Datasets:
File size: 17,306 Bytes
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"""Build viewer-friendly Parquet splits for LiteFold/InterPro."""
from __future__ import annotations
import argparse
import gzip
import hashlib
import json
import re
import shutil
import xml.etree.ElementTree as ET
from collections import Counter
from pathlib import Path
from typing import Any
import pandas as pd
ENTRY_COLUMNS = [
"interpro_id",
"interpro_numeric_id",
"name",
"short_name",
"entry_type",
"protein_count",
"is_llm",
"is_llm_reviewed",
"abstract",
"go_ids",
"go_terms",
"go_categories",
"go_count",
"member_databases",
"member_accessions",
"member_names",
"member_protein_counts",
"member_count",
"external_databases",
"external_accessions",
"external_xrefs",
"external_xref_count",
"pdb_ids",
"structure_count",
"publication_ids",
"pubmed_ids",
"publication_titles",
"publication_years",
"publication_count",
"parent_ids",
"child_ids",
"parent_count",
"child_count",
"tree_depth",
"taxonomy_names",
"taxonomy_protein_counts",
"taxonomy_count",
"key_species_names",
"key_species_protein_counts",
"key_species_count",
"in_entry_list",
"entry_list_type",
"entry_list_name",
"names_dat_name",
"short_names_dat_name",
"split_bucket",
]
def normalize_text(value: str | None) -> str | None:
if value is None:
return None
text = re.sub(r"\s+", " ", value).strip()
return text or None
def parse_int(value: str | None) -> int | None:
if value is None or value == "":
return None
try:
return int(value)
except ValueError:
return None
def parse_bool(value: str | None) -> bool | None:
if value is None or value == "":
return None
lowered = value.lower()
if lowered == "true":
return True
if lowered == "false":
return False
return None
def stable_bucket(value: str, buckets: int = 10) -> int:
digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:16]
return int(digest, 16) % buckets
def text_of(parent: ET.Element, tag: str) -> str | None:
child = parent.find(tag)
if child is None:
return None
return normalize_text("".join(child.itertext()))
def xref_value(db: str | None, dbkey: str | None) -> str:
if db and dbkey:
return f"{db}:{dbkey}"
return dbkey or db or ""
def parse_two_column_file(path: Path) -> dict[str, str]:
mapping: dict[str, str] = {}
if not path.exists():
return mapping
with path.open("r", encoding="utf-8", errors="replace") as handle:
for line in handle:
stripped = line.rstrip("\n")
if not stripped:
continue
parts = stripped.split("\t", 1)
if len(parts) == 2 and parts[0].startswith("IPR"):
mapping[parts[0]] = parts[1]
return mapping
def parse_entry_list(path: Path) -> dict[str, dict[str, str]]:
entries: dict[str, dict[str, str]] = {}
if not path.exists():
return entries
with path.open("r", encoding="utf-8", errors="replace") as handle:
header = next(handle, "").rstrip("\n").split("\t")
for line in handle:
parts = line.rstrip("\n").split("\t")
if len(parts) != len(header):
continue
row = dict(zip(header, parts))
entry_id = row.get("ENTRY_AC")
if entry_id:
entries[entry_id] = row
return entries
def parse_tree_depths(path: Path) -> dict[str, int]:
depths: dict[str, int] = {}
pattern = re.compile(r"^(?P<prefix>-*)(?P<id>IPR\d+)::")
if not path.exists():
return depths
with path.open("r", encoding="utf-8", errors="replace") as handle:
for line in handle:
match = pattern.match(line.strip())
if not match:
continue
entry_id = match.group("id")
depth = len(match.group("prefix")) // 2
previous = depths.get(entry_id)
if previous is None or depth < previous:
depths[entry_id] = depth
return depths
def parse_interpro2go_count(path: Path) -> int:
count = 0
if not path.exists():
return count
with path.open("r", encoding="utf-8", errors="replace") as handle:
