def insert_isoforms(src_entries: str, pro_url: str, stg_url: str):
entries = loadobj(src_entries)
con = MySQLdb.connect(**url2dict(stg_url))
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_varsplic")
cur.execute("""
CREATE TABLE webfront_varsplic
(
accession VARCHAR(20) PRIMARY KEY NOT NULL,
protein_acc VARCHAR(15) NOT NULL,
length INT(11) NOT NULL,
sequence LONGTEXT NOT NULL,
features LONGTEXT
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
sql = """
INSERT INTO webfront_varsplic VALUES (%s, %s, %s, %s, %s)
"""
with Table(con, sql) as table:
for accession, variant in ippro.get_isoforms(pro_url).items():
features = {}
for entry_acc, locations in variant["matches"].items():
entry = entries[entry_acc]
features[entry_acc] = {
"accession": entry_acc,
"integrated": entry.integrated_in,
"name": entry.name,
"type": entry.type.lower(),
"source_database": entry.database,
"locations": locations
}
table.insert((accession, variant["protein_acc"], variant["length"],
variant["sequence"], jsonify(features)))
con.commit()
cur = con.cursor()
cur.execute("""
CREATE INDEX i_varsplic
ON webfront_varsplic (protein_acc)
""")
cur.close()
con.close()
def insert_release_notes(p_entries: str, p_proteins: str, p_proteomes: str,
p_structures: str, p_taxonomy: str,
p_uniprot2matches: str, p_uniprot2proteome: str,
rel_url: str, stg_url: str, relfile: str):
logger.info("preparing data")
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc in entry["proteins"]:
try:
uniprot2pdbe[uniprot_acc].add(pdb_id)
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id}
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("""
SELECT name_long, version
FROM webfront_database
WHERE name_long IN ('UniProtKB', 'UniProtKB/Swiss-Prot', 'UniProtKB/TrEMBL')
""")
uniprot = {}
for name, version in cur:
uniprot[name] = {
"version": version,
"count": 0,
"signatures": 0,
"integrated_signatures": 0
}
cur.close()
con.close()
entries = loadobj(p_entries)
proteins = Store(p_proteins)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
# Entities found in InterPro
integrated_proteomes = set()
integrated_structures = set()
integrated_taxonomy = set()
# Number of proteins with GO terms from InterPro
uniprot2go = 0
logger.info("starting")
i = 0
for uniprot_acc, info in proteins.items():
i += 1
if not i % 10000000:
logger.info(f"{i:>12,}")
if info["reviewed"]:
database = uniprot["UniProtKB/Swiss-Prot"]
else:
database = uniprot["UniProtKB/TrEMBL"]
database["count"] += 1
try:
matches = u2matches[uniprot_acc]
except KeyError:
# No matches
continue
# Protein matched by at least one signature
database["signatures"] += 1
is_integrated = False
for entry_acc in matches:
entry = entries[entry_acc]
if entry.database == "interpro":
"""
Protein matched by at least one InterPro entry,
i.e. at least one integrated signature
"""
is_integrated = True
if entry.go_terms:
uniprot2go += 1
break
if is_integrated:
database["integrated_signatures"] += 1
try:
proteome_id = u2proteome[uniprot_acc]
except KeyError:
pass
else:
integrated_proteomes.add(proteome_id)
try:
pdb_ids = uniprot2pdbe[uniprot_acc]
except KeyError:
pass
else:
integrated_structures |= pdb_ids
integrated_taxonomy.add(info["taxid"])
proteins.close()
u2matches.close()
u2proteome.close()
logger.info(f"{i:>12,}")
# Sum Swiss-Prot and TrEMBL counts
for key in ["count", "signatures", "integrated_signatures"]:
value_sp = uniprot["UniProtKB/Swiss-Prot"][key]
value_tr = uniprot["UniProtKB/TrEMBL"][key]
uniprot["UniProtKB"][key] = value_sp + value_tr
logger.info("tracking changes since last releases")
con = MySQLdb.connect(**url2dict(rel_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("""
SELECT accession, source_database, integrated_id
FROM webfront_entry
WHERE is_alive = 1
""")
public_entries = set()
public_integrated = set()
for entry_acc, database, integrated_in in cur:
if database == "interpro":
public_entries.add(entry_acc)
elif integrated_in:
# Signature already integrated in the previous release
public_integrated.add(entry_acc)
cur.execute("""
SELECT name, version
FROM webfront_database
WHERE type = 'entry'
""")
public_databases = dict(cur.fetchall())
cur.execute("SELECT * FROM webfront_release_note")
prev_releases = cur.fetchall()
cur.close()
con.close()
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_release_note")
cur.execute("""
CREATE TABLE webfront_release_note
(
version VARCHAR(20) PRIMARY KEY NOT NULL,
release_date DATETIME NOT NULL,
content LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.executemany(
"""
INSERT INTO webfront_release_note
VALUES (%s, %s, %s)
""", prev_releases)
con.commit()
prev_releases = None
cur.execute("""
SELECT name, name_long, version, release_date
FROM webfront_database
WHERE type = 'entry'
""")
staging_databases = {row[0]: (row[1], row[2], row[3]) for row in cur}
interpro_new = []
interpro_types = {}
member_databases = {}
pubmed_citations = set()
interpro2go = 0
latest_entry = None
for entry in sorted(loadobj(p_entries).values(),
key=lambda e: e.creation_date):
if entry.is_deleted:
continue
if entry.database == "interpro":
for pub in entry.literature.values():
if pub["PMID"] is not None:
pubmed_citations.add(pub["PMID"])
try:
interpro_types[entry.type.lower()] += 1
except KeyError:
interpro_types[entry.type.lower()] = 1
if entry.accession not in public_entries:
interpro_new.append(entry.accession)
interpro2go += len(entry.go_terms)
latest_entry = entry.accession
else:
try:
obj = member_databases[entry.database]
except KeyError:
database, version, _ = staging_databases[entry.database]
is_new = is_updated = False
if entry.database not in public_databases:
is_new = True
elif version != public_databases[entry.database]:
is_updated = True
obj = member_databases[entry.database] = {
"name": database,
"version": version,
"signatures": 0,
"integrated_signatures": 0,
"recently_integrated": [],
"is_new": is_new,
"is_updated": is_updated,
"sets": set()
}
obj["signatures"] += 1
if entry.integrated_in:
obj["integrated_signatures"] += 1
if entry.accession not in public_integrated:
# Recent integration
obj["recently_integrated"].append(entry.accession)
if entry.clan:
obj["sets"].add(entry.clan["accession"])
# Transform sets of clans to counts:
for obj in member_databases.values():
obj["sets"] = len(obj["sets"])
structures = list(loadobj(p_structures).values())
proteomes = set(loadobj(p_proteomes).keys())
errors = integrated_proteomes - proteomes
if errors:
raise RuntimeError(f"{len(errors)} invalid proteomes")
taxa = set(loadobj(p_taxonomy).keys())
errors = integrated_taxonomy - taxa
if errors:
raise RuntimeError(f"{len(errors)} invalid taxa")
content = {
"notes": [], # TODO implement way to pass custom notes
"interpro": {
"entries": sum(interpro_types.values()),
"new_entries": interpro_new,
"latest_entry": latest_entry,
"types": interpro_types,
"go_terms": interpro2go
},
"member_databases": member_databases,
"proteins": uniprot,
"structures": {
"total":
len(structures),
"integrated":
len(integrated_structures),
"version":
max(entry["date"] for entry in structures).strftime("%Y-%m-%d")
},
"proteomes": {
"total": len(proteomes),
"integrated": len(integrated_proteomes),
"version": uniprot["UniProtKB"]["version"]
},
"taxonomy": {
"total": len(taxa),
"integrated": len(integrated_taxonomy),
"version": uniprot["UniProtKB"]["version"]
},
"citations": len(pubmed_citations)
}
_, version, date = staging_databases["interpro"]
cur.execute(
"""
SELECT COUNT(*)
FROM webfront_release_note
WHERE version = %s
""", (version, ))
n_rows, = cur.fetchone()
if n_rows:
cur.execute(
"""
UPDATE webfront_release_note
SET content = %s
WHERE version = %s
""", (json.dumps(content), version))
else:
cur.execute(
"""
INSERT INTO webfront_release_note
VALUES (%s, %s, %s)
""", (version, date, json.dumps(content)))
con.commit()
cur.close()
con.close()
with open(relfile, "wt") as fh:
new_integrated = 0
dbs_integrated = []
for db in sorted(member_databases.values(), key=lambda x: x["name"]):
cnt = len(db["recently_integrated"])
if cnt:
new_integrated += cnt
dbs_integrated.append(f"{db['name']} ({cnt})")
if new_integrated:
integr_str = (f" integrates {new_integrated} new methods from "
f"the {', '.join(dbs_integrated)} databases, and")
else:
integr_str = ""
u_ver = uniprot["UniProtKB"]["version"]
u_integ = uniprot["UniProtKB"]["integrated_signatures"]
u_total = uniprot["UniProtKB"]["count"]
u_cov = round(u_integ / u_total * 100, 1)
fh.write(f"""\
Title
-----
New releases: InterPro {version} and InterProScan 5.??-{version}
Image: alternate text
---------------------
InterPro: protein sequence analysis & classification
Image: title
------------
InterPro: protein sequence analysis & classification
Summary
-------
InterPro version {version} and InterProScan 5.??-{version} are now available! \
InterPro now features hundreds of new methods integrated \
from partner databases, and InterProScan draws on over \
{sum(interpro_types.values())//1000*1000} entries.
Body
----
<h3>
<a href="http://www.ebi.ac.uk/interpro/">InterPro version {version}</a>
</h3>
<p>
<a href="http://www.ebi.ac.uk/interpro/">InterPro {version}</a>\
{integr_str} covers {u_cov}% of UniProt Knowledgebase release {u_ver}. \
It predicts <a href="http://www.geneontology.org/">Gene Ontology</a> \
(GO) terms for over {uniprot2go/1e6:.0f} million UniProt proteins \
via the InterPro2GO pipeline.
