| |
| """Untitled17.ipynb |
| |
| Automatically generated by Colab. |
| |
| Original file is located at |
| https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV |
| """ |
|
|
| import pandas as pd |
| import numpy as np |
| import gzip |
| import re |
| from pathlib import Path |
| from tqdm import tqdm |
|
|
|
|
|
|
| PATHS = { |
| 'data_raw': BASE_PATH / 'data' / 'raw', |
| 'data_processed': BASE_PATH / 'data' / 'processed', |
| 'checkpoints': BASE_PATH / 'models' / 'checkpoints', |
| } |
|
|
| for path in PATHS.values(): |
| path.mkdir(parents=True, exist_ok=True) |
|
|
| AA_3TO1 = { |
| 'Ala': 'A', 'Arg': 'R', 'Asn': 'N', 'Asp': 'D', 'Cys': 'C', |
| 'Glu': 'E', 'Gln': 'Q', 'Gly': 'G', 'His': 'H', 'Ile': 'I', |
| 'Leu': 'L', 'Lys': 'K', 'Met': 'M', 'Phe': 'F', 'Pro': 'P', |
| 'Ser': 'S', 'Thr': 'T', 'Trp': 'W', 'Tyr': 'Y', 'Val': 'V' |
| } |
|
|
|
|
|
|
| clinvar_file = Path("/content/drive/MyDrive/clinvar/variation_summary.txt.gz") |
|
|
| if clinvar_file.exists(): |
| print(f" Fichier: {clinvar_file}") |
|
|
| with gzip.open(clinvar_file, "rt") as f: |
| df_clinvar_raw = pd.read_csv(f, sep="\t", low_memory=False) |
|
|
| print(f" Lignes totales: {len(df_clinvar_raw):,}") |
| print(f" Colonnes: {df_clinvar_raw.columns.tolist()[:10]}...") |
|
|
| df_clinvar = df_clinvar_raw[ |
| df_clinvar_raw["GeneSymbol"].notna() & |
| df_clinvar_raw["ProteinChange"].notna() & |
| df_clinvar_raw["ClinicalSignificance"].str.contains("Pathogenic|Benign", case=False, na=False) |
| ].copy() |
|
|
| print(f" Après filtre basique: {len(df_clinvar):,}") |
|
|
| MITO_KEYWORDS = [ |
| "mitochondrial", "Leigh", "MELAS", "MERRF", "NARP", "LHON", |
| "optic atrophy", "OXPHOS", "complex I", "complex II", "complex III", |
| "complex IV", "complex V", "cardiomyopathy", "encephalopathy", |
| "myopathy", "aminoacyl-tRNA", "respiratory chain" |
| ] |
|
|
| pattern = "|".join(MITO_KEYWORDS) |
|
|
| MITO_GENES = [ |
| 'OPA1', 'MFN1', 'MFN2', 'DNM1L', 'AFG3L2', 'SPG7', |
| 'SURF1', 'SCO1', 'SCO2', 'COX10', 'COX15', 'COX6B1', |
| 'NDUFAF1', 'NDUFAF2', 'NDUFAF3', 'NDUFAF4', 'NDUFAF5', 'NDUFAF6', |
| 'NUBPL', 'ACAD9', 'TIMMDC1', 'FOXRED1', |
| 'CHCHD10', 'CHCHD2', 'TIMM50', 'DNAJC19', 'AGK', |
| 'HARS2', 'IARS2', 'LARS2', 'MARS2', 'RARS2', 'VARS2', 'YARS2', |
| 'DARS2', 'SARS2', 'TARS2', 'AARS2', 'EARS2', 'FARS2', 'NARS2', 'PARS2', |
| 'POLG', 'POLG2', 'TWNK', 'RRM2B', 'MPV17', 'DGUOK', 'TK2', |
| 'SUCLA2', 'SUCLG1', 'FBXL4', 'SLC25A4', 'SLC25A3', |
| 'RMND1', 'GTPBP3', 'MTO1', 'TRMU', 'TSFM', 'GFM1', 'C12orf65', |
| 'LRPPRC', 'TACO1', 'MTFMT', 'ELAC2', |
| 'BCS1L', 'TTC19', 'UQCRQ', 'UQCRB', 'UQCRC2', |
| 'COA5', 'COA6', 'COA7', 'PET100', 'PET117', |
| 'TMEM70', 'ATP5F1A', 'ATP5F1D', 'ATP5F1E', |
| ] |
|
|
| df_mito = df_clinvar[ |
| df_clinvar["PhenotypeList"].str.contains(pattern, case=False, na=False) | |
| df_clinvar["GeneSymbol"].str.upper().isin([g.upper() for g in MITO_GENES]) |
| ].copy() |
|
|
| print(f" Variants mitochondriaux: {len(df_mito):,}") |
|
|
| else: |
| print(" non trouvé") |
| print(" → Téléchargez depuis: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/") |
| df_mito = pd.DataFrame() |
|
|
|
|
|
|
| records = [] |
|
|
| if len(df_mito) > 0: |
| for _, row in tqdm(df_mito.iterrows(), total=len(df_mito), desc="Parsing"): |
| protein_change = str(row.get("ProteinChange", "")) |
|
|
| match = re.search(r'p\.([A-Z])(\d+)([A-Z])', protein_change) |
|
|
