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"""Untitled17.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV
"""
import requests
import json
import gzip
import time
import pandas as pd
from io import StringIO, BytesIO
from tqdm.auto import tqdm
import pickle
class DisProtDownloader:
BASE_URL = "https://disprot.org/api"
def __init__(self, save_dir: Path):
self.save_dir = save_dir
self.save_dir.mkdir(parents=True, exist_ok=True)
def download_all(self) -> pd.DataFrame:
"""Télécharger toutes les entrées DisProt"""
print("\n📥 Téléchargement DisProt...")
url = f"{self.BASE_URL}/search?release=current&show_ambiguous=false&format=json"
try:
response = requests.get(url, timeout=120)
response.raise_for_status()
data = response.json()
entries = data.get('data', [])
print(f" ✓ {len(entries)} entrées téléchargées")
records = []
for entry in tqdm(entries, desc=" Parsing"):
acc = entry.get('acc', '')
disprot_id = entry.get('disprot_id', '')
name = entry.get('name', '')
sequence = entry.get('sequence', '')
organism = entry.get('organism', '')
for region in entry.get('regions', []):
records.append({
'disprot_id': disprot_id,
'uniprot_acc': acc,
'name': name,
'organism': organism,
'sequence': sequence,
'region_start': region.get('start', 0),
'region_end': region.get('end', 0),
'region_type': region.get('type', ''),
'term_name': region.get('term_name', ''),
'evidence': region.get('evidence', '')
})
df = pd.DataFrame(records)
save_path = self.save_dir / 'disprot_data.parquet'
df.to_parquet(save_path)
print(f" ✓ Sauvegardé: {save_path}")
return df
except Exception as e:
print(f" ✗ Erreur DisProt: {e}")
return pd.DataFrame()
class UniProtDownloader:
BASE_URL = "https://rest.uniprot.org/uniprotkb"
def __init__(self, save_dir: Path):
self.save_dir = save_dir
self.save_dir.mkdir(parents=True, exist_ok=True)
def download_mitochondrial_human(self, max_results: int = 5000) -> pd.DataFrame:
"""Télécharger les protéines mitochondriales humaines"""
query = "(organism_id:9606) AND (cc_scl_term:SL-0173)"
url = f"{self.BASE_URL}/search"
params = {
'query': query,
'format': 'json',
'size': min(500, max_results),
'fields': 'accession,id,protein_name,gene_names,sequence,length,cc_subcellular_location,ft_domain,ft_region,organism_name'
}
all_results = []
try:
response = requests.get(url, params=params, timeout=120)
response.raise_for_status()
data = response.json()
results = data.get('results', [])
all_results.extend(results)
print(f" ✓ {len(results)} ")
next_link = data.get('link', {}).get('next')
while next_link and len(all_results) < max_results:
time.sleep(0.5)
response = requests.get(next_link, timeout=120)
response.raise_for_status()
data = response.json()
results = data.get('results', [])
all_results.extend(results)
next_link = data.get('link', {}).get('next')
print(f" ... {len(all_results)} protéines")
records = []
for entry in tqdm(all_results[:max_results], desc=" Parsing"):
acc = entry.get('primaryAccession', '')
seq_data = entry.get('sequence', {})
sequence = seq_data.get('value', '')
length = seq_data.get('length', 0)
protein_name = ''
if 'proteinDescription' in entry:
rec_name = entry['proteinDescription'].get('recommendedName', {})
protein_name = rec_name.get('fullName', {}).get('value', '')
genes = entry.get('genes', [])
gene_name = genes[0].get('geneName', {}).get('value', '') if genes else ''
subcell = entry.get('comments', [])
locations = []
for comment in subcell:
if comment.get('commentType') == 'SUBCELLULAR LOCATION':
for loc in comment.get('subcellularLocations', []):
loc_val = loc.get('location', {}).get('value', '')
if loc_val:
locations.append(loc_val)
features = entry.get('features', [])
disorder_regions = []
for feat in features:
if feat.get('type') in ['Region', 'Compositional bias']:
desc = feat.get('description', '').lower()
if 'disordered' in desc or 'low complexity' in desc:
loc = feat.get('location', {})
start = loc.get('start', {}).get('value', 0)
end = loc.get('end', {}).get('value', 0)
disorder_regions.append((start, end))
records.append({
'uniprot_acc': acc,
'protein_name': protein_name,
'gene_name': gene_name,
'sequence': sequence,
'length': length,
'subcellular_locations': '|'.join(locations),
