""" Export primers in multiple formats. This module provides functions to export primer designs in various formats for ordering, documentation, and analysis. """ import json import csv from typing import Dict, List from datetime import datetime def export_primers( primers: Dict, format: str = "csv", output_file: str = None, include_validation: bool = True, validation_results: Dict = None ) -> str: """ Export primers to various formats. Parameters ---------- primers : dict Primer design results format : str Export format: "csv", "excel", "json", "idt_order", "miqe_checklist" output_file : str, optional Output file path. If None, returns string. include_validation : bool Include validation results if available. Default: True validation_results : dict, optional Validation results to include Returns ------- str Exported data (if output_file is None) or file path Example ------- >>> export_primers( ... primers=primer_results, ... format="csv", ... output_file="primers.csv" ... ) """ if format == "csv": return _export_csv(primers, output_file, include_validation, validation_results) elif format == "excel": return _export_excel(primers, output_file, include_validation, validation_results) elif format == "json": return _export_json(primers, output_file, include_validation, validation_results) elif format == "idt_order": return _export_idt_order(primers, output_file) elif format == "miqe_checklist": return _export_miqe_checklist(primers, output_file) else: raise ValueError(f"Unknown export format: {format}") def _export_csv( primers: Dict, output_file: str = None, include_validation: bool = True, validation_results: Dict = None ) -> str: """Export to CSV format.""" rows = [] # Header header = [ 'Pair_ID', 'Forward_Sequence', 'Reverse_Sequence', 'Amplicon_Size_bp', 'Forward_Tm_C', 'Reverse_Tm_C', 'Tm_Diff_C', 'Forward_GC_%', 'Reverse_GC_%', 'Forward_Length', 'Reverse_Length', 'Forward_Position', 'Reverse_Position', 'Design_Quality_Score' ] # Add validation columns if available if include_validation and validation_results: header.extend(['Specificity', 'Has_Dimers', 'Has_Secondary_Structures']) rows.append(header) # Data rows for pair in primers.get('primers', []): row = [ pair.get('pair_id', ''), pair['forward_seq'], pair['reverse_seq'], pair['amplicon_size'], pair['forward_tm'], pair['reverse_tm'], pair['tm_diff'], pair['forward_gc'], pair['reverse_gc'], pair['forward_length'], pair['reverse_length'], pair['forward_pos'], pair['reverse_pos'], pair['penalty'], ] # Add validation data if include_validation and validation_results: row.extend([ validation_results.get('specificity', {}).get('is_specific', 'N/A'), validation_results.get('dimers', {}).get('has_issues', 'N/A'), validation_results.get('secondary_structures', {}).get('has_issues', 'N/A'), ]) rows.append(row) # Write to file or return string if output_file: with open(output_file, 'w', newline='') as f: writer = csv.writer(f) writer.writerows(rows) return output_file else: import io output = io.StringIO() writer = csv.writer(output) writer.writerows(rows) return output.getvalue() def _export_excel( primers: Dict, output_file: str, include_validation: bool = True, validation_results: Dict = None ) -> str: """Export to Excel format with multiple sheets.""" try: import pandas as pd except ImportError: raise ImportError("pandas required for Excel export. Install with: pip install pandas openpyxl") # Prepare primers dataframe primer_data = [] for pair in primers.get('primers', []): primer_data.append({ 'Pair_ID': pair.get('pair_id', ''), 'Forward_Sequence': pair['forward_seq'], 'Reverse_Sequence': pair['reverse_seq'], 'Amplicon_Size_bp': pair['amplicon_size'], 'Forward_Tm_C': pair['forward_tm'], 'Reverse_Tm_C': pair['reverse_tm'], 'Tm_Diff_C': pair['tm_diff'], 'Forward_GC_%': pair['forward_gc'], 'Reverse_GC_%': pair['reverse_gc'], 'Forward_Length': pair['forward_length'], 'Reverse_Length': pair['reverse_length'], 'Design_Quality': pair['penalty'], }) df_primers = pd.DataFrame(primer_data) # Write to Excel with multiple sheets with pd.ExcelWriter(output_file, engine='openpyxl') as writer: df_primers.to_excel(writer, sheet_name='Primers', index=False) # Add parameters sheet params = primers.get('parameters', {}) df_params = pd.DataFrame([params]) df_params.to_excel(writer, sheet_name='Parameters', index=False) # Add validation sheet if available if include_validation and validation_results: val_data = [] for key, value in validation_results.items(): if isinstance(value, dict): for subkey, subvalue in value.items(): val_data.append({ 'Category': key, 'Check': subkey, 'Result': str(subvalue) }) if val_data: df_validation = pd.DataFrame(val_data) df_validation.to_excel(writer, sheet_name='Validation', index=False) return output_file def _export_json( primers: Dict, output_file: str = None, include_validation: bool = True, validation_results: Dict = None ) -> str: """Export to JSON format.""" export_data = { 'metadata': { 'generated': datetime.now().isoformat(), 'sequence_length': primers.get('sequence_length'), 