| if not match: |
| match = re.search(r'p\.([A-Z][a-z]{2})(\d+)([A-Z][a-z]{2})', protein_change) |
| if match: |
| wt_3, pos, mut_3 = match.groups() |
| wt = AA_3TO1.get(wt_3) |
| mut = AA_3TO1.get(mut_3) |
| if wt and mut: |
| match = type('Match', (), {'groups': lambda: (wt, pos, mut)})() |
| else: |
| match = None |
|
|
| if not match: |
| continue |
|
|
| wt, pos, mut = match.groups() |
|
|
| clin_sig = str(row.get("ClinicalSignificance", "")).lower() |
|
|
| if "pathogenic" in clin_sig and "benign" not in clin_sig: |
| label = 1 |
| elif "benign" in clin_sig and "pathogenic" not in clin_sig: |
| label = 0 |
| else: |
| continue |
|
|
| review = str(row.get("ReviewStatus", "")) |
|
|
| records.append({ |
| "gene_symbol": str(row["GeneSymbol"]).upper(), |
| "position": int(pos) - 1, |
| "wt_aa": wt, |
| "mut_aa": mut, |
| "label": label, |
| "source": "ClinVar_local", |
| "review_status": review, |
| "clinical_significance": row.get("ClinicalSignificance", ""), |
| "phenotype": str(row.get("PhenotypeList", ""))[:100], |
| }) |
|
|
| df_clinvar_parsed = pd.DataFrame(records) |
|
|
| print(f"\n ✓ Variants parsés: {len(df_clinvar_parsed)}") |
|
|
| if len(df_clinvar_parsed) > 0: |
| print(f"\n Labels:") |
| print(df_clinvar_parsed["label"].value_counts()) |
|
|
| print(f"\n Top 15 gènes:") |
| print(df_clinvar_parsed["gene_symbol"].value_counts().head(15)) |
|
|
| print(f"\n Review status:") |
| print(df_clinvar_parsed["review_status"].value_counts().head(5)) |
|
|
|
|
| uniprot_file = PATHS['data_raw'] / 'uniprot_mito_extended.parquet' |
| proteins_file = PATHS['data_processed'] / 'proteins_targeted.parquet' |
|
|
| seq_dict = {} |
| gene_to_acc = {} |
| acc_to_info = {} |
|
|
| if uniprot_file.exists(): |
| df_uniprot = pd.read_parquet(uniprot_file) |
| for _, row in df_uniprot.iterrows(): |
| acc = row['accession'] |
| seq = row['sequence'] |
| gene = str(row['gene_name']).upper() if row['gene_name'] else '' |
|
|
| seq_dict[acc] = seq |
| acc_to_info[acc] = { |
| 'cysteine_fraction': row.get('cysteine_fraction', seq.count('C')/len(seq) if seq else 0), |
| 'mito_region': row.get('mito_region', 'Unknown'), |
| } |
|
|
| if gene: |
| gene_to_acc[gene] = acc |
| gene_to_acc[gene.replace('-', '')] = acc |
|
|
| print(f" Protéines UniProt: {len(df_uniprot)}") |
|
|
| if proteins_file.exists(): |
| df_proteins = pd.read_parquet(proteins_file) |
| for _, row in df_proteins.iterrows(): |
| acc = row['accession'] |
| seq = row['sequence'] |
| gene = row['gene_symbol'].upper() |
|
|
| if acc not in seq_dict: |
| seq_dict[acc] = seq |
| gene_to_acc[gene] = acc |
|
|
| print(f" Protéines : {len(df_proteins)}") |
|
|
| print(f" séquences: {len(seq_dict)}") |
| print(f" Gènes : {len(gene_to_acc)}") |
|
|
|
|
|
|
| validated = [] |
| not_found_genes = set() |
| seq_mismatch = 0 |
|
|
| for _, row in tqdm(df_clinvar_parsed.iterrows(), total=len(df_clinvar_parsed), desc="Validation"): |
| gene = row['gene_symbol'] |
|
|
| acc = gene_to_acc.get(gene) |
|
|
| if not acc: |
| for variant in [gene.replace('-', ''), gene.split('-')[0], gene.split('_')[0]]: |
| if variant in gene_to_acc: |
| acc = gene_to_acc[variant] |
| break |
|
|
| if not acc: |
| not_found_genes.add(gene) |
| continue |
|
|
| seq = seq_dict.get(acc, '') |
| if not seq: |
| continue |
|
|
| pos = row['position'] |
| wt = row['wt_aa'] |
| mut = row['mut_aa'] |
|
|
| if 0 <= pos < len(seq): |
| if seq[pos] == wt: |