for line in handle:
stripped = line.strip()
if stripped and not stripped.startswith("!"):
count += 1
return count
def parse_release_notes(path: Path) -> dict[str, Any]:
notes = path.read_text(encoding="utf-8", errors="replace") if path.exists() else ""
release_match = re.search(r"Release\s+([0-9.]+),\s+([^\n]+)", notes)
last_entry_match = re.search(r"Last Entry\s+(IPR\d+)", notes)
go_match = re.search(r"Number of GO terms mapped to InterPro\s+-\s+([0-9]+)", notes)
return {
"release": release_match.group(1) if release_match else None,
"release_date": release_match.group(2).strip() if release_match else None,
"last_entry": last_entry_match.group(1) if last_entry_match else None,
"interpro_to_go_mappings": int(go_match.group(1)) if go_match else None,
}
def parse_dbinfo(release: ET.Element) -> list[dict[str, Any]]:
rows = []
for item in release.findall("dbinfo"):
rows.append(
{
"dbname": item.attrib.get("dbname"),
"version": item.attrib.get("version"),
"entry_count": parse_int(item.attrib.get("entry_count")),
"file_date": item.attrib.get("file_date"),
}
)
return rows
def extract_taxa(container: ET.Element | None) -> tuple[list[str], list[int]]:
names = []
counts = []
if container is None:
return names, counts
for taxon in container.findall("taxon_data"):
name = taxon.attrib.get("name")
count = parse_int(taxon.attrib.get("proteins_count"))
if name:
names.append(name)
counts.append(count if count is not None else 0)
return names, counts
def interpro_row(
elem: ET.Element,
entry_list: dict[str, dict[str, str]],
names_dat: dict[str, str],
short_names_dat: dict[str, str],
tree_depths: dict[str, int],
) -> dict[str, Any]:
entry_id = elem.attrib.get("id", "")
numeric_match = re.match(r"IPR(\d+)$", entry_id)
entry_info = entry_list.get(entry_id, {})
class_list = elem.find("class_list")
go_ids: list[str] = []
go_terms: list[str] = []
go_categories: list[str] = []
if class_list is not None:
for item in class_list.findall("classification"):
go_id = item.attrib.get("id")
if not go_id:
continue
go_ids.append(go_id)
go_terms.append(text_of(item, "description") or "")
go_categories.append(text_of(item, "category") or "")
member_databases: list[str] = []
member_accessions: list[str] = []
member_names: list[str] = []
member_protein_counts: list[int] = []
member_list = elem.find("member_list")
if member_list is not None:
for item in member_list.findall("db_xref"):
member_databases.append(item.attrib.get("db") or "")
member_accessions.append(item.attrib.get("dbkey") or "")
member_names.append(item.attrib.get("name") or "")
member_protein_counts.append(parse_int(item.attrib.get("protein_count")) or 0)
external_databases: list[str] = []
external_accessions: list[str] = []
external_xrefs: list[str] = []
external_doc_list = elem.find("external_doc_list")
if external_doc_list is not None:
for item in external_doc_list.findall("db_xref"):
db = item.attrib.get("db")
dbkey = item.attrib.get("dbkey")
external_databases.append(db or "")
external_accessions.append(dbkey or "")
external_xrefs.append(xref_value(db, dbkey))
pdb_ids: list[str] = []
structure_db_links = elem.find("structure_db_links")
if structure_db_links is not None:
for item in structure_db_links.findall("db_xref"):
dbkey = item.attrib.get("dbkey")
if dbkey:
pdb_ids.append(dbkey)
publication_ids: list[str] = []
pubmed_ids: list[str] = []
publication_titles: list[str] = []
publication_years: list[int] = []
pub_list = elem.find("pub_list")
if pub_list is not None:
for publication in pub_list.findall("publication"):
publication_ids.append(publication.attrib.get("id") or "")
title = text_of(publication, "title")
publication_titles.append(title or "")
