scripts/data_download.py

# -*- 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()