Genetic Mutation Annotator Tool with Pathogenicity Prediction

Abstract

Annotate genetic mutations with functional impact, pathogenicity predictions, and clinical interpretations

Cleaned Submission Note

This revision replaces a raw JSON display with readable Markdown. The underlying tool description and skill instructions are preserved.

Tool Summary

Annotate genetic mutations with functional impact, pathogenicity predictions, and clinical interpretations Mutation Annotation Tool 1.0.0

Input Schema

The original structured input schema is retained conceptually. Use the SKILL section below for executable instructions.

SKILL

SKILL.md - Mutation Annotation Tool

Name

Mutation Annotation Tool

Description

Performs functional annotation of mutations from VCF files, identifying mutation types, affected genes, amino acid changes, and functional impact predictions.

Input

• VCF format file (standard VCF 4.2 format)
• Mutation list (format like "BRCA1:c.68_69delAG" or "TP53:p.G245S")

Steps

Step 1: Parse VCF or Mutation Format

• Identify input format (VCF file or HGVS format)
• VCF format parsing: Extract CHROM, POS, REF, ALT
• HGVS format parsing: Use regex to extract gene name, variant position, variant type

Step 2: Determine Mutation Type

• SNP (Single Nucleotide Polymorphism): REF and ALT have same length and both are 1 base
• InDel (Insertion/Deletion): REF and ALT have different lengths, or contain "ins"/"del"
• Large structural variants: Variants beyond single base range

Step 3: Identify Affected Genes and Transcripts

• Query gene annotation database based on chromosome position
• Use simplified gene position mapping table (built-in data)
• Determine transcript ID and coding region position

Step 4: Predict Amino Acid Changes

• DNA to RNA to amino acid translation
• Identify amino acid substitution, frameshift, nonsense mutation caused by variant
• Calculate protein length change after mutation

Step 5: Predict Functional Impact

• Based on mutation position (domain, critical residue)
• Based on amino acid property changes (polarity, charge, size)
• Prediction classification: Benign / Likely Benign / VUS / Likely Pathogenic / Pathogenic
• Provide confidence score (0-1)

Step 6: Output Annotation Results

• JSON format output
• Contains complete annotation information
• Also output summary table

Output

Mutation annotation table (JSON format), containing the following fields:

• mutation_id: Unique mutation identifier
• gene: Affected gene
• transcript: Transcript ID
• chromosome: Chromosome
• position: Genomic position
• ref_allele: Reference allele
• alt_allele: Alternative allele
• variant_type: Variant type (SNP/InDel/Large Deletion etc.)
• protein_change: Protein change description
• aa_position: Amino acid position
• original_aa: Original amino acid
• substitute_aa: Substituted amino acid
• functional_impact: Functional impact prediction
• pathogenicity_score: Pathogenicity score (0-1)
• interpretation: Interpretation
• tools_used: List of annotation tools used

Tools

• Python 3.8+
• Standard library: re, json, sys
• Built-in gene annotation database (simplified version)

Examples

Input Example

BRCA1:c.68_69delAG
TP53:p.G245S
17:g.41244938C>G

Output Example

{
  "mutations": [
    {
      "mutation_id": "BRCA1_c.68_69delAG",
      "gene": "BRCA1",
      "variant_type": "frameshift_deletion",
      "protein_change": "p.E23Vfs*8",
      "functional_impact": "Pathogenic",
      "pathogenicity_score": 0.95
    }
  ],
  "summary": {
    "total_mutations": 1,
    "pathogenic": 1,
    "benign": 0,
    "vus": 0
  }
}

Integrity Note

This is a formatting cleanup revision. It does not introduce a new scientific claim.