</p>
<p>
The new release includes an update to UniParc (uniparc_match.tar.gz) \
matches to InterPro methods. You can find this on our ftp site: \
<a href="ftp://ftp.ebi.ac.uk/pub/databases/interpro">ftp://ftp.ebi.ac.uk/pub/databases/interpro</a>.
</p>
<p>
For full details, see <a href="//www.ebi.ac.uk/interpro/release_notes/">the latest InterPro Release Notes</a>.
</p>
<h3>
<a href="https://github.com/ebi-pf-team/interproscan">InterProScan 5.??-{version}</a>
</h3>
<p>
InterProScan 5.??-{version} uses data from the newly released InterPro {version}, \
which contains {sum(interpro_types.values()):,} entries. \
You can find the <a href="https://interproscan-docs.readthedocs.io/en/latest/ReleaseNotes.html">full release notes here</a>.
</p>
<p>
If you need help with InterPro or InterProScan, please contact us using \
<a href="http://www.ebi.ac.uk/support/interpro">our support form</a> - \
your message will reach everyone on the team.
</p>
Meta fields: description
------------------------
We are pleased to announce the release of InterPro {version} \
and InterProScan 5.??-{version}!
Meta fields: tags
-----------------
Protein families, InterProScan, InterPro, Protein, \
protein family, protein motif
URL alias
---------
about/news/service-news/InterPro-{version}
""")
logger.info("complete")
def export_structure_matches(url: str, p_proteins: str, p_structures: str,
outdir: str):
shutil.copy(os.path.join(os.path.dirname(__file__), "feature.dtd"), outdir)
logger.info("loading structures")
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc, chains in entry["proteins"].items():
try:
uniprot2pdbe[uniprot_acc][pdb_id] = chains
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id: chains}
logger.info("loading CATH/SCOP domains")
uni2prot2cath = pdbe.get_cath_domains(url)
uni2prot2scop = pdbe.get_scop_domains(url)
logger.info("writing file")
output = os.path.join(outdir, "feature.xml.gz")
with gzip.open(output, "wt", encoding="utf-8") as fh:
fh.write('<?xml version="1.0" encoding="UTF-8"?>\n')
fh.write('<!DOCTYPE interprofeature SYSTEM "feature.dtd">\n')
fh.write('<interprofeature>\n')
with Store(p_proteins) as proteins:
doc = getDOMImplementation().createDocument(None, None, None)
for uniprot_acc, protein in proteins.items():
pdb_entries = uniprot2pdbe.get(uniprot_acc, {})
cath_entries = uni2prot2cath.get(uniprot_acc, {})
scop_entries = uni2prot2scop.get(uniprot_acc, {})
if pdb_entries or cath_entries or scop_entries:
elem = doc.createElement("protein")
elem.setAttribute("id", uniprot_acc)
elem.setAttribute("name", protein["identifier"])
elem.setAttribute("length", str(protein["length"]))
elem.setAttribute("crc64", protein["crc64"])
for pdb_id in sorted(pdb_entries):
chains = pdb_entries[pdb_id]
for chain_id in sorted(chains):
domain = doc.createElement("domain")
domain.setAttribute("id", f"{pdb_id}{chain_id}")
domain.setAttribute("dbname", "PDB")
for loc in chains[chain_id]:
start = loc["protein_start"]
end = loc["protein_end"]
coord = doc.createElement("coord")
coord.setAttribute("pdb", pdb_id)
coord.setAttribute("chain", chain_id)
coord.setAttribute("start", str(start))
coord.setAttribute("end", str(end))
domain.appendChild(coord)
elem.appendChild(domain)
for domain_id in sorted(cath_entries):
entry = cath_entries[domain_id]
domain = doc.createElement("domain")
domain.setAttribute("id", domain_id)
domain.setAttribute("cfn", entry["superfamily"]["id"])
domain.setAttribute("dbname", "CATH")
for loc in entry["locations"]:
coord = doc.createElement("coord")
coord.setAttribute("pdb", entry["pdb_id"])
coord.setAttribute("chain", entry["chain"])
coord.setAttribute("start", str(loc["start"]))
coord.setAttribute("end", str(loc["end"]))
domain.appendChild(coord)
elem.appendChild(domain)
for domain_id in sorted(scop_entries):
entry = scop_entries[domain_id]
domain = doc.createElement("domain")
domain.setAttribute("id", domain_id)
domain.setAttribute("cfn", entry["superfamily"]["id"])
domain.setAttribute("dbname", "SCOP")
for loc in entry["locations"]:
coord = doc.createElement("coord")
coord.setAttribute("pdb", entry["pdb_id"])
coord.setAttribute("chain", entry["chain"])
coord.setAttribute("start", str(loc["start"]))
coord.setAttribute("end", str(loc["end"]))
domain.appendChild(coord)
elem.appendChild(domain)
elem.writexml(fh, addindent=" ", newl="\n")
fh.write('</interprofeature>\n')
logger.info("complete")
def export_interpro(url: str,
p_entries: str,
p_entry2xrefs: str,
p_interpro2taxonomy: str,
outdir: str,
tmpdir: Optional[str] = None):
shutil.copy(os.path.join(os.path.dirname(__file__), "interpro.dtd"),
outdir)
logger.info("loading entries")
entries = loadobj(p_entries)
interpro_entries = []
deleted_entries = []
for e in entries.values():
if e.database != "interpro":
continue
elif e.is_deleted:
deleted_entries.append(e.accession)
else:
interpro_entries.append(e.accession)
logger.info("creating entry-taxon database")
fd, taxdb = mkstemp(dir=tmpdir)
os.close(fd)
os.remove(taxdb)
with DumpFile(p_interpro2taxonomy) as interpro2taxonomy:
with KVdb(taxdb, writeback=True) as kvdb:
i = 0
for entry_acc, taxon_id, counts in interpro2taxonomy:
kvdb[f"{entry_acc}-{taxon_id}"] = str(counts)
i += 1
if not i % 1000000:
kvdb.sync()
logger.info("loading protein counts")
con = MySQLdb.connect(**url2dict(url), charset="utf8mb4")
cur = MySQLdb.cursors.SSCursor(con)
cur.execute("""
SELECT accession, counts
FROM webfront_entry
""")
num_proteins = {}
for entry_acc, counts in cur:
num_proteins[entry_acc] = str(json.loads(counts)["proteins"])
output = os.path.join(outdir, "interpro.xml.gz")
with gzip.open(output, "wt", encoding="utf-8") as fh:
fh.write('<?xml version="1.0" encoding="UTF-8"?>\n')
fh.write('<!DOCTYPE interprodb SYSTEM "interpro.dtd">\n')
fh.write("<interprodb>\n")
doc = getDOMImplementation().createDocument(None, None, None)
# writing <release> section (do not log progress, < 1 sec)
elem = doc.createElement("release")
databases = {}
cur.execute("""
SELECT name, name_alt, type, num_entries, version, release_date
FROM webfront_database
ORDER BY name_long
""")
for name, name_alt, db_type, entry_count, version, date in cur:
databases[name] = name_alt
if db_type in ("entry", "protein"):
dbinfo = doc.createElement("dbinfo")
dbinfo.setAttribute("version", version)
dbinfo.setAttribute("dbname", name_alt)
dbinfo.setAttribute("entry_count", str(entry_count))
dbinfo.setAttribute("file_date",
date.strftime("%d-%b-%y").upper())
elem.appendChild(dbinfo)
elem.writexml(fh, addindent=" ", newl="\n")
logger.info("loading taxonomic data")
key_species = {
"3702", # Arabidopsis thaliana
"6239", # Caenorhabditis elegans
"7955", # Danio rerio
"7227", # Drosophila melanogaster
"9606", # H**o sapiens
"10090", # Mus musculus
"367110", # Neurospora crassa
"10116", # Rattus norvegicus
"559292", # Saccharomyces cerevisiae
"284812", # Schizosaccharomyces pombe
"4577", # Zea mays
}
superkingdoms = {
"Archaea": None,
"Bacteria": None,
"Eukaryota": None,
"Viruses": None
}
cur.execute("""
SELECT accession, scientific_name, full_name, lineage
FROM webfront_taxonomy
""")
taxa = {}
for tax_id, sci_name, full_name, lineage in cur:
"""
lineage stored as a string with heading/leading whitespaces,
and a whitespace between taxa
"""
taxa[tax_id] = (full_name, lineage.strip().split())
if sci_name in superkingdoms:
superkingdoms[sci_name] = tax_id
cur.close()
con.close()
# Raise if a superkingdom is not in the table
for sci_name, tax_id in superkingdoms.items():
if tax_id is None:
raise ValueError(f"{sci_name}: missing taxon ID")
superkingdoms = {tax_id for tax_id in superkingdoms.values()}
logger.info("writing entries")
with DumpFile(p_entry2xrefs) as entry2xrefs, KVdb(taxdb) as kvdb:
for entry_acc, xrefs in entry2xrefs:
entry = entries[entry_acc]
if entry.database != "interpro" or entry.is_deleted:
continue
elem = doc.createElement("interpro")
elem.setAttribute("id", entry.accession)
elem.setAttribute("protein_count", num_proteins[entry_acc])
elem.setAttribute("short_name", entry.short_name)
elem.setAttribute("type", entry.type)
name = doc.createElement("name")
name.appendChild(doc.createTextNode(entry.name))
elem.appendChild(name)
text = _restore_abstract('\n'.join(entry.description))
try:
_doc = parseString(f"<abstract>{text}</abstract>")
except ExpatError as exc:
# TODO: use CDATA section for all entries
logger.warning(f"{entry_acc}: {exc}")
# abstract = doc.createElement("abstract")
# abstract.appendChild(doc.createCDATASection(text))
else:
abstract = _doc.documentElement
elem.appendChild(abstract)
