Add generalizable MeSH mappings

scripts/generate_mesh_mappings.py

@@ -0,0 +1,154 @@
+# -*- coding: utf-8 -*-
+
+"""Append lexical mappings between MeSH and other gene/protein vocabularies."""
+
+import collections
+import re
+from typing import Iterable, Mapping, Optional, Tuple
+
+import pandas as pd
+import pyobo
+import pystow
+from indra.databases import mesh_client
+
+from biomappings.resources import append_prediction_tuples
+from biomappings.utils import get_script_url
+
+#: This is a dictionary from MeSH suffix to PyOBO prefix
+#: Ellipses are left for unhandled organisms
+MAP = {
+    # 'human': 'hgnc',  # 13168 matches
+    'mouse': 'mgi',  # 9822 matches
+    'Arabidopsis': ...,  # 5068 matches
+    'rat': 'rgd',  # 4681 matches
+    'S cerevisiae': 'sgd',  # 3536 matches
+    'Drosophila': 'fb',  # 3061 matches
+    'C elegans': ...,  # 2040 matches
+    'E coli': ...,  # 1984 matches
+    'zebrafish': 'zfin',  # 1940 matches
+    'Xenopus': ...,  # 1305 matches
+    'S pombe': ...,  # 1030 matches
+    # 'Bacteria': ...,  # 259 matches
+    # 'bacteria': ...,  # 233 matches
+    'Bacillus subtilis': ...,  # 226 matches
+    'Mycobacterium tuberculosis': ...,  # 199 matches
+    'Zea mays': ...,  # 152 matches
+    'Pseudomonas aeruginosa': ...,  # 149 matches
+    'Staphylococcus aureus': ...,  # 126 matches
+    'Plasmodium falciparum': ...,  # 110 matches
+    'Salmonella typhimurium': ...,  # 107 matches
+    'Nicotiana tabacum': ...,  # 95 matches
+    'Candida albicans': ...,  # 91 matches
+    'chicken': 'cgnc',  # 91 matches
+    'Trypanosoma brucei': ...,  # 90 matches
+    'Gallus gallus': 'cgnc',  # 90 matches
+    'Bos taurus': ...,  # 82 matches
+    'Dictyostelium discoideum': ...,  # 82 matches
+    'Oryza sativa': ...,  # 81 matches
+}
+
+NEED_PREFIX = {
+    'mgi',
+}
+
+UNIPROT_NAMES = {
+    'mgi': ('database(MGI)', 'Mus musculus (Mouse) [10090]'),
+    'rgd': ('database(RGD)', 'Rattus norvegicus (Rat) [10116]'),
+    'sgd': ('database(SGD)', "Saccharomyces cerevisiae (strain ATCC 204508 / S288c) (Baker's yeast) [559292]"),
+    'fb': ('database(FlyBase)', "Drosophila melanogaster (Fruit fly) [7227]"),
+    'zfin': ('database(ZFIN)', "Danio rerio (Zebrafish) (Brachydanio rerio) [7955]"),
+    'wb': ('database(WormBase)', "..."),
+    # 'cgnc': ('database(CGNC)', "Gallus gallus (Chicken) [9031]"),
+}
+
+
+def get_id_to_uniprot(prefix: str) -> Optional[Mapping[str, str]]:
+    """Get identifier to UniProt mapping."""
+    if prefix not in UNIPROT_NAMES:
+        return
+
+    column, organism = UNIPROT_NAMES[prefix]
+    url = 'https://www.uniprot.org/uniprot/'
+    params = {
+        'format': 'tab',
+        'columns': f'{column},id',
+        'fil': f'organism:"{organism}"',
+    }
+    df = pystow.ensure_csv(
+        key='biomappings',
+        url=url,
+        name=f'{prefix}_uniprot.tsv.gz',
+        read_csv_kwargs=dict(
+            sep='\t',
+            compression='gzip',
+            dtype=str,
+        ),
+        download_kwargs=dict(
+            backend='requests',
+            params=params,
+        ),
+    )
+    return {
+        mgi_id.rstrip(';'): uniprot_id
+        for mgi_id, uniprot_id in df.values
+        if pd.notna(mgi_id) and pd.notna(uniprot_id)
+    }
+
+
+def print_map(minimum: int = 80):
+    """Print the map dictionary."""
+    names = pyobo.get_name_id_mapping('mesh')
+    counter = collections.Counter()
+    for name in names:
+        if 'protein, ' in name:
+            counter[name.split('protein, ')[1]] += 1
+
+    for key, count in counter.most_common():
+        if count <= minimum:
+            continue
+        print(f"'{key}': ...,  # {count} matches")
+
+
+def get_mappings() -> Iterable[Tuple[str, ...]]:
+    """Iterate high-confidence lexical mappings between MeSH and UniProt mouse proteins."""
+    url = get_script_url(__file__)
+    mapping_type = 'lexical'
+    match_type = 'skos:exactMatch'
+    confidence = 0.999
+
+    for suffix, key in MAP.items():
+        if key is ...:
+            continue
+        try:
+            name_to_id_mapping = pyobo.get_name_id_mapping(key)
+        except ValueError:
+            print('skipping', key)
+            continue
+
+        id_to_uniprot = get_id_to_uniprot(key)
+        if id_to_uniprot is None:
+            print('no lookup for', key)
+            continue
+
+        mesh_protein_re = re.compile(rf'^(.+) protein, {suffix}$')
+        for mesh_name, mesh_id in mesh_client.mesh_name_to_id.items():
+            match = mesh_protein_re.match(mesh_name)
+            if not match:
+                continue
+            gene_name = match.groups()[0]
+            identifier = name_to_id_mapping.get(gene_name)
+            if not identifier:
+                continue
+            uniprot_id = id_to_uniprot.get(identifier)
+            if uniprot_id is None:
+                continue
+            yield (
+                'mesh', mesh_id, mesh_name,
+                match_type,
+                'uniprot', uniprot_id, gene_name,
+                mapping_type, confidence, url,
+            )
+
+
+if __name__ == '__main__':
+    append_prediction_tuples(get_mappings())