| |
| """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 |
| from ast import literal_eval |
| import re |
|
|
| df_disprot = pd.read_parquet(PATHS['disprot'] / 'disprot_data.parquet') |
| df_uniprot = pd.read_parquet(PATHS['uniprot'] / 'uniprot_mitochondrial.parquet') |
| df_clinvar = pd.read_parquet(PATHS['clinvar'] / 'clinvar_variants.parquet') |
| df_mobidb = pd.read_parquet(PATHS['mobidb'] / 'mobidb_data.parquet') |
|
|
| print(f" DisProt: {len(df_disprot)} ") |
| print(f" UniProt: {len(df_uniprot)} ") |
| print(f" ClinVar: {len(df_clinvar)} ") |
| print(f" MobiDB: {len(df_mobidb)} ") |
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| mito_accs = set(df_uniprot['uniprot_acc'].unique()) |
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| df_disprot_mito = df_disprot[df_disprot['uniprot_acc'].isin(mito_accs)].copy() |
| print(f" ✓ DisProt : {len(df_disprot_mito)} régions ({df_disprot_mito['uniprot_acc'].nunique()} protéines)") |
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| idp_mito_accs = set(df_disprot_mito['uniprot_acc'].unique()) |
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| def parse_protein_change(change_str: str) -> dict: |
| if not change_str or pd.isna(change_str): |
| return None |
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| aa_map = { |
| 'Ala': 'A', 'Arg': 'R', 'Asn': 'N', 'Asp': 'D', 'Cys': 'C', |
| 'Gln': 'Q', 'Glu': 'E', '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', |
| 'Ter': '*' |
| } |
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|
| pattern = r'([A-Z][a-z]{2})(\d+)([A-Z][a-z]{2})' |
| match = re.match(pattern, change_str) |
|
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| if match: |
| wt_3 = match.group(1) |
| pos = int(match.group(2)) |
| mut_3 = match.group(3) |
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| wt_1 = aa_map.get(wt_3, '?') |
| mut_1 = aa_map.get(mut_3, '?') |
|
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| if wt_1 != '?' and mut_1 != '?' and mut_1 != '*': |
| return { |
| 'position': pos - 1, |
| 'wt_aa': wt_1, |
| 'mut_aa': mut_1 |
| } |
|
|
| return None |
|
|
| parsed_mutations = [] |
| for idx, row in df_clinvar.iterrows(): |
| parsed = parse_protein_change(row['protein_change']) |
| if parsed: |
| parsed['clinvar_id'] = row['clinvar_id'] |
| parsed['gene'] = row['gene'] |
| parsed['is_pathogenic'] = row['is_pathogenic'] |
| parsed['is_benign'] = row['is_benign'] |
| parsed['clinical_significance'] = row['clinical_significance'] |
| parsed_mutations.append(parsed) |
|
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| df_mutations = pd.DataFrame(parsed_mutations) |
| print(f" ✓ {len(df_mutations)} mutations parsées avec succès") |
|
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| n_pathogenic = df_mutations['is_pathogenic'].sum() |
| n_benign = df_mutations['is_benign'].sum() |
| print(f" ✓ Pathognes {n_pathogenic}") |
| print(f" ✓ Bénin: {n_benign}") |
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| gene_to_seq = {} |
| gene_to_acc = {} |
| for _, row in df_uniprot.iterrows(): |
| gene = row['gene_name'] |
| if gene and row['sequence']: |
| gene_to_seq[gene] = row['sequence'] |
| gene_to_acc[gene] = row['uniprot_acc'] |
|
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| df_mutations['sequence'] = df_mutations['gene'].map(gene_to_seq) |
| df_mutations['uniprot_acc'] = df_mutations['gene'].map(gene_to_acc) |
|
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| df_mutations_valid = df_mutations.dropna(subset=['sequence']).copy() |
|
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| def validate_mutation(row): |
| seq = row['sequence'] |
| pos = row['position'] |
| wt = row['wt_aa'] |
|
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| if pos < 0 or pos >= len(seq): |
| return False |
|
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| actual_aa = seq[pos] |
| return actual_aa == wt |
|
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| df_mutations_valid['is_valid'] = df_mutations_valid.apply(validate_mutation, axis=1) |
| df_mutations_final = df_mutations_valid[df_mutations_valid['is_valid']].copy() |
|
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| print(f" ✓ {len(df_mutations_final)} mutations validé") |
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| df_pathogenic = df_mutations_final[df_mutations_final['is_pathogenic']].copy() |
| df_benign = df_mutations_final[df_mutations_final['is_benign']].copy() |
|
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| print(f" Pathogènes : {len(df_pathogenic)}") |
| print(f" Bénins : {len(df_benign)}") |
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| df_pathogenic['label'] = 1 |
| df_benign['label'] = 0 |
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| df_dataset = pd.concat([df_pathogenic, df_benign], ignore_index=True) |
| df_dataset = df_dataset.sample(frac=1, random_state=42).reset_index(drop=True) |
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| print(f" ✓ : {len(df_dataset)} mutations") |
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| disorder_regions_by_acc = {} |
| for acc in df_disprot['uniprot_acc'].unique(): |
| regions = df_disprot[df_disprot['uniprot_acc'] == acc][['region_start', 'region_end']].values.tolist() |
| disorder_regions_by_acc[acc] = regions |
|
|
| def is_in_disorder_region(row): |
| acc = row['uniprot_acc'] |
| pos = row['position'] + 1 |
|
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| if acc not in disorder_regions_by_acc: |
| return None |
| for start, end in disorder_regions_by_acc[acc]: |
| if start <= pos <= end: |
| return True |
| return False |
|
|
| df_dataset['in_disorder_region'] = df_dataset.apply(is_in_disorder_region, axis=1) |
|
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| n_in_disorder = df_dataset['in_disorder_region'].sum() |
| n_annotated = df_dataset['in_disorder_region'].notna().sum() |
| print(f" ✓ {n_in_disorder}/{n_annotated} ") |
