure to predict alternative splicing and polyadenylation.

Sudipto Baul, Naima Ahmed Fahmi, Guangyu Wang, Hao Zheng, Ahmed Louri, Jeongsik Yong, Wei Zhang

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sudipto BaulDepartment of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States.
Naima Ahmed FahmiDepartment of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States.
Guangyu WangHouston Methodist Research Institute, Weill Cornell Medical College, 1840 Dynamic Wy, Houston, TX 77030, United States.
Hao ZhengDepartment of Electrical and Computer Engineering, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States.
Ahmed LouriDepartment of Electrical and Computer Engineering, George Washington University, 800 22nd Street NW, Washington, DC 20052, United States.
Jeongsik YongDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, 420 Washington Ave SE, Minneapolis, MN 55455, United States.
Wei ZhangDepartment of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States.

Funding

National Science Foundation NSF-III2152030National Science Foundation NSF-III2246796
6 · The paper itself

Abstract

Understanding how the 3D structure of the genome influences gene regulation is a growing area of interest, particularly in the context of alternative post-transcriptional regulatory events such as alternative splicing (AS) and alternative polyadenylation (APA). These processes are essential for generating transcript and protein diversity, and they are tightly coordinated with transcription. However, despite their biological importance, the relationship between chromatin interactions and alternative pre-messenger RNA regulation remains poorly understood. This gap largely stems from a lack of computational tools capable of integrating structural genomic data with RNA processing dynamics. Exploring how chromatin interactions and epigenetic landscapes shape these events is essential for uncovering the multilayered regulation of gene expression. To bridge this gap, we present EpGAT, a graph attention network-based model that integrates epigenetic read coverage and chromatin interaction data to predict and quantify AS and APA events. By explicitly modeling the spatial organization of the genome, EpGAT captures the regulatory influence of chromatin looping and long-range genomic interactions on RNA processing. The model's predictions are validated through rigorous cross-cell line and cross-chromosome evaluations, affirming its generalizability and reliability. Beyond prediction, EpGAT offers interpretability by tracing learned parameters back to genomic features, enabling the identification of active enhancers, mapping promoter-enhancer connectivity, and pinpointing the epigenetic factors most critical to specific RNA processing events. These capabilities make EpGAT a powerful tool for dissecting the complex interplay between genome architecture and transcriptomic regulation. More broadly, it provides a generalizable framework for multiple tasks to study the link between 3D genome organization, epigenetic signals, and RNA processing.

Indexed as

Alternative SplicingEpigenesis, GeneticEpigenomicsPolyadenylationChromatinGraph Neural NetworksHumansChromatinalternative polyadenylationalternative splicingattention coefficientschromatin interactionepigeneticsgraph attention networkpromoter–enhancer interaction

Identifiers

PMID41766646
PMCPMC12951080

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.