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🧬 Gene Variant Pathogenicity Predictor

An analysis application that allows users to simulate single-nucleotide variations (SNVs) in genes and predict whether they are “likely benign” or “likely pathogenic”, using the Evo2 LLM Model.

🚀 Overview

This project consists of:

  • A modified Evo2 LLM tokenizer for efficient inference with SNVs.
  • A Next.js frontend to display gene information, allow input of nucleotide changes, and view predictions.
  • Deployment to a Modal container, bundling the Evo2 model with custom tweaks.

🧪 Functionality

  • View gene details and reference sequences.
  • Introduce a single-nucleotide variation in the sequence.
  • Predict the impact of that mutation using the Evo2 LLM.
  • Classify the result as:
    • Likely Benign
    • Likely Pathogenic

🛠️ Modifications

After cloning the original Evo2 Repository, a small change was made in the tokenizer when trying to run in the Modal Container (It is recommended to use H100 GPUs):

# File: vortex/model/tokenizer.py (under CharLevelTokenizer)

def tokenize(self, text: str):
    return list(np.frombuffer(text.encode("utf-8"), dtype=np.uint8))

The updated Evo2 directory is added to the Modal container with (as mentioned under main.py):

.add_local_dir("evo2", remote_path="/evo2", ignore=["*.venv", "*.ipynb"], copy=True)

🧱 Tech Stack

  • Next.js with ShadCN – frontend UI for sequence interaction

  • Python (with Modal) – model backend for Evo2 inference

  • Evo2 LLM – large language model for protein/nucleotide analysis

  • NumPy, Pandas, FastAPI, Matplotlib – backend utility libraries

  • Scikit-learn - for evaluation metrics

📦 Setup & Usage

  1. Clone and install dependencies (from their respective directories)

    cd backend/
pip install -r requirements.txt
    cd frontend/
npm install

  2. Modify the Evo2 tokenizer (already included if using this repo)

  3. Run locally (Modal / Backend):

    cd backend/
modal init 
modal run main.py

  4. Start the Next.js frontend:

    cd frontend/
npm run dev