'disorder_regions': str(disorder_regions),
'is_mitochondrial': True
})
df = pd.DataFrame(records)
save_path = self.save_dir / 'uniprot_mitochondrial.parquet'
df.to_parquet(save_path)
print(f" ✓ : {save_path}")
return df
except Exception as e:
print(f" ✗ : {e}")
return pd.DataFrame()
class ClinVarDownloader:
def __init__(self, save_dir: Path):
self.save_dir = save_dir
self.save_dir.mkdir(parents=True, exist_ok=True)
def download_variants_for_genes(self, gene_list: List[str], max_per_gene: int = 100) -> pd.DataFrame:
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
all_variants = []
for gene in tqdm(gene_list[:50], desc=" Gènes"):
try:
search_url = f"{base_url}/esearch.fcgi"
search_params = {
'db': 'clinvar',
'term': f'{gene}[gene] AND ("pathogenic"[clinsig] OR "benign"[clinsig]) AND "single nucleotide variant"[vartype]',
'retmax': max_per_gene,
'retmode': 'json'
}
response = requests.get(search_url, params=search_params, timeout=30)
response.raise_for_status()
search_data = response.json()
id_list = search_data.get('esearchresult', {}).get('idlist', [])
if not id_list:
continue
time.sleep(0.34)
fetch_url = f"{base_url}/esummary.fcgi"
fetch_params = {
'db': 'clinvar',
'id': ','.join(id_list[:max_per_gene]),
'retmode': 'json'
}
response = requests.get(fetch_url, params=fetch_params, timeout=30)
response.raise_for_status()
fetch_data = response.json()
results = fetch_data.get('result', {})
for uid in id_list[:max_per_gene]:
if uid not in results or uid == 'uids':
continue
variant = results[uid]
title = variant.get('title', '')
clinical_sig = variant.get('clinical_significance', {}).get('description', '')
protein_change = ''
if '(p.' in title:
start = title.find('(p.') + 3
end = title.find(')', start)
protein_change = title[start:end]
all_variants.append({
'clinvar_id': uid,
'gene': gene,
'title': title,
'protein_change': protein_change,
'clinical_significance': clinical_sig,
'is_pathogenic': 'pathogenic' in clinical_sig.lower(),
'is_benign': 'benign' in clinical_sig.lower()
})
time.sleep(0.34)
except Exception as e:
print(f" Error {gene}: {e}")
continue
df = pd.DataFrame(all_variants)
if len(df) > 0:
save_path = self.save_dir / 'clinvar_variants.parquet'
df.to_parquet(save_path)
print(f" ✓ {len(df)} {save_path}")
else:
print("None")
return df
class MobiDBDownloader:
BASE_URL = "https://mobidb.org/api/download"
def __init__(self, save_dir: Path):
self.save_dir = save_dir
self.save_dir.mkdir(parents=True, exist_ok=True)
def download_for_proteins(self, uniprot_accs: List[str]) -> pd.DataFrame:
records = []
for acc in tqdm(uniprot_accs[:200], desc=" Protéines"):
try:
url = f"https://mobidb.org/api/download?acc={acc}&format=json"
response = requests.get(url, timeout=30)
if response.status_code != 200:
continue
data = response.json()
consensus = data.get('consensus', {})
disorder_regions = consensus.get('disorder', {}).get('regions', [])
plddt_regions = consensus.get('plddt', {}).get('regions', [])
records.append({
'uniprot_acc': acc,
'disorder_content': data.get('disorder_content', 0),
'disorder_regions': str(disorder_regions),
'plddt_low_regions': str(plddt_regions),
'sequence_length': data.get('length', 0)
})
time.sleep(0.1)
except Exception as e:
continue
df = pd.DataFrame(records)
if len(df) > 0:
save_path = self.save_dir / 'mobidb_data.parquet'
df.to_parquet(save_path)
print(f" ✓ {len(df)} entrées sauvegardées: {save_path}")
return df
disprot_downloader = DisProtDownloader(PATHS['disprot'])
df_disprot = disprot_downloader.download_all()
uniprot_downloader = UniProtDownloader(PATHS['uniprot'])
df_uniprot = uniprot_downloader.download_mitochondrial_human(max_results=2000)
if len(df_uniprot) > 0:
mito_genes = df_uniprot['gene_name'].dropna().unique().tolist()
mito_genes = [g for g in mito_genes if g]
clinvar_downloader = ClinVarDownloader(PATHS['clinvar'])
df_clinvar = clinvar_downloader.download_variants_for_genes(mito_genes[:100])
else:
df_clinvar = pd.DataFrame()
if len(df_uniprot) > 0:
mito_accs = df_uniprot['uniprot_acc'].tolist()
mobidb_downloader = MobiDBDownloader(PATHS['mobidb'])
df_mobidb = mobidb_downloader.download_for_proteins(mito_accs[:200])
else:
df_mobidb = pd.DataFrame() |