if entry.go_terms:
go_list = doc.createElement("class_list")
for term in entry.go_terms:
go_elem = doc.createElement("classification")
go_elem.setAttribute("id", term["identifier"])
go_elem.setAttribute("class_type", "GO")
_elem = doc.createElement("category")
_elem.appendChild(
doc.createTextNode(term["category"]["name"]))
go_elem.appendChild(_elem)
_elem = doc.createElement("description")
_elem.appendChild(doc.createTextNode(term["name"]))
go_elem.appendChild(_elem)
go_list.appendChild(go_elem)
elem.appendChild(go_list)
if entry.literature:
pub_list = doc.createElement("pub_list")
for pub_id in sorted(entry.literature):
pub = entry.literature[pub_id]
pub_elem = doc.createElement("publication")
pub_elem.setAttribute("id", pub_id)
_elem = doc.createElement("author_list")
if pub["authors"]:
_elem.appendChild(
doc.createTextNode(", ".join(pub['authors'])))
else:
_elem.appendChild(doc.createTextNode("Unknown"))
pub_elem.appendChild(_elem)
if pub["title"]:
_elem = doc.createElement("title")
_elem.appendChild(doc.createTextNode(pub["title"]))
pub_elem.appendChild(_elem)
if pub["URL"]:
_elem = doc.createElement("url")
_elem.appendChild(doc.createTextNode(pub["URL"]))
pub_elem.appendChild(_elem)
_elem = doc.createElement("db_xref")
if pub["PMID"]:
_elem.setAttribute("db", "PUBMED")
_elem.setAttribute("dbkey", str(pub["PMID"]))
else:
_elem.setAttribute("db", "MEDLINE")
_elem.setAttribute("dbkey", "MEDLINE")
pub_elem.appendChild(_elem)
if pub["ISO_journal"]:
_elem = doc.createElement("journal")
_elem.appendChild(
doc.createTextNode(pub["ISO_journal"]))
pub_elem.appendChild(_elem)
if pub["ISBN"]:
_elem = doc.createElement("book_title")
isbn = f"ISBN:{pub['ISBN']}"
_elem.appendChild(doc.createTextNode(isbn))
pub_elem.appendChild(_elem)
if pub["volume"] or pub["issue"] or pub["raw_pages"]:
_elem = doc.createElement("location")
if pub["volume"]:
_elem.setAttribute("volume", pub["volume"])
if pub["issue"]:
_elem.setAttribute("issue", pub["issue"])
if pub["raw_pages"]:
_elem.setAttribute("pages", pub["raw_pages"])
pub_elem.appendChild(_elem)
if pub["year"]:
_elem = doc.createElement("year")
_elem.appendChild(
doc.createTextNode(str(pub["year"])))
pub_elem.appendChild(_elem)
pub_list.appendChild(pub_elem)
elem.appendChild(pub_list)
parent, children = entry.relations
if parent:
par_elem = doc.createElement("parent_list")
_elem = doc.createElement("rel_ref")
_elem.setAttribute("ipr_ref", parent)
par_elem.appendChild(_elem)
elem.appendChild(par_elem)
if children:
child_list = doc.createElement("child_list")
for child in children:
_elem = doc.createElement("rel_ref")
_elem.setAttribute("ipr_ref", child)
child_list.appendChild(_elem)
elem.appendChild(child_list)
members = []
for database, signatures in entry.integrates.items():
for signature_acc in signatures:
members.append((
signature_acc,
entries[signature_acc].short_name,
database,
num_proteins[signature_acc],
))
mem_list = doc.createElement("member_list")
for member in sorted(members):
_elem = doc.createElement("db_xref")
_elem.setAttribute("protein_count", member[3])
_elem.setAttribute("db", databases[member[2]])
_elem.setAttribute("dbkey", member[0])
_elem.setAttribute("name", member[1])
mem_list.appendChild(_elem)
elem.appendChild(mem_list)
# Merge cross-references and pathways
cross_refs = {}
for key, values in entry.cross_references.items():
cross_refs[databases[key]] = values
for key, values in entry.pathways.items():
cross_refs[databases[key]] = [val["id"] for val in values]
if cross_refs:
xref_list = doc.createElement("external_doc_list")
for ref_db in sorted(cross_refs):
for ref_id in sorted(cross_refs[ref_db]):
_elem = doc.createElement("db_xref")
_elem.setAttribute("db", ref_db)
_elem.setAttribute("dbkey", ref_id)
xref_list.appendChild(_elem)
elem.appendChild(xref_list)
if xrefs["structures"]:
xref_list = doc.createElement("structure_db_links")
for pdb_id in sorted(xrefs["structures"]):
_elem = doc.createElement("db_xref")
_elem.setAttribute("db", "PDB")
_elem.setAttribute("dbkey", pdb_id)
xref_list.appendChild(_elem)
elem.appendChild(xref_list)
# Find key species and taxonomic distribution
entry_key_species = []
entry_superkingdoms = {}
for tax_id in xrefs["taxa"]:
full_name, lineage = taxa[tax_id]
if tax_id in key_species:
entry_key_species.append((full_name, tax_id))
# Find the superkingdom contain this taxon
for superkingdom_id in superkingdoms:
if superkingdom_id in lineage:
break
else:
continue
try:
other_lineage = entry_superkingdoms[superkingdom_id]
except KeyError:
entry_superkingdoms[superkingdom_id] = lineage
else:
# Compare lineages and find lowest common ancestor
i = 0
while i < len(lineage) and i < len(other_lineage):
if lineage[i] != other_lineage[i]:
break
i += 1
# Path to the lowest common ancestor
entry_superkingdoms[superkingdom_id] = lineage[:i]
# Get lowest common ancestor for each represented superkingdom
lowest_common_ancestors = []
for lineage in entry_superkingdoms.values():
# Lowest common ancestor
tax_id = lineage[-1]
full_name, _ = taxa[tax_id]
lowest_common_ancestors.append((full_name, tax_id))
# Write taxonomic distribution
tax_dist = doc.createElement("taxonomy_distribution")
for full_name, tax_id in sorted(lowest_common_ancestors):
_elem = doc.createElement("taxon_data")
_elem.setAttribute("name", full_name)
key = f"{entry_acc}-{tax_id}"
_elem.setAttribute("proteins_count", kvdb[key])
tax_dist.appendChild(_elem)
elem.appendChild(tax_dist)
if entry_key_species:
# Write key species
key_spec = doc.createElement("key_species")
for full_name, tax_id in sorted(entry_key_species):
_elem = doc.createElement("taxon_data")
_elem.setAttribute("name", full_name)
key = f"{entry_acc}-{tax_id}"
_elem.setAttribute("proteins_count", kvdb[key])
key_spec.appendChild(_elem)
elem.appendChild(key_spec)
elem.writexml(fh, addindent=" ", newl="\n")
if deleted_entries:
block = doc.createElement("deleted_entries")
for entry_acc in sorted(deleted_entries):
elem = doc.createElement("del_ref")
elem.setAttribute("id", entry_acc)
block.appendChild(elem)
block.writexml(fh, addindent=" ", newl="\n")
fh.write("</interprodb>\n")
logger.info(f"temporary file: {os.path.getsize(taxdb)/1024/1024:,.0f} MB")
os.remove(taxdb)
logger.info("complete")
def insert_entries(pfam_url: str, stg_url: str, p_entries: str,
p_entry2xrefs: str):
logger.info("fetching Wikipedia data for Pfam entries")
wiki = pfam.get_wiki(pfam_url)
logger.info("loading Pfam curation/family details")
pfam_details = pfam.get_details(pfam_url)
logger.info("populating webfront_entry")
entries = loadobj(p_entries)
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_entry")
cur.execute("""
CREATE TABLE webfront_entry
(
entry_id VARCHAR(10) DEFAULT NULL,
accession VARCHAR(25) PRIMARY KEY NOT NULL,
type VARCHAR(50) NOT NULL,
name LONGTEXT,
short_name VARCHAR(100),
source_database VARCHAR(10) NOT NULL,
member_databases LONGTEXT,
integrated_id VARCHAR(25),
go_terms LONGTEXT,
description LONGTEXT,
wikipedia LONGTEXT,
details LONGTEXT,
literature LONGTEXT,
hierarchy LONGTEXT,
cross_references LONGTEXT,
interactions LONGTEXT,
pathways LONGTEXT,
overlaps_with LONGTEXT,
is_featured TINYINT NOT NULL,
is_alive TINYINT NOT NULL,
history LONGTEXT,
entry_date DATETIME NOT NULL,
deletion_date DATETIME,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
# Count number of structural models per entry
cur.execute("""
SELECT accession, COUNT(*)
FROM webfront_structuralmodel
GROUP BY accession
""")
num_struct_models = dict(cur.fetchall())
cur.close()
sql = """
INSERT INTO webfront_entry
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
%s, %s, %s, %s, %s, %s, %s, %s, %s)
"""
with Table(con, sql) as table:
with DumpFile(p_entry2xrefs) as df:
for accession, xrefs in df:
entry = entries[accession]
counts = reduce(xrefs)
counts.update({
"interactions":
len(entry.ppi),
"pathways":
sum([len(v) for v in entry.pathways.values()]),
"sets":
1 if entry.clan else 0,
"structural_models":
num_struct_models.get(accession, 0)
})
table.insert(
(None, accession, entry.type.lower(),
entry.name, entry.short_name, entry.database,
jsonify(entry.integrates), entry.integrated_in,
jsonify(entry.go_terms), jsonify(entry.description),
jsonify(wiki.get(accession)),
jsonify(pfam_details.get(accession)),
jsonify(entry.literature), jsonify(entry.hierarchy),
jsonify(entry.cross_references), jsonify(entry.ppi),
jsonify(entry.pathways), jsonify(entry.overlaps_with), 0,
0 if entry.is_deleted else 1, jsonify(entry.history),
entry.creation_date, entry.deletion_date,
jsonify(counts)))
con.commit()
con.close()