year = parse_int(text_of(publication, "year"))
publication_years.append(year if year is not None else 0)
xref = publication.find("db_xref")
if xref is not None and xref.attrib.get("db") == "PUBMED":
dbkey = xref.attrib.get("dbkey")
if dbkey:
pubmed_ids.append(dbkey)
parent_ids = []
parent_list = elem.find("parent_list")
if parent_list is not None:
parent_ids = [item.attrib["ipr_ref"] for item in parent_list.findall("rel_ref") if item.attrib.get("ipr_ref")]
child_ids = []
child_list = elem.find("child_list")
if child_list is not None:
child_ids = [item.attrib["ipr_ref"] for item in child_list.findall("rel_ref") if item.attrib.get("ipr_ref")]
taxonomy_names, taxonomy_protein_counts = extract_taxa(elem.find("taxonomy_distribution"))
key_species_names, key_species_protein_counts = extract_taxa(elem.find("key_species"))
abstract = elem.find("abstract")
abstract_text = normalize_text(" ".join(abstract.itertext())) if abstract is not None else None
return {
"interpro_id": entry_id,
"interpro_numeric_id": int(numeric_match.group(1)) if numeric_match else None,
"name": text_of(elem, "name"),
"short_name": elem.attrib.get("short_name") or None,
"entry_type": elem.attrib.get("type") or None,
"protein_count": parse_int(elem.attrib.get("protein_count")),
"is_llm": parse_bool(elem.attrib.get("is-llm")),
"is_llm_reviewed": parse_bool(elem.attrib.get("is-llm-reviewed")),
"abstract": abstract_text,
"go_ids": go_ids,
"go_terms": go_terms,
"go_categories": go_categories,
"go_count": len(go_ids),
"member_databases": member_databases,
"member_accessions": member_accessions,
"member_names": member_names,
"member_protein_counts": member_protein_counts,
"member_count": len(member_accessions),
"external_databases": external_databases,
"external_accessions": external_accessions,
"external_xrefs": external_xrefs,
"external_xref_count": len(external_xrefs),
"pdb_ids": pdb_ids,
"structure_count": len(pdb_ids),
"publication_ids": publication_ids,
"pubmed_ids": pubmed_ids,
"publication_titles": publication_titles,
"publication_years": publication_years,
"publication_count": len(publication_ids),
"parent_ids": parent_ids,
"child_ids": child_ids,
"parent_count": len(parent_ids),
"child_count": len(child_ids),
"tree_depth": tree_depths.get(entry_id),
"taxonomy_names": taxonomy_names,
"taxonomy_protein_counts": taxonomy_protein_counts,
"taxonomy_count": len(taxonomy_names),
"key_species_names": key_species_names,
"key_species_protein_counts": key_species_protein_counts,
"key_species_count": len(key_species_names),
"in_entry_list": entry_id in entry_list,
"entry_list_type": entry_info.get("ENTRY_TYPE"),
"entry_list_name": entry_info.get("ENTRY_NAME"),
"names_dat_name": names_dat.get(entry_id),
"short_names_dat_name": short_names_dat.get(entry_id),
"split_bucket": stable_bucket(entry_id),
}
def parse_xml(
path: Path,
entry_list: dict[str, dict[str, str]],
names_dat: dict[str, str],
short_names_dat: dict[str, str],
tree_depths: dict[str, int],
) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
rows: list[dict[str, Any]] = []
dbinfo_rows: list[dict[str, Any]] = []
with gzip.open(path, "rb") as handle:
for _, elem in ET.iterparse(handle, events=("end",)):
if elem.tag == "release":
dbinfo_rows = parse_dbinfo(elem)
elem.clear()
elif elem.tag == "interpro":
rows.append(interpro_row(elem, entry_list, names_dat, short_names_dat, tree_depths))
elem.clear()
return rows, dbinfo_rows
def build_dataset(raw_dir: Path, out_dir: Path) -> dict[str, Any]:
current_dir = raw_dir / "current_release"
entry_list = parse_entry_list(current_dir / "entry.list")
names_dat = parse_two_column_file(current_dir / "names.dat")
