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f07511a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | # -*- coding: utf-8 -*-
"""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)} ")
mito_accs = set(df_uniprot['uniprot_acc'].unique())
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)")
idp_mito_accs = set(df_disprot_mito['uniprot_acc'].unique())
def parse_protein_change(change_str: str) -> dict:
if not change_str or pd.isna(change_str):
return None
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': '*'
}
pattern = r'([A-Z][a-z]{2})(\d+)([A-Z][a-z]{2})'
match = re.match(pattern, change_str)
if match:
wt_3 = match.group(1)
pos = int(match.group(2))
mut_3 = match.group(3)
wt_1 = aa_map.get(wt_3, '?')
mut_1 = aa_map.get(mut_3, '?')
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)
df_mutations = pd.DataFrame(parsed_mutations)
print(f" ✓ {len(df_mutations)} mutations parsées avec succès")
n_pathogenic = df_mutations['is_pathogenic'].sum()
n_benign = df_mutations['is_benign'].sum()
print(f" ✓ Pathognes {n_pathogenic}")
print(f" ✓ Bénin: {n_benign}")
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']
df_mutations['sequence'] = df_mutations['gene'].map(gene_to_seq)
df_mutations['uniprot_acc'] = df_mutations['gene'].map(gene_to_acc)
df_mutations_valid = df_mutations.dropna(subset=['sequence']).copy()
def validate_mutation(row):
seq = row['sequence']
pos = row['position']
wt = row['wt_aa']
if pos < 0 or pos >= len(seq):
return False
actual_aa = seq[pos]
return actual_aa == wt
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()
print(f" ✓ {len(df_mutations_final)} mutations validé")
df_pathogenic = df_mutations_final[df_mutations_final['is_pathogenic']].copy()
df_benign = df_mutations_final[df_mutations_final['is_benign']].copy()
print(f" Pathogènes : {len(df_pathogenic)}")
print(f" Bénins : {len(df_benign)}")
df_pathogenic['label'] = 1
df_benign['label'] = 0
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)
print(f" ✓ : {len(df_dataset)} mutations")
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
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)
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} ") |