logger.info("complete")
def insert_proteomes(p_entries: str, p_proteins: str, p_proteomes: str,
p_structures: str, p_uniprot2ida: str,
p_uniprot2matches: str, p_uniprot2proteome: str,
stg_url: str):
logger.info("preparing data")
proteomes = loadobj(p_proteomes)
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc in entry["proteins"]:
try:
uniprot2pdbe[uniprot_acc].add(pdb_id)
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id}
# Init all proteomes
xrefs = {}
for proteome_id in proteomes:
xrefs[proteome_id] = {
"domain_architectures": set(),
"entries": {},
"proteins": 0,
"sets": set(),
"structures": set(),
"taxa": set()
}
entries = loadobj(p_entries)
proteins = Store(p_proteins)
u2ida = Store(p_uniprot2ida)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
logger.info("starting")
i = 0
for uniprot_acc, proteome_id in u2proteome.items():
proteome = xrefs[proteome_id]
proteome["proteins"] += 1
info = proteins[uniprot_acc]
proteome["taxa"].add(info["taxid"])
try:
dom_members, dom_arch, dom_arch_id = u2ida[uniprot_acc]
except KeyError:
pass
else:
proteome["domain_architectures"].add(dom_arch_id)
for entry_acc in u2matches.get(uniprot_acc, []):
entry = entries[entry_acc]
try:
proteome["entries"][entry.database].add(entry_acc)
except KeyError:
proteome["entries"][entry.database] = {entry_acc}
if entry.clan:
proteome["sets"].add(entry.clan["accession"])
try:
pdb_ids = uniprot2pdbe[uniprot_acc]
except KeyError:
pass
else:
proteome["structures"] |= pdb_ids
i += 1
if not i % 10000000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
proteins.close()
u2ida.close()
u2matches.close()
u2proteome.close()
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_proteome")
cur.execute("""
CREATE TABLE webfront_proteome
(
accession VARCHAR(20) PRIMARY KEY NOT NULL,
name VARCHAR(215) NOT NULL,
is_reference TINYINT NOT NULL,
strain VARCHAR(512),
assembly VARCHAR(512),
taxonomy_id VARCHAR(20) NOT NULL,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
sql = """
INSERT INTO webfront_proteome VALUES (%s, %s, %s, %s, %s, %s, %s)
"""
with Table(con, sql) as table:
for proteome_id, info in proteomes.items():
counts = reduce(xrefs[proteome_id])
counts["entries"]["total"] = sum(counts["entries"].values())
table.insert(
(proteome_id, info["name"], 1 if info["is_reference"] else 0,
info["strain"], info["assembly"], info["taxon_id"],
jsonify(counts)))
con.commit()
con.close()
logger.info("complete")
def insert_structures(p_entries: str, p_proteins: str, p_structures: str,
p_uniprot2ida: str, p_uniprot2matches: str,
p_uniprot2proteome: str, stg_url: str):
logger.info("preparing data")
entries = {}
for entry in loadobj(p_entries).values():
entries[entry.accession] = (entry.database, entry.clan)
uniprot2pdbe = {}
xrefs = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc, chains in entry["proteins"].items():
try:
uniprot2pdbe[uniprot_acc][pdb_id] = chains
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id: chains}
xrefs[pdb_id] = {
"domain_architectures": set(),
"entries": {},
"proteomes": set(),
"proteins": 0,
"sets": set(),
"taxa": set()
}
proteins = Store(p_proteins)
u2ida = Store(p_uniprot2ida)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
logger.info("starting")
i = 0
for uniprot_acc in sorted(uniprot2pdbe):
info = proteins[uniprot_acc]
try:
dom_members, dom_arch, dom_arch_id = u2ida[uniprot_acc]
except KeyError:
dom_arch_id = None
proteome_id = u2proteome.get(uniprot_acc)
matches = u2matches.get(uniprot_acc, {})
for pdb_id, chains in uniprot2pdbe[uniprot_acc].items():
_xrefs = xrefs[pdb_id]
if dom_arch_id:
_xrefs["domain_architectures"].add(dom_arch_id)
if proteome_id:
_xrefs["proteomes"].add(proteome_id)
_xrefs["proteins"] += 1
_xrefs["taxa"].add(info["taxid"])
for entry_acc, locations in matches.items():
database, clan = entries[entry_acc]
for chain_id, segments in chains.items():
if overlaps_pdb_chain(locations, segments):
try:
_xrefs["entries"][database].add(entry_acc)
except KeyError:
_xrefs["entries"][database] = {entry_acc}
if clan:
_xrefs["sets"].add(clan["accession"])
break # Skip other chains
i += 1
if not i % 10000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
proteins.close()
u2ida.close()
u2matches.close()
u2proteome.close()
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_structure")
cur.execute("""
CREATE TABLE webfront_structure
(
accession VARCHAR(4) PRIMARY KEY NOT NULL,
name VARCHAR(512) NOT NULL,
source_database VARCHAR(10) NOT NULL,
experiment_type VARCHAR(16) NOT NULL,
release_date DATETIME NOT NULL,
resolution FLOAT,
literature LONGTEXT,
chains LONGTEXT NOT NULL,
proteins LONGTEXT NOT NULL,
secondary_structures LONGTEXT,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
sql = """
INSERT INTO webfront_structure
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
"""
with Table(con, sql) as table:
for pdb_id, info in loadobj(p_structures).items():
counts = reduce(xrefs[pdb_id])
counts["entries"]["total"] = sum(counts["entries"].values())
table.insert((
pdb_id,
info["name"],
"pdb",
info["evidence"],
info["date"],
info["resolution"],
jsonify(info["citations"]),
# Sorted list of unique chain (e.g. 'A', 'B', ...)
jsonify(sorted({
chain_id
for chains in info["proteins"].values()
for chain_id in chains
}),
nullable=False),
jsonify(info["proteins"], nullable=False),
jsonify(info["secondary_structures"]),
jsonify(counts)))
con.commit()
con.close()
logger.info("complete")
def insert_proteins(p_entries: str, p_proteins: str, p_structures: str,
p_taxonomy: str, p_uniprot2comments: str,
p_uniprot2name: str, p_uniprot2evidences: str,
p_uniprot2ida: str, p_uniprot2matches: str,
p_uniprot2proteome: str, p_uniprot2sequence: str,
pro_url: str, stg_url: str):
logger.info("loading CATH/SCOP domains")
uniprot2cath = pdbe.get_cath_domains(pro_url)
uniprot2scop = pdbe.get_scop_domains(pro_url)
logger.info("preparing data")
proteins = Store(p_proteins)
u2comments = Store(p_uniprot2comments)
u2descriptions = Store(p_uniprot2name)
u2evidences = Store(p_uniprot2evidences)
u2ida = Store(p_uniprot2ida)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
u2sequence = Store(p_uniprot2sequence)
taxonomy = {}
for taxid, info in loadobj(p_taxonomy).items():
taxonomy[taxid] = jsonify({
"taxId": taxid,
"scientificName": info["sci_name"],
"fullName": info["full_name"]
})
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc in entry["proteins"]:
try:
uniprot2pdbe[uniprot_acc].append(pdb_id)
except KeyError:
uniprot2pdbe[uniprot_acc] = [pdb_id]
logger.info("counting proteins/IDA")
ida_count = {}
for dom_members, dom_arch, dom_arch_id in u2ida.values():
try:
ida_count[dom_arch_id] += 1
except KeyError:
ida_count[dom_arch_id] = 1
logger.info("inserting proteins")
entries = loadobj(p_entries)
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("""
SELECT protein_acc, COUNT(*)
FROM webfront_varsplic
GROUP BY protein_acc
""")
isoforms = dict(cur.fetchall())
cur.execute("DROP TABLE IF EXISTS webfront_protein")
cur.execute("""
CREATE TABLE webfront_protein
(
accession VARCHAR(15) PRIMARY KEY NOT NULL,
identifier VARCHAR(16) NOT NULL,
organism LONGTEXT NOT NULL,
name VARCHAR(255) NOT NULL,
description LONGTEXT,
sequence LONGBLOB NOT NULL,
length INT(11) NOT NULL,
proteome VARCHAR(20),
gene VARCHAR(70),
go_terms LONGTEXT,
evidence_code INT(11) NOT NULL,
source_database VARCHAR(10) NOT NULL,
is_fragment TINYINT NOT NULL,
structure LONGTEXT,
tax_id VARCHAR(20) NOT NULL,
ida_id VARCHAR(40),
ida TEXT,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
i = 0
sql = """
INSERT into webfront_protein
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
"""
with Table(con, sql) as table:
for uniprot_acc, protein_info in proteins.items():
taxid = protein_info["taxid"]
try:
taxon = taxonomy[taxid]
except KeyError:
table.close()
con.close()
raise RuntimeError(f"{uniprot_acc}: invalid taxon {taxid}")
try:
name = u2descriptions[uniprot_acc]
except KeyError:
table.close()
con.close()
raise RuntimeError(f"{uniprot_acc}: missing name")
try:
evidence, gene = u2evidences[uniprot_acc]
except KeyError:
table.close()
con.close()
raise RuntimeError(f"{uniprot_acc}: missing evidence")
try:
sequence = u2sequence[uniprot_acc]
except KeyError:
table.close()
con.close()
raise RuntimeError(f"{uniprot_acc}: missing sequence")
proteome_id = u2proteome.get(uniprot_acc)
clans = []
databases = {}
go_terms = {}
for entry_acc in u2matches.get(uniprot_acc, []):
entry = entries[entry_acc]
try:
databases[entry.database] += 1
except KeyError:
databases[entry.database] = 1
if entry.clan:
clans.append(entry.clan["accession"])
for term in entry.go_terms:
go_terms[term["identifier"]] = term
protein_structures = {}
domains = uniprot2cath.get(uniprot_acc)
if domains:
protein_structures["cath"] = {}
for dom in domains.values():
dom_id = dom["id"]
protein_structures["cath"][dom_id] = {
"domain_id": dom["superfamily"]["id"],
"coordinates": dom["locations"]
}
domains = uniprot2scop.get(uniprot_acc)
if domains:
protein_structures["scop"] = {}
for dom in domains.values():
dom_id = dom["id"]
protein_structures["scop"][dom_id] = {
"domain_id": dom["superfamily"]["id"],
"coordinates": dom["locations"]
}
try:
dom_members, dom_arch, dom_arch_id = u2ida[uniprot_acc]
except KeyError:
dom_arch = dom_arch_id = None
dom_count = 0
else:
dom_count = ida_count[dom_arch_id]
table.insert(
(uniprot_acc, protein_info["identifier"], taxon, name,
jsonify(u2comments.get(uniprot_acc)),
gzip.compress(sequence.encode("utf-8")),
protein_info["length"], proteome_id, gene,
jsonify(list(go_terms.values())), evidence,
"reviewed" if protein_info["reviewed"] else "unreviewed",
1 if protein_info["fragment"] else 0,
jsonify(protein_structures), protein_info["taxid"],
dom_arch_id, dom_arch,
jsonify({
"domain_architectures": dom_count,
"entries": databases,
"isoforms": isoforms.get(uniprot_acc, 0),
"proteomes": 1 if proteome_id else 0,
"sets": len(set(clans)),
"structures": len(uniprot2pdbe.get(uniprot_acc, [])),
"taxa": 1
})))
i += 1
if not i % 10000000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
con.commit()
proteins.close()
u2comments.close()
u2descriptions.close()
u2evidences.close()
u2ida.close()
u2matches.close()
u2proteome.close()
u2sequence.close()
logger.info("indexing")
cur = con.cursor()
cur.execute("""
CREATE UNIQUE INDEX ui_protein_identifier
ON webfront_protein (identifier)
""")
cur.execute("""
CREATE INDEX i_protein_proteome
ON webfront_protein (proteome)
""")
cur.execute("""
CREATE INDEX i_protein_database
ON webfront_protein (source_database)
""")
cur.execute("""
CREATE INDEX i_protein_taxon
ON webfront_protein (tax_id)
""")
cur.execute("""
CREATE INDEX i_protein_ida
ON webfront_protein (ida_id)
""")
cur.execute("""
CREATE INDEX i_protein_fragment
ON webfront_protein (is_fragment)
""")
cur.close()
con.close()
logger.info("complete")
def insert_taxonomy(p_entries: str,
p_proteins: str,
p_structures: str,
p_taxonomy: str,
p_uniprot2matches: str,
p_uniprot2proteome: str,
stg_url: str,
p_interpro2taxonomy: str,
tmpdir: Optional[str] = None):
logger.info("preparing data")
dt = DirectoryTree(tmpdir)
entries = loadobj(p_entries)
taxonomy = loadobj(p_taxonomy)
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc, chains in entry["proteins"].items():
try:
uniprot2pdbe[uniprot_acc][pdb_id] = chains
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id: chains}
proteins = Store(p_proteins)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
logger.info("starting")
i = 0
xrefs = {}
files = []
for uniprot_acc, info in proteins.items():
taxon_id = info["taxid"]
try:
taxon = xrefs[taxon_id]
except KeyError:
taxon = xrefs[taxon_id] = init_xrefs()
try:
proteome_id = u2proteome[uniprot_acc]
except KeyError:
pass
else:
taxon["proteomes"].add(proteome_id)
taxon["proteins"]["all"] += 1
protein_structures = uniprot2pdbe.get(uniprot_acc, {})
# Add structures to taxon, regardless of entry matches
taxon["structures"]["all"] |= set(protein_structures.keys())
databases = set()
for entry_acc, locations in u2matches.get(uniprot_acc, {}).items():
entry = entries[entry_acc]
database = entry.database
try:
taxon["entries"][database].add(entry_acc)
except KeyError:
taxon["entries"][database] = {entry_acc}
if database not in databases:
# Counting the protein *once* per database
databases.add(database)
try:
taxon["proteins"]["databases"][database] += 1
except KeyError:
taxon["proteins"]["databases"][database] = 1
try:
taxon["proteins"]["entries"][entry_acc] += 1
except KeyError:
taxon["proteins"]["entries"][entry_acc] = 1
for pdb_id, chains in protein_structures.items():
for chain_id, segments in chains.items():
if overlaps_pdb_chain(locations, segments):
try:
taxon["structures"]["entries"][entry_acc].add(
pdb_id)
except KeyError:
taxon["structures"]["entries"][entry_acc] = {
pdb_id
}
break # Skip other chains
i += 1
if not i % 1000000:
output = dt.mktemp()
dump_xrefs(xrefs, taxonomy, output)
files.append(output)
xrefs = {}
if not i % 10000000:
logger.info(f"{i:>12,}")
if xrefs:
output = dt.mktemp()
dump_xrefs(xrefs, taxonomy, output)
files.append(output)
xrefs = {}
logger.info(f"{i:>12,}")
logger.info(f"temporary files: "
f"{sum(map(os.path.getsize, files))/1024/1024:.0f} MB")
proteins.close()
u2matches.close()
u2proteome.close()
logger.info("populating taxonomy tables")
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_taxonomy")
cur.execute("""
CREATE TABLE webfront_taxonomy
(
accession VARCHAR(20) PRIMARY KEY NOT NULL,
scientific_name VARCHAR(255) NOT NULL,
full_name VARCHAR(512) NOT NULL,
lineage LONGTEXT NOT NULL,
parent_id VARCHAR(20),
rank VARCHAR(20) NOT NULL,
children LONGTEXT,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.execute("DROP TABLE IF EXISTS webfront_taxonomyperentry")
cur.execute("""
CREATE TABLE webfront_taxonomyperentry
(
id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
tax_id VARCHAR(20) NOT NULL,
entry_acc VARCHAR(25) NOT NULL,
counts LONGTEXT NULL NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.execute("DROP TABLE IF EXISTS webfront_taxonomyperentrydb")
cur.execute("""
CREATE TABLE webfront_taxonomyperentrydb
(
id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
tax_id VARCHAR(20) NOT NULL,
source_database VARCHAR(10) NOT NULL,
counts LONGTEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
table = Table(con,
query="""
INSERT INTO webfront_taxonomy VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
""")
per_entry = Table(con,
query="""
INSERT INTO webfront_taxonomyperentry (tax_id,entry_acc,counts)
VALUES (%s, %s, %s)
""")
per_database = Table(con,
query="""
INSERT INTO webfront_taxonomyperentrydb (tax_id,source_database,counts)
VALUES (%s, %s, %s)
""")
with DumpFile(p_interpro2taxonomy, compress=True) as interpro2taxonomy:
interpro_entries = {
entry.accession
for entry in entries.values()
if entry.database == "interpro" and not entry.is_deleted
}
i = 0
for taxon_id, taxon_xrefs in merge_dumps(files):
taxon = taxonomy[taxon_id]
protein_counts = taxon_xrefs.pop("proteins")
structure_counts = taxon_xrefs.pop("structures")
counts = reduce(taxon_xrefs)
# Add total protein count (not grouped by database/entry)
counts["proteins"] = protein_counts["all"]
# Add total structure count
counts["structures"] = len(structure_counts["all"])
# Add total entry count (not grouped by database)
counts["entries"]["total"] = sum(counts["entries"].values())
table.insert(
(taxon_id, taxon["sci_name"], taxon["full_name"],
f" {' '.join(taxon['lineage'])} ", taxon["parent"],
taxon["rank"], jsonify(taxon["children"]), jsonify(counts)))
# Remove the 'entry' property
# (no needed for webfront_taxonomyperentry)
entry_counts = counts.pop("entries")
database_structures = {}
for entry_acc, count in protein_counts["entries"].items():
if entry_acc in interpro_entries:
interpro2taxonomy.dump((entry_acc, taxon_id, count))
counts["proteins"] = count
try:
entry_structures = structure_counts["entries"][entry_acc]
except KeyError:
counts["structures"] = 0
else:
counts["structures"] = len(entry_structures)
database = entries[entry_acc].database
try:
database_structures[database] |= entry_structures
except KeyError:
database_structures[database] = entry_structures.copy()
finally:
per_entry.insert((taxon_id, entry_acc, jsonify(counts)))
for database, count in protein_counts["databases"].items():
counts.update({
"entries":
entry_counts[database],
"proteins":
count,
"structures":
len(database_structures.get(database, []))
})
per_database.insert((taxon_id, database, jsonify(counts)))
i += 1
if not i % 100000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
table.close()
per_entry.close()
per_database.close()
con.commit()
dt.remove()
logger.info("indexing")
cur = con.cursor()
cur.execute("""
CREATE INDEX i_webfront_taxonomyperentry_tax
ON webfront_taxonomyperentry (tax_id)
""")
cur.execute("""
CREATE INDEX i_webfront_taxonomyperentry_entry
ON webfront_taxonomyperentry (entry_acc)
""")
cur.execute("""
CREATE INDEX i_webfront_taxonomyperentrydb_tax
ON webfront_taxonomyperentrydb (tax_id)
""")
cur.execute("""
CREATE INDEX i_webfront_taxonomyperentrydb_database