short_names_dat = parse_two_column_file(current_dir / "short_names.dat")
tree_depths = parse_tree_depths(current_dir / "ParentChildTreeFile.txt")
release_notes = parse_release_notes(current_dir / "release_notes.txt")
interpro2go_count = parse_interpro2go_count(current_dir / "interpro2go")
rows, dbinfo_rows = parse_xml(
current_dir / "interpro.xml.gz",
entry_list=entry_list,
names_dat=names_dat,
short_names_dat=short_names_dat,
tree_depths=tree_depths,
)
if out_dir.exists():
shutil.rmtree(out_dir)
data_dir = out_dir / "data"
metadata_dir = out_dir / "metadata"
data_dir.mkdir(parents=True, exist_ok=True)
metadata_dir.mkdir(parents=True, exist_ok=True)
df = pd.DataFrame.from_records(rows, columns=ENTRY_COLUMNS)
df = df.sort_values(["split_bucket", "interpro_id"], kind="mergesort")
train = df[df["split_bucket"].ne(0)].sort_values("interpro_id", kind="mergesort")
test = df[df["split_bucket"].eq(0)].sort_values("interpro_id", kind="mergesort")
train.to_parquet(data_dir / "train-00000-of-00001.parquet", index=False, compression="zstd")
test.to_parquet(data_dir / "test-00000-of-00001.parquet", index=False, compression="zstd")
dbinfo_df = pd.DataFrame.from_records(dbinfo_rows)
dbinfo_df.to_parquet(metadata_dir / "database_info.parquet", index=False, compression="zstd")
entry_type_counts = df["entry_type"].value_counts(dropna=False).to_dict()
member_database_counts = Counter(database for values in df["member_databases"] for database in values)
go_category_counts = Counter(category for values in df["go_categories"] for category in values)
external_database_counts = Counter(database for values in df["external_databases"] for database in values)
summary = {
"source": "LiteFold/InterPro",
"release": release_notes.get("release"),
"release_date": release_notes.get("release_date"),
"last_entry": release_notes.get("last_entry"),
"entry_rows": int(len(df)),
"database_info_rows": int(len(dbinfo_df)),
"entry_list_rows": int(len(entry_list)),
"names_dat_rows": int(len(names_dat)),
"short_names_dat_rows": int(len(short_names_dat)),
"tree_entries": int(len(tree_depths)),
"interpro2go_mapping_rows": int(interpro2go_count),
"release_notes_interpro2go_mappings": release_notes.get("interpro_to_go_mappings"),
"splits": {
"train": int(len(train)),
"test": int(len(test)),
},
"split_strategy": "deterministic sha256(interpro_id) % 10; bucket 0 is test, buckets 1-9 are train",
"entry_type_counts": {str(k): int(v) for k, v in entry_type_counts.items()},
"entries_with_go": int(df["go_count"].gt(0).sum()),
"entries_with_members": int(df["member_count"].gt(0).sum()),
"entries_with_structures": int(df["structure_count"].gt(0).sum()),
"entries_with_publications": int(df["publication_count"].gt(0).sum()),
"top_member_databases": dict(member_database_counts.most_common(20)),
"go_category_counts": dict(go_category_counts.most_common()),
"top_external_databases": dict(external_database_counts.most_common(20)),
"columns": ENTRY_COLUMNS,
"source_files_used": [
"current_release/interpro.xml.gz",
"current_release/entry.list",
"current_release/names.dat",
"current_release/short_names.dat",
"current_release/ParentChildTreeFile.txt",
"current_release/interpro2go",
"current_release/release_notes.txt",
],
}
(out_dir / "dataset_summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
return summary
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--raw-dir", type=Path, default=Path("LiteFold_InterPro_raw"))
parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_InterPro_processed"))
args = parser.parse_args()
summary = build_dataset(args.raw_dir, args.out_dir)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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