310 lines
8.5 KiB
Markdown
310 lines
8.5 KiB
Markdown
# CRISPR-Cas Analysis Module - Implementation Plan
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## Overview
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This document outlines the planned implementation of CRISPR-Cas system analysis for the BtToxin Pipeline. This feature is **reserved for future development** and provides a roadmap for integrating CRISPR-Cas detection with toxin activity assessment.
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## Status: RESERVED
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All infrastructure is prepared but implementation is **not yet started**. This module will be activated when resources and requirements are finalized.
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---
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## Architecture
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### Directory Structure (to be created)
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```
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crispr_cas/
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├── scripts/
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│ ├── detect_crispr.py # CRISPR array detection
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│ ├── fusion_analysis.py # Spacer-toxin gene analysis
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│ └── crispr_scoring.py # Integration with shoter scoring
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├── docs/
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│ ├── IMPLEMENTATION.md # This file
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│ └── API_REFERENCE.md # Module API documentation (to be created)
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└── tests/
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├── test_detect_crispr.py
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└── test_fusion_analysis.py
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```
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### Data Flow
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```
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Genome (.fna) → CRISPRCasFinder → CRISPR Results (JSON)
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↓
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Fusion Analysis Module
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↓
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Toxin Genes (All_Toxins.txt)
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↓
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Enhanced Shoter Scoring
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↓
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CRISPR-Augmented Activity Scores
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```
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---
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## Implementation Plan
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### Phase 1: CRISPR Detection
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**File:** `crispr_cas/scripts/detect_crispr.py`
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**Tool:** CRISPRCasFinder (https://crisprcas.i2bc.paris-saclay.fr/
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**Tasks:**
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1. Integrate CRISPRCasFinder CLI or implement Python wrapper
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2. Parse CRISPRCasFinder output (General Case or Cas spacer)
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3. Extract:
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- Cas type/subtype (I-E, I-F, II-A, V-A, etc.)
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- CRISPR array positions
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- Spacer sequences
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- Repeat sequences
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- Protospacer Adjacent Motif (PAM) sequences
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**Output Format (JSON):**
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```json
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{
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"strain_id": {
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"cas_type": "I-E",
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"arrays": [
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{
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"position": "contig_1:12345-12678",
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"repeat": "5'-GTTTTAGAGCTATGCTGTTTTGAATGGTCCCAAAAC-3'",
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"spacers": [
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{"sequence": "ATGCGTCGAC", "position": 0},
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{"sequence": "CGTAGCTAGC", "position": 37}
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]
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}
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],
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"summary": {"num_arrays": 3, "num_spacers": 24}
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}
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}
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```
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---
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### Phase 2: Spacer-Toxin Gene Association
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**File:** `crispr_cas/scripts/fusion_analysis.py`
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**Tasks:**
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1. Map CRISPR arrays to genomic positions
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2. Identify toxin genes near CRISPR arrays (within 10kb window)
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3. Analyze potential spacer-target matches:
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- Extract toxin gene sequences
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- Perform BLAST of spacers against toxin genes
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- Identify potential immunity or targeting relationships
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**Output Format (JSON):**
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```json
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{
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"strain_id": {
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"crispr_toxin_associations": [
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{
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"crispr_array": "contig_1:12345-12678",
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"nearby_toxins": ["Cry1Ac1", "Cry2Aa3"],
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"spacer_targets": [
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{"spacer": "ATGCGTCGAC", "target": "Cry1Ac1", "identity": 0.95}
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],
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"distance_to_toxin": 2500
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}
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]
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}
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}
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```
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---
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### Phase 3: Integration with Shoter Scoring
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**File:** Modify `scripts/bttoxin_shoter.py`
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**Reserved Parameters (add to argument parser):**
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```python
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# CRISPR-Cas Integration (Reserved for Future Implementation)
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ap.add_argument("--crispr_weight", type=float, default=0.0,
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help="[FUTURE] Weight for CRISPR-Cas contribution to activity scores (0-1)")
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ap.add_argument("--crispr_results", type=Path, default=None,
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help="[FUTURE] Path to CRISPR-Cas detection results JSON")
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ap.add_argument("--crispr_fusion", action="store_true", default=False,
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help="[FUTURE] Enable spacer-toxin fusion analysis")
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```
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**Scoring Integration (in `score_strain()` function):**
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```python
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# Reserved: CRISPR-Cas scoring integration
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# When CRISPR is enabled, modify strain scores:
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#
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# if args.crispr_weight > 0 and crispr_data:
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# crispr_boost = calculate_crispr_activity_boost(
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# strain=strain,
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# crispr_data=crispr_data.get(strain, {}),
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# toxin_hits=toxin_hits
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# )
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# # Apply CRISPR boost to target order scores
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# for order, score in sscore.scores.items():
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# sscore.scores[order] = score * (1 - args.crispr_weight) + \
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# crispr_boost.get(order, 0) * args.crispr_weight
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```
