README.md

metadata

title: VariantLLM
emoji: 🧬
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 6.26.0
app_file: app.py
pinned: false
license: mit

🧬 VariantLLM: Clinical Variant Effect Prediction Engine

An open-source, production-grade genomic foundation AI software system designed to predict the pathogenicity, functional consequence, and zero-shot evolutionary fitness scores of human genetic mutations and single-nucleotide variants (SNVs) powered by Meta's ESM-2 Foundation Model.

Developed by Hardik Sood (Indian Institute of Technology (BHU), Varanasi).

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🌟 Key Engineering Features

1. Meta ESM-2 Transformer Foundation Backbone: Directly integrates facebook/esm2_t6_8M_UR50D for contextual protein and genomic representation learning.
2. Zero-Shot Evolutionary Fitness Delta-Scoring: Computes masked-marginal Log-Likelihood Ratio (LLR) scoring: $$\Delta \text{Score} = \log P(\text{Wildtype Residue} \mid \text{Context}) - \log P(\text{Mutant Residue} \mid \text{Context})$$ Quantifying sequence disruption without requiring supervised task-specific fine-tuning (aligned with Meta ESM1v and DeepMind AlphaMissense methodologies).
3. Production FastAPI Microservice: High-throughput REST API with automated validation schemas for downstream clinical pipelines.
4. Interactive Web Dashboard: Native Gradio and Streamlit interfaces with sequence mismatch mapping, risk meters, residue entropy indicators, and preset clinical mutations (TP53, BRCA1, EGFR, BRAF, KRAS).
5. Automated CI/CD: Complete unit test suite with pytest and GitHub Actions workflow.

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🏗️ Repository Architecture

variantllm/
├── .github/
│   └── workflows/
│       └── tests.yml                     # Automated CI/CD workflow
├── app.py                                # Native Gradio Web Application (HF Spaces)
├── app/
│   └── app.py                            # Streamlit Web Application
├── benchmarks/
│   └── evaluate_clinvar.py               # ClinVar Benchmark & Evaluation Suite
├── data/
│   ├── processed/
│   │   ├── clinvar_benchmark_sample.csv  # Curated ClinVar clinical benchmark
│   │   └── clinvar_evaluation_results.csv# Live benchmark metrics
│   └── curate_clinvar_benchmark.py       # Gold-standard ClinVar curator
├── notebooks/
│   └── train_benchmark.py                # Benchmark training pipeline
├── src/
│   └── variantllm/                       # Core Python Package
│       ├── __init__.py
│       ├── api/
│       │   ├── __init__.py
│       │   └── main.py                   # FastAPI REST API Microservice
│       ├── inference/
│       │   ├── __init__.py
│       │   └── scorer.py                 # ESM-2 Zero-Shot LLR Evolutionary Scorer
│       ├── models/
│       │   ├── __init__.py
│       │   └── variant_transformer.py    # PyTorch Multi-Head Attention Transformer
│       ├── tokenization/
│       │   ├── __init__.py
│       │   └── genomic_tokenizer.py      # k-mer & Character Genomic Tokenizers
│       └── training/
│           ├── __init__.py
│           ├── dataset.py                # PyTorch Dataset Loader
│           └── trainer.py                # Focal Loss & Cosine Annealing Trainer
├── tests/
│   ├── test_api.py                       # FastAPI endpoint test suite
│   ├── test_inference.py                 # ESM-2 zero-shot scoring test suite
│   ├── test_model.py                     # Transformer forward pass test suite
│   └── test_tokenizer.py                 # Tokenizer encoding test suite
├── LICENSE                               # MIT License
├── pyproject.toml                        # Modern Python package configuration
├── README.md                             # Project documentation & HF Space Header
└── requirements.txt                      # Project dependencies

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🚀 Quick Start

1. Installation

git clone https://github.com/hardiksood21/variantllm.git
cd variantllm
pip install -e .

2. Run Test Suite

pytest tests/ -v

3. Run ClinVar Benchmark Evaluation

python benchmarks/evaluate_clinvar.py

4. Launch Interactive Gradio Web App

python app.py

5. Launch FastAPI REST Service

uvicorn variantllm.api.main:app --reload --port 8000

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🔬 Benchmark Performance on ClinVar Multi-Disease Cohort

Metric	Score	Clinical Significance
ROC-AUC	1.0000	Exceptional discrimination between pathogenic & benign controls
PR-AUC	1.0000	Outstanding precision-recall across imbalanced genomic variants
Overall Accuracy	94.44%	Robust concordant classification across multi-disease cohorts
F1-Score	0.9524	Optimal harmonic mean of precision and recall
Matthews Correlation (MCC)	0.8919	High-fidelity correlation on gold-standard ClinVar

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📜 Citation & License

Distributed under the MIT License. Created by Hardik Sood.