| info = acc_to_info.get(acc, {}) |
|
|
| validated.append({ |
| 'uniprot_acc': acc, |
| 'gene_symbol': gene, |
| 'position': pos, |
| 'wt_aa': wt, |
| 'mut_aa': mut, |
| 'label': row['label'], |
| 'source': row['source'], |
| 'review_status': row.get('review_status', ''), |
| 'clinical_significance': row.get('clinical_significance', ''), |
| 'phenotype': row.get('phenotype', ''), |
| 'cysteine_fraction': info.get('cysteine_fraction', 0), |
| 'mito_region': info.get('mito_region', 'Unknown'), |
| }) |
| else: |
| seq_mismatch += 1 |
|
|
| df_validated = pd.DataFrame(validated) |
|
|
|
|
|
|
| if len(df_validated) > 0: |
| print(f"\n Labels validés:") |
| print(df_validated["label"].value_counts()) |
|
|
|
|
| benign_file = PATHS['data_processed'] / 'mutations_master.parquet' |
|
|
| if benign_file.exists(): |
| df_benign_existing = pd.read_parquet(benign_file) |
| print(f" Socle bénin existant: {len(df_benign_existing)}") |
|
|
| |
| df_benign_existing = df_benign_existing.copy() |
| df_benign_existing['label'] = 0 |
| df_benign_existing['source'] = df_benign_existing.get('label_source', 'gnomAD_UniProt') |
|
|
| else: |
| df_benign_existing = pd.DataFrame() |
|
|
|
|
|
|
| datasets = [] |
|
|
| if len(df_validated) > 0: |
| datasets.append(df_validated) |
| print(f" + ClinVar: {len(df_validated)}") |
|
|
| if len(df_benign_existing) > 0: |
| cols = ['uniprot_acc', 'position', 'wt_aa', 'mut_aa', 'label', 'source'] |
| cols_exist = [c for c in cols if c in df_benign_existing.columns] |
| df_benign_clean = df_benign_existing[cols_exist].copy() |
|
|
| if 'gene_symbol' not in df_benign_clean.columns and 'gene_symbol' in df_benign_existing.columns: |
| df_benign_clean['gene_symbol'] = df_benign_existing['gene_symbol'] |
|
|
| datasets.append(df_benign_clean) |
| print(f" + Bénins existants: {len(df_benign_clean)}") |
|
|
| if datasets: |
| df_final = pd.concat(datasets, ignore_index=True) |
|
|
| df_final['mutation_key'] = ( |
| df_final['uniprot_acc'].astype(str) + '_' + |
| df_final['position'].astype(str) + '_' + |
| df_final['mut_aa'].astype(str) |
| ) |
|
|
| df_final['priority'] = df_final['source'].apply(lambda x: 0 if 'ClinVar' in str(x) else 1) |
| df_final = df_final.sort_values('priority') |
| df_final = df_final.drop_duplicates(subset='mutation_key', keep='first') |
| df_final = df_final.drop(columns=['priority', 'mutation_key']) |
|
|
| print(f"\n ✓ Dataset final: {len(df_final)}") |
| else: |
| df_final = pd.DataFrame() |
| print(" Aucun dataset à fusionner") |
|
|
|
|
|
|
| if len(df_final) > 0: |
|
|
| df_final['n_terminal'] = df_final['position'] < 50 |
| df_final['cysteine_gained'] = df_final['mut_aa'] == 'C' |
| df_final['cysteine_lost'] = df_final['wt_aa'] == 'C' |
| df_final['mutation_id'] = df_final['wt_aa'] + (df_final['position'] + 1).astype(str) + df_final['mut_aa'] |
|
|
| df_final['ros_axis'] = ( |
| df_final['cysteine_lost'] | |
| df_final['cysteine_gained'] | |
| (df_final.get('cysteine_fraction', 0) > 0.03) | |
| (df_final.get('mito_region', '') == 'IMS') |
| ) |
|
|
| df_final['import_axis'] = df_final['n_terminal'] |
|
|
| df_final.to_parquet(PATHS['data_processed'] / 'mutations_dataset_final.parquet') |
| df_final.to_csv(PATHS['data_processed'] / 'mutations_dataset_final.tsv', sep='\t', index=False) |
|
|
| if len(df_clinvar_parsed) > 0: |
| df_clinvar_parsed.to_parquet(PATHS['data_raw'] / 'clinvar_mito_parsed.parquet') |