'num_primers': primers.get('num_primers_found'), }, 'parameters': primers.get('parameters', {}), 'primers': primers.get('primers', []), } if include_validation and validation_results: export_data['validation'] = validation_results # Write to file or return string if output_file: with open(output_file, 'w') as f: json.dump(export_data, f, indent=2) return output_file else: return json.dumps(export_data, indent=2) def _export_idt_order(primers: Dict, output_file: str = None) -> str: """ Export in IDT (Integrated DNA Technologies) order format. Format: Name, Sequence, Scale, Purification """ lines = [] lines.append("Name\tSequence\tScale\tPurification") for i, pair in enumerate(primers.get('primers', []), 1): # Forward primer fwd_name = f"Primer_Pair{i}_Forward" lines.append(f"{fwd_name}\t{pair['forward_seq']}\t25nm\tSTD") # Reverse primer rev_name = f"Primer_Pair{i}_Reverse" lines.append(f"{rev_name}\t{pair['reverse_seq']}\t25nm\tSTD") # Probe if present if 'probe_seq' in pair: probe_name = f"Primer_Pair{i}_Probe" lines.append(f"{probe_name}\t{pair['probe_seq']}\t25nm\tHPLC") content = "\n".join(lines) if output_file: with open(output_file, 'w') as f: f.write(content) return output_file else: return content def _export_miqe_checklist(primers: Dict, output_file: str) -> str: """ Export MIQE compliance checklist. Creates an Excel file with MIQE checklist pre-filled where possible. """ try: import pandas as pd except ImportError: raise ImportError("pandas required for MIQE checklist. Install with: pip install pandas openpyxl") # MIQE essential information miqe_items = [ # Experimental Design ('Experimental Design', 'qPCR purpose', '', 'Essential'), ('Experimental Design', 'Experimental design', '', 'Essential'), ('Experimental Design', 'Sample nature', '', 'Essential'), # Sample ('Sample', 'Description', '', 'Essential'), ('Sample', 'Volume/mass', '', 'Essential'), ('Sample', 'Storage conditions', '', 'Essential'), ('Sample', 'RNA/DNA quality', '', 'Essential'), # Primers ('Primers', 'Primer sequences', 'See Primers sheet', 'Essential'), ('Primers', 'Location on target', f"{primers.get('primers', [{}])[0].get('forward_pos', 'N/A')} - {primers.get('primers', [{}])[0].get('reverse_pos', 'N/A')}", 'Essential'), ('Primers', 'Amplicon length', f"{primers.get('primers', [{}])[0].get('amplicon_size', 'N/A')} bp", 'Essential'), ('Primers', 'In silico specificity check', 'Performed', 'Essential'), ('Primers', 'Empirical specificity check', '', 'Essential'), # Protocol ('Protocol', 'Complete reaction conditions', '', 'Essential'), ('Protocol', 'Reaction volume', '', 'Essential'), ('Protocol', 'Primer concentration', '', 'Essential'), ('Protocol', 'Cycling conditions', '', 'Essential'), # Validation ('Validation', 'Evidence of optimization', '', 'Essential'), ('Validation', 'Specificity (melt curve)', '', 'Essential'), ('Validation', 'Standard curve slope', '', 'Essential'), ('Validation', 'PCR efficiency', '', 'Essential'), ('Validation', 'R²', '', 'Essential'), ('Validation', 'Linear dynamic range', '', 'Essential'), ('Validation', 'Cq variation at LOD', '', 'Essential'), # Data Analysis ('Data Analysis', 'Cq method', '', 'Essential'), ('Data Analysis', 'Outlier identification', '', 'Essential'), ('Data Analysis', 'Results of NTCs', '', 'Essential'), ('Data Analysis', 'Normalization method', '', 'Essential'), ('Data Analysis', 'Number and description of reference genes', '', 'Essential'), ('Data Analysis', 'Statistical methods', '', 'Essential'), ] df_miqe = pd.DataFrame(miqe_items, columns=['Category', 'Item', 'Value', 'MIQE Level']) # Also include primer details primer_data = [] for i, pair in enumerate(primers.get('primers', []), 1): primer_data.append({ 'Pair': i, 'Forward': pair['forward_seq'], 'Reverse': pair['reverse_seq'], 'Amplicon_bp': pair['amplicon_size'], 'Tm_F': pair['forward_tm'], 'Tm_R': pair['reverse_tm'], }) df_primers = pd.DataFrame(primer_data) # Write to Excel with pd.ExcelWriter(output_file, engine='openpyxl') as writer: df_miqe.to_excel(writer, sheet_name='MIQE_Checklist', index=False) df_primers.to_excel(writer, sheet_name='Primers', index=False) return output_file def export_for_benchling(primers: Dict, output_file: str = None) -> str: """ Export in Benchling-compatible format. Parameters ---------- primers : dict Primer design results output_file : str, optional Output CSV file path Returns ------- str File path or CSV content """ rows = [] rows.append(['Name', 'Bases', 'Description']) for i, pair in enumerate(primers.get('primers', []), 1): rows.append([ f"Pair{i}_Forward", pair['forward_seq'], f"Forward primer, Tm={pair['forward_tm']}°C, GC={pair['forward_gc']}%" ]) rows.append([ f"Pair{i}_Reverse", pair['reverse_seq'], f"Reverse primer, Tm={pair['reverse_tm']}°C, GC={pair['reverse_gc']}%" ]) if output_file: with open(output_file, 'w', newline='') as f: writer = csv.writer(f) writer.writerows(rows) return output_file else: import io output = io.StringIO() writer = csv.writer(output) writer.writerows(rows) return output.getvalue()