# Uncovering the epitranscriptome regulatory codes using machine learning

> **NIH NIH SC3** · UNIVERSITY OF TEXAS SAN ANTONIO · 2021 · $112,500

## Abstract

Project Summary
N6-methyladenosine (m6A) is the most abundant methylation widely found in mRNAs of mammalian cells whose
function is largely unknown. Recent research has accumulated increasingly strong evidence of m6A's
involvement in different diseases such as leukemia, breast cancer, lung cancer, and AIDs. While m6A's close
involvement in many diseases is apparent, mechanistic evidence linking m6A alterations to disease phenotypes
is mostly missing. Most of the recent research is fueled by the high throughput sequencing technologies such as
MeRIP-seq for transcriptome-wide profiling of m6A methylation. However, due to the innate limitations of such
technologies, sophisticated machine learning based algorithms are urgently needed to address the problem of
detecting m6A sites with high sensitivity and precision, accurate quantification of m6A methylation levels and the
prediction of m6A sites differentially affected under disease and normal conditions and the prediction of genes
whose expression levels are regulated by m6A. Without bridging these knowledge gaps, it is impossible to made
inroads to the problem of finding m6A's role in regulating diseases. To address these issues, our aims in this
proposal are 1) Establish a deep learning algorithm for base-resolution m6A site prediction; 2) Establish base-
resolution m6A differential site prediction using a hierarchical Bayesian approach; and 3) Determine m6A-
mediated genes and functions by Bayesian Negative-Binomial regression. The proposed research will employ
deep learning and adversarial learned inference methods and utilize both methylation quantification and
sequence information for the first time. Also, it will employ Bayesian graphical model-based methods for
combining sequence and methylation level information. It is expected that the developed algorithms will have
broad applications in functional study, for which we plan to closely work with our collaborators in applying these
algorithms in their research of Kaposi's sarcoma-associated herpesvirus (KSHV), which will lead to the fulfillment
our long term goal in the eventual validation and practical medical application of m6A research in the future.

## Key facts

- **NIH application ID:** 10246787
- **Project number:** 5SC3GM136594-02
- **Recipient organization:** UNIVERSITY OF TEXAS SAN ANTONIO
- **Principal Investigator:** Jianqiu Zhang
- **Activity code:** SC3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $112,500
- **Award type:** 5
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246787

## Citation

> US National Institutes of Health, RePORTER application 10246787, Uncovering the epitranscriptome regulatory codes using machine learning (5SC3GM136594-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246787. Licensed CC0.

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