ON webfront_taxonomyperentrydb (source_database)
""")
cur.close()
con.close()
logger.info("complete")
def export(p_entries: str, p_uniprot2matches: str, outdir: str):
logger.info("loading entries")
entries = []
integrated = {}
for e in loadobj(p_entries).values():
if e.database == "interpro" and not e.is_deleted:
entries.append(e)
for signatures in e.integrates.values():
for signature_acc in signatures:
integrated[signature_acc] = (e.accession, e.name)
logger.info("writing entry.list")
with open(os.path.join(outdir, "entry.list"), "wt") as fh:
fh.write("ENTRY_AC\tENTRY_TYPE\tENTRY_NAME\n")
for e in sorted(entries, key=lambda e: (e.type, e.accession)):
fh.write(f"{e.accession}\t{e.type}\t{e.name}\n")
logger.info("writing names.dat")
with open(os.path.join(outdir, "names.dat"), "wt") as fh:
for e in sorted(entries, key=lambda e: e.accession):
fh.write(f"{e.accession}\t{e.name}\n")
logger.info("writing short_names.dat")
with open(os.path.join(outdir, "short_names.dat"), "wt") as fh:
for e in sorted(entries, key=lambda e: e.accession):
fh.write(f"{e.accession}\t{e.short_name}\n")
logger.info("writing interpro2go")
with open(os.path.join(outdir, "interpro2go"), "wt") as fh:
fh.write(f"!date: {datetime.now():%Y/%m/%d %H:%M:%S}\n")
fh.write("!Mapping of InterPro entries to GO\n")
fh.write("!\n")
for e in sorted(entries, key=lambda e: e.accession):
for term in e.go_terms:
fh.write(f"InterPro:{e.accession} {e.name} > "
f"GO:{term['name']} ; {term['identifier']}\n")
logger.info("writing ParentChildTreeFile.txt")
with open(os.path.join(outdir, "ParentChildTreeFile.txt"), "wt") as fh:
for e in sorted(entries, key=lambda e: e.accession):
root = e.hierarchy["accession"]
if root == e.accession and e.hierarchy["children"]:
_write_node(e.hierarchy, fh, level=0)
logger.info("writing protein2ipr.dat.gz")
filepath = os.path.join(outdir, "protein2ipr.dat.gz")
with gzip.open(filepath, "wt") as fh, Store(p_uniprot2matches) as sh:
i = 0
for uniprot_acc, protein_entries in sh.items():
matches = []
for signature_acc in sorted(protein_entries):
try:
interpro_acc, name = integrated[signature_acc]
except KeyError:
# Not integrated signature or InterPro entry
continue
locations = protein_entries[signature_acc]
for loc in locations:
matches.append((
uniprot_acc,
interpro_acc,
name,
signature_acc,
# We do not consider fragmented locations
loc["fragments"][0]["start"],
max(f["end"] for f in loc["fragments"])
))
for m in sorted(matches):
fh.write('\t'.join(map(str, m)) + '\n')
i += 1
if not i % 10000000:
logger.debug(f"{i:>12,}")
logger.info(f"{i:>12,}")
logger.info("complete")
def insert_clans(stg_url: str, p_alignments: str, p_clans: str, p_entries: str,
p_entry2xrefs: str, **kwargs):
max_xrefs = kwargs.get("max_xrefs", 1000000)
tmpdir = kwargs.get("tmpdir")
logger.info("aggregating clan cross-references")
dt = DirectoryTree(tmpdir)
entry2clan = {}
for entry_acc, entry in loadobj(p_entries).items():
if entry.clan:
entry2clan[entry_acc] = entry.clan["accession"]
clans = {}
files = []
num_xrefs = 0
with DumpFile(p_entry2xrefs) as df:
for entry_acc, entry_xrefs in df:
try:
clan_acc = entry2clan[entry_acc]
except KeyError:
continue
try:
clan_xrefs = clans[clan_acc]
except KeyError:
clan_xrefs = clans[clan_acc] = {}
# We do not need the number of matches
del entry_xrefs["matches"]
cnt_before = sum(map(len, clan_xrefs.values()))
deepupdate(entry_xrefs, clan_xrefs)
cnt_after = sum(map(len, clan_xrefs.values()))
num_xrefs += cnt_after - cnt_before
if num_xrefs >= max_xrefs:
file = dt.mktemp()
with DumpFile(file, compress=True) as df2:
for clan_acc in sorted(clans):
df2.dump((clan_acc, clans[clan_acc]))
files.append(file)
clans = {}
num_xrefs = 0
file = dt.mktemp()
with DumpFile(file, compress=True) as df2:
for clan_acc in sorted(clans):
df2.dump((clan_acc, clans[clan_acc]))
files.append(file)
logger.info("inserting clans")
clans = loadobj(p_clans)
con = MySQLdb.connect(**url2dict(stg_url), charset="utf8mb4")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_set")
cur.execute("""
CREATE TABLE webfront_set
(
accession VARCHAR(20) PRIMARY KEY NOT NULL,
name VARCHAR(400),
description TEXT,
source_database VARCHAR(10) NOT NULL,
relationships LONGTEXT NOT NULL,
authors TEXT,
literature TEXT,
counts LONGTEXT DEFAULT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
sql = """
INSERT INTO webfront_set
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
"""
with Table(con, sql) as table:
for clan_acc, xrefs in merge_dumps(files):
clan = clans[clan_acc]
counts = reduce(xrefs)
counts["entries"] = {
clan["database"]: len(clan["members"]),
"total": len(clan["members"])
}
table.insert(
(clan_acc, clan["name"], clan["description"], clan["database"],
jsonify(clan["relationships"],
nullable=False), jsonify(clan.get("authors")),
jsonify(clan.get("literature")), jsonify(counts)))
logger.info(f"temporary files: {dt.size / 1024 / 1024:.0f} MB")
dt.remove()
logger.info("inserting alignments")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS webfront_alignment")
cur.execute("""
CREATE TABLE webfront_alignment
(
id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
set_acc VARCHAR(20) NOT NULL,
entry_acc VARCHAR(25) NOT NULL,
target_acc VARCHAR(25) NOT NULL,
target_set_acc VARCHAR(20),
score DOUBLE NOT NULL,
seq_length MEDIUMINT NOT NULL,
domains TEXT NOT NULL
) CHARSET=utf8mb4 DEFAULT COLLATE=utf8mb4_unicode_ci
""")
cur.close()
sql = """
INSERT INTO webfront_alignment (
set_acc, entry_acc, target_acc, target_set_acc, score,
seq_length, domains
)
VALUES (%s, %s, %s, %s, %s, %s, %s)
"""
with DumpFile(p_alignments) as df, Table(con, sql) as table:
for alignments in df:
for aln in alignments:
table.insert(aln)
con.commit()
con.close()
logger.info("complete")
def export(url: str, p_entries: str, p_entry2xrefs: str, p_taxonomy: str,
outdir: str, max_xrefs: int = 100000):
logger.info("loading database versions")
con = MySQLdb.connect(**url2dict(url))
cur = con.cursor()
cur.execute(
"""
SELECT name, name_long, version, release_date
FROM webfront_database
WHERE type = 'entry'
"""
)
databases = {}
release_version = release_date = None
for name, full_name, version, date in cur:
databases[name] = full_name
if name == "interpro":
release_version = version
release_date = date.strftime("%Y-%m-%d")
cur.close()
con.close()
if release_version is None:
raise RuntimeError("missing release version/date for InterPro")
logger.info("loading taxonomic info")
sci_names = {}
for taxon_id, taxon in loadobj(p_taxonomy).items():
sci_names[taxon_id] = taxon["sci_name"]
try:
shutil.rmtree(outdir)
except FileNotFoundError:
pass
finally:
os.makedirs(outdir, mode=0o775)
entries = loadobj(p_entries)
logger.info("starting")
i = 0
types = {}
num_xrefs = {}
with DumpFile(p_entry2xrefs) as df:
for accession, entry_xrefs in df:
entry = entries[accession]
if entry.is_deleted:
continue
fields, xrefs = _init_fields(entry)
fields.append({
"name": "source_database",
"value": databases[entry.database]
})
for uniprot_acc, uniprot_id in entry_xrefs["proteins"]:
xrefs.append({
"dbname": "UNIPROT",
"dbkey": uniprot_acc
})
xrefs.append({
"dbname": "UNIPROT",
"dbkey": uniprot_id
})
for tax_id in entry_xrefs["taxa"]:
xrefs.append({
"dbname": "TAXONOMY",
"dbkey": tax_id
})
xrefs.append({
"dbname": "TAXONOMY",
"dbkey": sci_names[tax_id]
})
for upid in entry_xrefs["proteomes"]:
xrefs.append({
"dbname": "PROTEOMES",
"dbkey": upid
})
for pdbe_id in entry_xrefs["structures"]:
xrefs.append({
"dbname": "PDB",
"dbkey": pdbe_id
})
entry_type = entry.type.lower()
try:
dt, items = types[entry_type]
except KeyError:
dt = DirectoryTree(outdir, entry_type)
items = []
types[entry_type] = (dt, items)
num_xrefs[entry_type] = 0
items.append({
"fields": fields,
"cross_references": xrefs
})
num_xrefs[entry_type] += len(xrefs)
if num_xrefs[entry_type] >= max_xrefs:
path = dt.mktemp(suffix=".json")
with open(path, "wt") as fh:
json.dump({
"name": "InterPro",
"release": release_version,
"release_date": release_date,
"entry_count": len(items),
"entries": items
}, fh, indent=4)
items.clear()
num_xrefs[entry_type] = 0
i += 1
if not i % 10000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
for entry_type, (dt, items) in types.items():
if num_xrefs[entry_type]:
path = dt.mktemp(suffix=".json")
with open(path, "wt") as fh:
json.dump({
"name": "InterPro",
"release": release_version,
"release_date": release_date,
"entry_count": len(items),
"entries": items
}, fh, indent=4)
logger.info("complete")
def index_documents(hosts: Sequence[str], indir: str, version: str,
threads: int = 4, step: int = 100e6):
