README.md

metadata

title: DeepVRegulome
emoji: 🧬
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: true
license: cc-by-nc-4.0
models:
  - duttaprat/DeepVRegulome
tags:
  - biology
  - bioinformatics
  - genomics
  - regulatory-genomics
  - variant-effect-prediction
  - dnabert
  - transcription-factors
  - splice-sites
short_description: 464 DNABERT models for regulatory variant effect prediction

DeepVRegulome Demo

Interactive regulatory variant-effect prediction using 464 fine-tuned DNABERT models

DeepVRegulome is a deep-learning framework for predicting the functional impact of short genomic variants in non-coding regulatory regions.

The framework uses fine-tuned DNABERT models trained on ENCODE ChIP-seq data to predict transcription-factor binding, histone-mark enrichment, splice-site activity, and allele-specific regulatory effects.

This Hugging Face Space provides a lightweight interactive demonstration of the DeepVRegulome prediction framework.

Model Coverage

DeepVRegulome contains 464 fine-tuned DNABERT models:

• 458 transcription-factor binding models
• 4 histone-modification models
• 1 splice-acceptor model
• 1 splice-donor model

The transcription-factor and histone models were trained using regulatory regions derived from ENCODE ChIP-seq experiments.

Features

Binding Prediction

Select a transcription-factor or histone-mark model and enter a DNA sequence to estimate its predicted binding or regulatory activity.

The interface accepts a standard 301 bp DNA sequence, although shorter or longer sequences may also be processed.

Variant Effect Scoring

Compare reference and alternate DNA sequences to estimate how a genomic variant changes predicted regulatory activity.

The output includes:

• Reference-allele probability
• Alternate-allele probability
• Change in predicted binding
• Log-odds-ratio variant-effect score
• Predicted gain or loss of regulatory activity

Splice-Site Prediction (Added in PyPi package, Will be added in the Space)

The splice-acceptor and splice-donor models can be used to evaluate sequence changes near exon–intron boundaries and identify variants that may alter predicted splice-site activity.

Using the Demo

1. Select Binding Prediction or Variant Effect Scoring.
2. Choose a model from the model menu.
3. Paste the required DNA sequence or sequences.
4. Click Predict.
5. Review the predicted probabilities and variant-effect results.

A random DNA-sequence generator is also available for quickly testing the interface.

Python Package

Install DeepVRegulome from PyPI:

pip install deepvregulome

Links

• Paper: arXiv:2511.09026
• Models: duttaprat/DeepVRegulome
• Code: GitHub
• PyPI: pip install deepvregulome
• Full Web App: deepvregulome.streamlit.app