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---
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### Phase 4: Enhanced Visualization
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**File:** `scripts/plot_shotter.py`
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**Tasks:**
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1. Add CRISPR-Cas panel to existing heatmaps
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2. Visualize:
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- CRISPR array positions on genome
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- Spacer-toxin targeting relationships
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- CRISPR-enhanced activity scores
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**Output Format:**
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- Extended PDF report with CRISPR section
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- Additional JSON with CRISPR metadata
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- Optional: Genomic track visualization (SVG/PNG)
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---
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## Pixi Integration
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The pixi environment is already configured (commented out) in `pixi.toml`:
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```toml
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# =========================
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# CRISPR-Cas 环境：预留用于未来的 CRISPR-Cas 分析
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# =========================
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# [feature.crispr.dependencies]
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# python = ">=3.9"
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# biopython = "*"
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# pandas = ">=2.0.0"
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# =========================
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# [feature.crispr.tasks]
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# crispr-detect = "python crispr_cas/scripts/detect_crispr.py"
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# crispr-fusion = "python crispr_cas/scripts/fusion_analysis.py"
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```
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**To activate CRISPR module:**
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1. Uncomment the `[feature.crispr.dependencies]` section
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2. Uncomment the `[feature.crispr.tasks]` section
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3. Add `crispr` to environments list
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4. Run `pixi install`
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---
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## Usage Examples (When Implemented)
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### Basic CRISPR Detection
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```bash
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pixi run -e crispr crispr-detect \
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--input genome.fna \
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--output crispr_results.json
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```
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### Full Pipeline with CRISPR Integration
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```bash
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# Run CRISPR detection first
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pixi run -e crispr crispr-detect --input genome.fna --output crispr.json
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# Run pipeline with CRISPR-enhanced scoring
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pixi run pipeline \
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--input genome.fna \
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--toxicity_csv Data/toxicity-data.csv \
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--crispr_results crispr.json \
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--crispr_weight 0.2 \
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--crispr_fusion
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```
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### API Integration (Future)
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```python
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# Backend API endpoint (to be implemented)
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POST /api/v1/tasks
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{
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"files": ["genome.fna"],
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"crispr_enabled": true,
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"crispr_weight": 0.2,
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"crispr_fusion": true
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}
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```
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---
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## Scientific Background
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### Why CRISPR-Cas in Bt Analysis?
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1. **Self-Immunity**: CRISPR-Cas systems in Bt may provide immunity against phages, affecting strain fitness
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2. **Plasmid Tracking**: CRISPR spacers can indicate plasmid content and horizontal gene transfer history
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3. **Strain Differentiation**: CRISPR array patterns can distinguish closely related strains
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4. **Toxin Gene Proximity**: CRISPR arrays near toxin genes may indicate genomic defense mechanisms
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### Expected Benefits
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- Enhanced strain characterization beyond toxin profiling
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- Better understanding of strain evolution and adaptation
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- Potential correlation with biocontrol efficacy
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- Additional markers for strain selection
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---
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## Testing Strategy
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### Unit Tests
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- CRISPR detection mock data parsing
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- Spacer-toxin distance calculation
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- CRISPR score calculation logic
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### Integration Tests
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- End-to-end pipeline with small genome
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- Comparison with manual CRISPRCasFinder results
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- Scoring consistency with/without CRISPR
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### Validation
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- Compare CRISPR-enhanced scores with experimental bioassay data
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- Validate CRISPR-toxin associations using known literature
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---
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## Dependencies
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### External Tools
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- **CRISPRCasFinder** (v4.2+): https://crisprcas.i2bc.paris-saclay.fr/
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- **BLAST+** (for spacer-toxin alignment)
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### Python Packages
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- biopython >= 1.79
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- pandas >= 2.0.0
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- numpy >= 1.21.0
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---
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## Timeline Estimate
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- **Phase 1**: 2-3 weeks (CRISPR detection wrapper)
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- **Phase 2**: 2-3 weeks (Fusion analysis)
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- **Phase 3**: 1-2 weeks (Shoter integration)
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- **Phase 4**: 2-3 weeks (Visualization)
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**Total**: ~2-3 months for full implementation
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---
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## References
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1. Couvin, D. et al. (2018) CRISPRCasFinder, an update of CRISPRFinder, includes a portable version, a web server and many tools to study CRISPRs. *Bioinformatics*, 34(20), 3579-3581.
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2. Chakraborty, S. et al. (2020) CRISPR-Cas systems in Bacillus thuringiensis: diversity, evolution and potential applications. *Frontiers in Microbiology*, 11, 591.
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3. BtToxin Pipeline Documentation: `docs/shotter_workflow.md`
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---
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## Contact
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For questions or implementation guidance, refer to the main project documentation or create an issue in the project repository.
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**Last Updated:** 2025-01-13
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**Status:** Reserved - Implementation Pending
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