kwargs = {
"thread_count": threads,
"queue_size": threads,
"raise_on_exception": False,
"raise_on_error": False
}
es = connect(hosts, timeout=30, verbose=False)
num_documents = 0
num_indexed = 0
first_pass = True
while True:
for filepath in iter_files(indir, version):
docs = loadobj(filepath)
if first_pass:
# Count only once the number of documents to index
num_documents += len(docs)
actions = []
for idx, doc_id, doc in docs:
actions.append({
"_op_type": "index",
"_index": idx,
"_id": doc_id,
"_source": doc
})
failed = []
for i, (ok, info) in enumerate(pbulk(es, actions, **kwargs)):
if ok:
num_indexed += 1
if not num_indexed % 100e6:
logger.info(f"{num_indexed:>14,} / {num_documents:,}")
else:
failed.append(docs[i])
# try:
# is_429 = info["index"]["status"] == 429
# except (KeyError, IndexError):
# is_429 = False
#
# try:
# exc = info["index"]["exception"]
# except (KeyError, TypeError):
# exc = None
#
# if is_429 or isinstance(exc, exceptions.ConnectionTimeout):
# pause = True
# else:
# logger.debug(info)
if failed:
# Overwrite file with failed documents
dumpobj(filepath, failed)
else:
# Remove file as all documents have been successfully indexed
os.remove(filepath)
logger.info(f"{num_indexed:>14,} / {num_documents:,}")
first_pass = False
if num_indexed == num_documents:
break
# Update index settings
for base_alias in (IDA_BASE_ALIAS, REL_BASE_ALIAS):
alias = base_alias + STAGING_ALIAS_SUFFIX
# This assumes there are indices with the 'staging' alias
for index in es.indices.get_alias(name=alias):
es.indices.put_settings(
body={
"number_of_replicas": 1,
"refresh_interval": None # default (1s)
},
index=index
)
def export_documents(src_proteins: str, src_entries: str, src_proteomes: str,
src_structures: str, src_taxonomy: str,
src_uniprot2ida: str, src_uniprot2matches: str,
src_uniprot2proteomes: str, outdirs: Sequence[str],
version: str, cache_size: int = 100000):
logger.info("preparing data")
os.umask(0o002)
organizers = []
for path in outdirs:
try:
shutil.rmtree(path)
except FileNotFoundError:
pass
os.makedirs(path, mode=0o775)
organizers.append(DirectoryTree(path))
open(os.path.join(path, f"{version}{LOAD_SUFFIX}"), "w").close()
logger.info("loading domain architectures")
domains = {}
with Store(src_uniprot2ida) as u2ida:
for dom_members, dom_arch, dom_arch_id in u2ida.values():
try:
dom = domains[dom_arch_id]
except KeyError:
domains[dom_arch_id] = {
"ida_id": dom_arch_id,
"ida": dom_arch,
"counts": 1
}
else:
dom["counts"] += 1
logger.info("writing IDA documents")
num_documents = 0
domains = list(domains.values())
for i in range(0, len(domains), cache_size):
documents = []
for dom in domains[i:i + cache_size]:
documents.append((
IDA_INDEX + version,
dom["ida_id"],
dom
))
num_documents += len(documents)
for org in organizers:
filepath = org.mktemp()
dumpobj(filepath, documents)
os.rename(filepath, f"{filepath}{EXTENSION}")
domains = None
proteins = Store(src_proteins)
uniprot2ida = Store(src_uniprot2ida)
uniprot2matches = Store(src_uniprot2matches)
uniprot2proteomes = Store(src_uniprot2proteomes)
entries = loadobj(src_entries) # mem: ~1.5 GB
proteomes = loadobj(src_proteomes) # mem: <1 GB
structures = loadobj(src_structures) # mem: ~ 4GB
taxonomy = loadobj(src_taxonomy) # mem: ~ 2.5GB
uniprot2pdbe = {} # mem: <1 GB
for pdb_id, entry in structures.items():
for uniprot_acc in entry["proteins"]:
try:
uniprot2pdbe[uniprot_acc].append(pdb_id)
except KeyError:
uniprot2pdbe[uniprot_acc] = [pdb_id]
logger.info("writing relationship documents")
i = 0
documents = []
used_entries = set()
used_taxa = set()
for uniprot_acc, info in proteins.items():
taxid = info["taxid"]
taxon = taxonomy[taxid]
used_taxa.add(taxid) # remember that this taxon has been used
try:
dom_members, dom_arch, dom_arch_id = uniprot2ida[uniprot_acc]
except KeyError:
dom_members = []
dom_arch = dom_arch_id = None
# Create an empty document (all properties set to None)
doc = init_rel_doc()
doc.update({
"protein_acc": uniprot_acc.lower(),
"protein_length": info["length"],
"protein_is_fragment": info["fragment"],
"protein_db": "reviewed" if info["reviewed"] else "unreviewed",
"text_protein": join(uniprot_acc, info["identifier"]),
# Taxonomy
"tax_id": taxid,
"tax_name": taxon["sci_name"],
"tax_lineage": taxon["lineage"],
"tax_rank": taxon["rank"],
"text_taxonomy": join(taxid, taxon["full_name"], taxon["rank"])
})
proteome_id = uniprot2proteomes.get(uniprot_acc)
if proteome_id:
proteome = proteomes[proteome_id]
doc.update({
"proteome_acc": proteome_id.lower(),
"proteome_name": proteome["name"],
"proteome_is_reference": proteome["is_reference"],
"text_proteome": join(proteome_id,
proteome["name"],
proteome["assembly"],
proteome["taxon_id"],
proteome["strain"]),
})
# Adding PDBe structures/chains
pdb_chains = {} # mapping PDB-chain ID -> chain segments
pdb_documents = {} # mapping PDB-chain ID -> ES document
for pdb_id in uniprot2pdbe.get(uniprot_acc, []):
pdb_entry = structures[pdb_id]
chains = pdb_entry["proteins"][uniprot_acc]
pdb_doc = doc.copy()
pdb_doc.update({
"structure_acc": pdb_id.lower(),
"structure_resolution": pdb_entry["resolution"],
"structure_date": pdb_entry["date"],
"structure_evidence": pdb_entry["evidence"],
"protein_structure": chains,
"text_structure": join(pdb_id,
pdb_entry["evidence"],
pdb_entry["name"])
})
for chain_id, segments in chains.items():
pdb_chain_id = f"{pdb_id}-{chain_id}"
locations = []
for segment in segments:
locations.append({
"fragments": [{
"start": segment["protein_start"],
"end": segment["protein_end"],
}]
})
chain_doc = pdb_doc.copy()
chain_doc.update({
"structure_chain_acc": chain_id,
"structure_protein_locations": locations,
"structure_chain": pdb_chain_id
})
pdb_documents[pdb_chain_id] = chain_doc
pdb_chains[pdb_chain_id] = segments
# Adding entries
overlapping_chains = set() # chains associated to at least one entry
matches = uniprot2matches.get(uniprot_acc, {})
num_protein_docs = 0
for entry_acc, locations in matches.items():
used_entries.add(entry_acc) # this entry has been used
entry = entries[entry_acc]
if entry.integrated_in:
interpro_acc = entry.integrated_in.lower()
else:
interpro_acc = None
entry_obj = {
"entry_acc": entry_acc.lower(),
"entry_db": entry.database,
"entry_type": entry.type.lower(),
"entry_date": entry.creation_date.strftime("%Y-%m-%d"),
"entry_protein_locations": locations,
"entry_go_terms": [t["identifier"] for t in entry.go_terms],
"entry_integrated": interpro_acc,
"text_entry": join(entry_acc, entry.short_name, entry.name,
entry.type.lower(), interpro_acc),
}
if entry.clan:
entry_obj.update({
"set_acc": entry.clan["accession"].lower(),
"set_db": entry.database,
"text_set": join(entry.clan["accession"],
entry.clan["name"]),
})
if entry_acc in dom_members:
entry_obj.update({
"ida_id": dom_arch_id,
"ida": dom_arch,
})
# Test if the entry overlaps PDB chains
entry_chains = set()
for pdb_chain_id, segments in pdb_chains.items():
if overlaps_pdb_chain(locations, segments):
# Entry overlaps chain: associate entry to struct/chain
chain_doc = pdb_documents[pdb_chain_id]
entry_doc = chain_doc.copy()
entry_doc.update(entry_obj)
documents.append((
entry.database + version,
get_rel_doc_id(entry_doc),
entry_doc
))
entry_chains.add(pdb_chain_id)
num_protein_docs += 1
if entry_chains:
# Entry overlaps at least one chain
overlapping_chains |= entry_chains
else:
# Associate entry to protein directly
entry_doc = doc.copy()
entry_doc.update(entry_obj)
documents.append((
entry.database + version,
get_rel_doc_id(entry_doc),
entry_doc
))
num_protein_docs += 1
# Add chains not overlapping any entry
for chain_id, chain_doc in pdb_documents.items():
if chain_id in overlapping_chains:
continue
chain_doc.update({
"ida_id": dom_arch_id,
"ida": dom_arch,
})
documents.append((
# Not overlapping any entry -> not associated to a member DB
REL_INDEX + version,
get_rel_doc_id(chain_doc),
chain_doc
))
num_protein_docs += 1
if not num_protein_docs:
# No relationships for this protein: fallback to protein doc
documents.append((
REL_INDEX + version,
get_rel_doc_id(doc),
doc
))
while len(documents) >= cache_size:
for org in organizers:
filepath = org.mktemp()
dumpobj(filepath, documents[:cache_size])
os.rename(filepath, f"{filepath}{EXTENSION}")
del documents[:cache_size]
num_documents += cache_size
i += 1
if not i % 10000000:
logger.info(f"{i:>12,}")
logger.info(f"{i:>12,}")
logger.info("writing remaining documents")
# Add unused entries
for entry in entries.values():
if entry.accession in used_entries or entry.is_deleted:
continue
if entry.integrated_in:
