impl.py

# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA

import argparse
import json
import sys
from typing import List, Dict, Any

class CRISPRPredictor:
    """
    Simulates off-target prediction for CRISPR/Cas9.
    """
    
    def __init__(self):
        # Mock Genome Database: A small set of sequences that look somewhat like targets
        # In a real app, this would query a Bowtie/BWA index or an API like Cas-OFFinder.
        self.reference_genome = [
            {"chrom": "chr1", "loc": 10500, "seq": "GAGTCCGAGCAGAAGAAGAA", "gene": "TARGET_GENE"}, # Perfect match
            {"chrom": "chr2", "loc": 23040, "seq": "GAGTCCGAGCAGAAGAAGAT", "gene": "OFF_TARGET_1"}, # 1 mismatch
            {"chrom": "chr5", "loc": 50012, "seq": "GAGTCCGAGCAGAAGTTGAA", "gene": "OFF_TARGET_2"}, # 2 mismatches
            {"chrom": "chrX", "loc": 99320, "seq": "TTGTCCGAGCAGAAGAAGAA", "gene": "OFF_TARGET_3"}, # 2 mismatches (5' end)
            {"chrom": "chr8", "loc": 11000, "seq": "AAAAAAAAAAAAAAAAAAAA", "gene": "CONTROL"},      # No match
        ]

    def predict(self, sgRNA: str, pam: str = "NGG") -> Dict[str, Any]:
        sgRNA = sgRNA.upper()
        results = []
        
        for site in self.reference_genome:
            ref_seq = site["seq"]
            mismatches = self._count_mismatches(sgRNA, ref_seq)
            
            # Simple risk logic
            risk = "NONE"
            if mismatches == 0:
                risk = "ON_TARGET"
            elif mismatches <= 2:
                risk = "HIGH"
            elif mismatches <= 4:
                risk = "MEDIUM"
            else:
                risk = "LOW"
            
            if risk != "LOW":
                results.append({
                    "locus": f"{site['chrom']}:{site['loc']}",
                    "sequence": ref_seq,
                    "gene": site["gene"],
                    "mismatches": mismatches,
                    "risk_level": risk
                })
        
        return {
            "query_sequence": sgRNA,
            "pam_constraint": pam,
            "off_targets_found": len(results),
            "predictions": results
        }

    def _count_mismatches(self, seq1: str, seq2: str) -> int:
        if len(seq1) != len(seq2):
            return 99 # Length mismatch
        return sum(1 for a, b in zip(seq1, seq2) if a != b)

def main():
    parser = argparse.ArgumentParser(description="CRISPR Off-Target Predictor")
    parser.add_argument("--sequence", type=str, required=True, help="20nt sgRNA sequence")
    parser.add_argument("--pam", type=str, default="NGG", help="PAM sequence (default: NGG)")
    
    args = parser.parse_args()
    
    predictor = CRISPRPredictor()
    result = predictor.predict(args.sequence, args.pam)
    
    print(json.dumps(result, indent=2))

if __name__ == "__main__":
    from typing import Any # lazy import for typing in main
    main()

__AUTHOR_SIGNATURE__ = "9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE"