# -*- coding: utf-8 -*- """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()