interpro_acc = entry.integrated_in.lower()
else:
interpro_acc = None
doc = init_rel_doc()
doc.update({
"entry_acc": entry.accession.lower(),
"entry_db": entry.database,
"entry_type": entry.type.lower(),
"entry_date": entry.creation_date.strftime("%Y-%m-%d"),
"entry_protein_locations": [],
"entry_go_terms": [t["identifier"] for t in entry.go_terms],
"entry_integrated": interpro_acc,
"text_entry": join(entry.accession, entry.short_name, entry.name,
entry.type.lower(), interpro_acc),
})
if entry.clan:
doc.update({
"set_acc": entry.clan["accession"].lower(),
"set_db": entry.database,
"text_set": join(entry.clan["accession"],
entry.clan["name"]),
})
documents.append((
entry.database + version,
get_rel_doc_id(doc),
doc
))
# Add unused taxa
for taxon in taxonomy.values():
if taxon["id"] in used_taxa:
continue
doc = init_rel_doc()
doc.update({
"tax_id": taxon["id"],
"tax_name": taxon["full_name"],
"tax_lineage": taxon["lineage"],
"tax_rank": taxon["rank"],
"text_taxonomy": join(taxon["id"], taxon["full_name"],
taxon["rank"])
})
documents.append((
REL_INDEX + version,
get_rel_doc_id(doc),
doc
))
num_documents += len(documents)
while documents:
for org in organizers:
filepath = org.mktemp()
dumpobj(filepath, documents[:cache_size])
os.rename(filepath, f"{filepath}{EXTENSION}")
del documents[:cache_size]
proteins.close()
uniprot2ida.close()
uniprot2matches.close()
uniprot2proteomes.close()
for path in outdirs:
open(os.path.join(path, f"{version}{DONE_SUFFIX}"), "w").close()
logger.info(f"complete ({num_documents:,} documents)")
def export_entries(url: str, p_metacyc: str, p_clans: str,
p_proteins: str, p_structures: str,
p_uniprot2matches: str, p_uniprot2proteome: str,
p_uniprot2ida: str, p_entry2xrefs: str, p_entries: str,
**kwargs):
min_overlap = kwargs.get("overlap", 0.2)
processes = kwargs.get("processes", 1)
min_similarity = kwargs.get("similarity", 0.75)
tmpdir = kwargs.get("tmpdir")
con = cx_Oracle.connect(url)
cur = con.cursor()
entries = {}
logger.info("loading active InterPro entries")
for entry in _get_interpro_entries(cur):
entries[entry.accession] = entry
logger.info("enriching entries with IntAct data")
for accession, interactions in intact.get_interactions(cur).items():
try:
entry = entries[accession]
except KeyError:
continue
else:
entry.ppi = interactions
logger.info("loading deleted InterPro entries")
for entry in _get_retired_interpro_entries(cur):
if entry.accession in entries:
cur.close()
con.close()
raise RuntimeError(f"entry cannot be active "
f"and deleted {entry.accession}")
entries[entry.accession] = entry
logger.info("loading member database signatures")
for entry in _get_signatures(cur):
if entry.integrated_in and entry.integrated_in not in entries:
cur.close()
con.close()
raise RuntimeError(f"{entry.accession} integrated "
f"in missing entry ({entry.integrated_in})")
entries[entry.accession] = entry
logger.info("loading past entry names")
past_names = _get_name_history(cur)
logger.info("loading past signature integrations")
past_integrations = _get_integration_history(cur)
logger.info("loading ENZYME")
u2enzyme = uniprot.get_swissprot2enzyme(cur)
logger.info("loading Reactome pathways")
u2reactome = uniprot.get_swissprot2reactome(cur)
cur.close()
con.close()
logger.info("loading MetaCyc pathways")
ec2metacyc = metacyc.get_ec2pathways(p_metacyc)
# Updating entry history
for entry in entries.values():
try:
names = past_names[entry.accession]
except KeyError:
pass
else:
entry.history["names"] = names
try:
signatures = past_integrations[entry.accession]
except KeyError:
pass
else:
entry.history["signatures"] = signatures
# Updating entry clan info
for clan in loadobj(p_clans).values():
for entry_acc, score, seq_length in clan["members"]:
try:
entry = entries[entry_acc]
except:
continue
else:
entry.clan = {
"accession": clan["accession"],
"name": clan["name"]
}
inqueue = Queue(maxsize=processes)
outqueue = Queue()
workers = []
for _ in range(max(1, processes - 1)):
dt = DirectoryTree(tmpdir)
p = Process(target=_process_proteins,
args=(inqueue, entries, min_overlap, dt, outqueue))
p.start()
workers.append((p, dt))
logger.info("processing")
uniprot2pdbe = {}
for pdb_id, entry in loadobj(p_structures).items():
for uniprot_acc, chains in entry["proteins"].items():
try:
uniprot2pdbe[uniprot_acc][pdb_id] = chains
except KeyError:
uniprot2pdbe[uniprot_acc] = {pdb_id: chains}
proteins = Store(p_proteins)
u2matches = Store(p_uniprot2matches)
u2proteome = Store(p_uniprot2proteome)
i = 0
for uniprot_acc, matches in u2matches.items():
inqueue.put((
uniprot_acc,
proteins[uniprot_acc],
matches,
u2proteome.get(uniprot_acc),
uniprot2pdbe.get(uniprot_acc, {}),
set(u2enzyme.get(uniprot_acc, [])),
set(u2reactome.get(uniprot_acc, []))
))
i += 1
if not i % 10000000:
logger.info(f"{i:>15,}")
proteins.close()
u2matches.close()
u2proteome.close()
logger.info(f"{i:>15,}")
# Send sentinel
for _ in workers:
inqueue.put(None)
# Merge results from workers
logger.info("exporting domain architectures")
entries_with_xrefs = set()
xref_files = []
entry_counts = {}
entry_intersections = {}
interpro2enzyme = {}
interpro2reactome = {}
with Store(p_uniprot2ida, u2matches.get_keys(), tmpdir) as u2ida:
for _ in workers:
obj = outqueue.get()
xref_files.append(obj[0]) # str
entries_with_xrefs |= obj[1] # set
ida_file = obj[2] # str
deepupdate(obj[3], entry_counts, replace=False) # dict
deepupdate(obj[4], entry_intersections, replace=False) # dict
deepupdate(obj[5], interpro2enzyme) # dict
deepupdate(obj[6], interpro2reactome) # dict
with DumpFile(ida_file) as df:
i = 0
for uniprot_acc, dom_members, dom_str, dom_id in df:
u2ida[uniprot_acc] = (
dom_members,
dom_str,
dom_id
)
i += 1
if not i % 1000:
u2ida.sync()
u2ida.sync()
size = u2ida.merge(processes=processes)
# Adding empty EntryXrefs objects for entries without xrefs
xref_files.append(workers[0][1].mktemp())
with DumpFile(xref_files[-1], compress=True) as df:
for entry_acc in sorted(set(entries.keys()) - entries_with_xrefs):
df.dump((entry_acc, EntryXrefs().asdict()))
logger.info("exporting cross-references")
with DumpFile(p_entry2xrefs, compress=True) as df:
for entry_acc, xrefs in merge_dumps(xref_files):
df.dump((entry_acc, xrefs))
entry = entries[entry_acc]
# Reactome pathways
if entry_acc in interpro2reactome:
pathways = interpro2reactome[entry_acc]
entry.pathways["reactome"] = [
dict(zip(("id", "name"), pthw))
for pthw in sorted(pathways)
]
# EC numbers
if entry_acc in interpro2enzyme:
ecnos = sorted(interpro2enzyme[entry_acc])
entry.cross_references["ec"] = ecnos
# MetaCyc pathways
pathways = set()
for ecno in ecnos:
pathways |= set(ec2metacyc.get(ecno, []))
if pathways:
entry.pathways["metacyc"] = [
dict(zip(("id", "name"), pthw))
for pthw in sorted(pathways)
]
for p, dt in workers:
size += dt.size
dt.remove()
logger.info(f"temporary files: {size / 1024 / 1024:.0f} MB")
logger.info("calculating overlapping relationships")
supfam = "homologous_superfamily"
types = (supfam, "domain", "family", "repeat")
for entry_acc, overlaps in entry_intersections.items():
entry1 = entries[entry_acc]
entry_cnt = entry_counts[entry_acc]
type1 = entry1.type.lower()
for other_acc, overlap_counts in overlaps.items():
o1 = overlap_counts["1"]
o2 = overlap_counts["2"]
other_cnt = entry_counts[other_acc]
# Independent coefficients
coef1 = o1 / (entry_cnt + other_cnt - o1)
coef2 = o2 / (entry_cnt + other_cnt - o2)
# Final coefficient: average of independent coefficients
coef = (coef1 + coef2) * 0.5
# Containment indices
c1 = o1 / entry_cnt
c2 = o2 / other_cnt
if all([item < min_similarity for item in (coef, c1, c2)]):
continue
# Entries are similar enough
entry2 = entries[other_acc]
type2 = entry2.type.lower()
if ((type1 == supfam and type2 in types)
or (type1 in types and type2 == supfam)):
# e1 -> e2 relationship
entry1.overlaps_with.append({
"accession": other_acc,
"name": entry2.name,
"type": type2
})
# e2 -> e1 relationship
entry2.overlaps_with.append({
"accession": entry_acc,
"name": entry1.name,
"type": type1
})
dumpobj(p_entries, entries)
logger.info("populating ENTRY2PATHWAY")
con = cx_Oracle.connect(url)
cur = con.cursor()
cur.execute("TRUNCATE TABLE INTERPRO.ENTRY2PATHWAY")
cur.close()
sql = "INSERT INTO INTERPRO.ENTRY2PATHWAY VALUES (:1, :2, :3, :4)"
with Table(con, sql) as table:
for e in entries.values():
for database, pathways in e.pathways.items():
code = PATHWAY_DATABASE[database]
for pthw in pathways:
table.insert((
e.accession,
code,
pthw["id"],
pthw["name"]
))
con.commit()
con.close()
logger.info("complete")
