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🧬 cEpiReg-Database

Context-Specific Epigenetic Regulatory Network Database for Alzheimer’s Disease


Live Demo

Database Website: https://bioed.bu.edu/students_24/Team_10/Team-10_database.html

Project Overview Video: Available on the homepage of the live site.

⚠️ Note: This repository contains the source code used for the Boston University deployment. Some asset paths are configured for the BU course server environment.


Project Summary

cEpiReg constructs a comprehensive database elucidating the intricate regulatory mechanisms underlying Alzheimer's Disease (AD) genetics.

By integrating:

  • Single-cell RNA-seq (scRNA-seq)
  • Single-cell ATAC-seq (scATAC-seq)
  • eQTL analysis
  • Public databases (ENCODE, Epimap, GWAS Catalog, UCSC, dbSNP)

we identified and connected:

  • Transcription Factors (TFs)
  • Target Genes (TGs)
  • Cis-Regulatory Elements (CREs)
  • Disease-associated SNPs

This project establishes a regulatory framework linking TF → CRE → Target Gene → Genetic Variant, uncovering hidden regulatory relationships critical for understanding AD pathogenesis.

Done in collaboration with the Hou Lab.


Methods Overview

The cEpiReg framework integrates multiple analytical pipelines:

Gene Regulatory Network Construction

  • Built GRNs using pySCENIC
  • Identified transcription factor → target gene relationships

CRE Identification (ATAC-seq)

  • Linked cis-regulatory elements to genes
  • Cross-referenced with ENCODE and Epimap datasets

eQTL Integration

  • Associated SNPs with genes and CREs
  • Connected regulatory variation to disease genetics

Web-Based Database Implementation

  • Interactive query interface
  • Downloadable data tables (.csv, .txt)
  • External database linking (UCSC, dbSNP)
  • Network visualizations (NetworkX + Plotly)
  • Integrated help documentation

While full multi-layer integration was limited by cell-type and tissue-specific sample constraints, the platform successfully integrates independent regulatory modules into a cohesive database interface.


Key Features

  • Gene and region query functionality
  • Interactive network visualizations
  • Downloadable query results (.csv / .txt)
  • External database integration (UCSC Genome Browser, dbSNP)
  • Heatmaps and CRE visualizations
  • Structured navigation across GRN, CRE Explorer, and eQTL modules
  • Comprehensive help page

Repository Contents

File Description
Team-10_database.html Homepage
GRN-test.html / grn-test.py Gene Regulatory Network module
ATAC.html / ATAC_AJAX.py CRE Explorer module
eQTL-data.html / eqtl.py CRE + eQTL integration module
styles.css / style1.css Website styling
Database_Overview.mp4 Demo video
NR-biorender-cepireg-logo.png Project logo

Team Contributions

Neha Rao

  • Designed homepage, navigation UI, and Help page
  • Developed GRN query system and visualizations
  • Implemented interactive networks using NetworkX + Plotly
  • Enabled downloadable visualizations (.png) and tables (.csv)

Jawahar Mahendran

  • Designed CRE Explorer module
  • Implemented Pie Charts, Data Tables, and IGV integration
  • Enabled .txt download functionality
  • Contributed to Help page revisions

Bhavana Kapalli

  • Designed CRE + eQTL module
  • Integrated UCSC/dbSNP external links
  • Implemented Google Charts heatmap visualization
  • Enabled downloadable .csv outputs
  • Help page design contributions

Conference Proceedings / Abstracts Resulting from This Work

Fu, T. T., Mahendran, J., Kapalli, B., Rao, N., Farrer, L., & Hou, L. (2025). W50. Epigenetic regulatory network in microglia reveals context-dependent trans-regulatory programs in brain disease. European Neuropsychopharmacology, 99, 234.

Fu, T. T., Kapalli, B., Mahendran, J., Rao, N., & Hou, L. (2024). Leveraging context-specific epigenomic regulatory networks (EPINETS) to dissect the genetics of neuropsychiatric disorders. European Neuropsychopharmacology, 87, 8.


Acknowledgments

We extend our heartfelt gratitude to:

  • Dr. Lei Hou
  • Dr. Ting-Ting Fu

for their mentorship, guidance, and expertise throughout the development of the cEpiReg-Database.

The database was developed at Boston University as part of the BF768 course (Spring 2024).


Technologies Used

  • Python
  • pySCENIC
  • NetworkX
  • Plotly (JavaScript)
  • Google Charts
  • HTML / CSS
  • AJAX
  • UCSC Genome Browser integration

About

Context-specific epigenetic regulatory network database integrating single-cell multi-omics to dissect Alzheimer’s disease genetics.

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