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d267ea4
adding module for silva taxonomy
jplfaria 205fad3
Run ruff
cthoyt b99644a
Update silva.py
cthoyt 9ff0766
Merge branch 'main' into pr/348
cthoyt a159f4a
Add typedef example
cthoyt 09a07e9
Update silva.py
cthoyt 68ca239
Update __init__.py
cthoyt b4fa6e3
Replace parent relationship with HAS_TAXONOMIC_CLASSIFICATION typedef
jplfaria 8967240
Update silva.py
cthoyt 7926155
Merge branch 'main' into pr/348
cthoyt f4b2619
ID spaces get automatically constructed now
cthoyt 20acf22
Fix nomenclature mistake
cthoyt f46273a
Add fixmes
cthoyt 1fffd60
Update silva.py
cthoyt 25e44d6
Update silva.py
cthoyt ae99167
Remove duplicates
cthoyt 151d147
Minor cleanup
cthoyt 5381ec3
Update silva.py
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"""Convert SILVA small subunit (ssu) taxonomy to OBO format.""" | ||
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import logging | ||
from collections.abc import Iterable | ||
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import pandas as pd | ||
from tqdm.auto import tqdm | ||
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from pyobo.struct import Obo, Reference, Term | ||
from pyobo.struct.typedef import has_taxonomy_rank | ||
from pyobo.utils.path import ensure_path | ||
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__all__ = [ | ||
"SILVAGetter", | ||
] | ||
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PREFIX = "silva.taxon" | ||
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#: A mapping from SILVA rank names to TAXRANK references | ||
SILVA_RANK_TO_TAXRANK = { | ||
"domain": Reference(prefix="TAXRANK", identifier="0000037", name="domain"), | ||
"major_clade": Reference(prefix="TAXRANK", identifier="0001004", name="major_clade"), | ||
"superkingdom": Reference(prefix="TAXRANK", identifier="0000022", name="superkingdom"), | ||
"kingdom": Reference(prefix="TAXRANK", identifier="0000017", name="kingdom"), | ||
"subkingdom": Reference(prefix="TAXRANK", identifier="0000029", name="subkingdom"), | ||
"superphylum": Reference(prefix="TAXRANK", identifier="0000027", name="superphylum"), | ||
"phylum": Reference(prefix="TAXRANK", identifier="0000001", name="phylum"), | ||
"subphylum": Reference(prefix="TAXRANK", identifier="0000008", name="subphylum"), | ||
"infraphylum": Reference(prefix="TAXRANK", identifier="0000040", name="infraphylum"), | ||
"superclass": Reference(prefix="TAXRANK", identifier="0000015", name="superclass"), | ||
"class": Reference(prefix="TAXRANK", identifier="0000002", name="class"), | ||
"subclass": Reference(prefix="TAXRANK", identifier="0000007", name="subclass"), | ||
"infraclass": Reference(prefix="TAXRANK", identifier="0000019", name="infraclass"), | ||
"superorder": Reference(prefix="TAXRANK", identifier="0000020", name="superorder"), | ||
"order": Reference(prefix="TAXRANK", identifier="0000003", name="order"), | ||
"suborder": Reference(prefix="TAXRANK", identifier="0000014", name="suborder"), | ||
"superfamily": Reference(prefix="TAXRANK", identifier="0000018", name="superfamily"), | ||
"family": Reference(prefix="TAXRANK", identifier="0000004", name="family"), | ||
"subfamily": Reference(prefix="TAXRANK", identifier="0000024", name="subfamily"), | ||
"genus": Reference(prefix="TAXRANK", identifier="0000005", name="genus"), | ||
} | ||
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#: URLs for the SILVA files. | ||
SILVA_TAXONOMY_URL = "https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/tax_slv_ssu_138.2.txt.gz" | ||
SILVA_TAXMAP_URL = "https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/taxmap_slv_ssu_ref_nr_138.2.txt.gz" | ||
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logger = logging.getLogger(__name__) | ||
logger.setLevel(logging.WARNING) | ||
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class SILVAGetter(Obo): | ||
"""An ontology representation of the SILVA taxonomy.""" | ||
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ontology = bioversions_key = PREFIX | ||
typedefs = [has_taxonomy_rank] | ||
idspaces = { | ||
PREFIX: "https://www.arb-silva.de/no_cache/download/archive/current/Exports/taxonomy/", | ||
"ena.embl": "https://www.ebi.ac.uk/ena/browser/view/", | ||
} | ||
root_terms = [ | ||
Reference(prefix=PREFIX, identifier="2", name="Archaea"), | ||
Reference(prefix=PREFIX, identifier="3", name="Bacteria"), | ||
Reference(prefix=PREFIX, identifier="4", name="Eukaryota"), | ||
] | ||
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def iter_terms(self, force: bool = False) -> Iterable[Term]: | ||
"""Iterate over terms in the SILVA ontology.""" | ||
return iter_terms_silva(version=self._version_or_raise, force=force) | ||
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def iter_terms_silva(version: str, force: bool = False) -> Iterable[Term]: | ||
"""Iterate over SILVA terms from the main taxonomy and taxmap files.""" | ||
# --- Process the main taxonomy file --- | ||
taxonomy_path = ensure_path(PREFIX, url=SILVA_TAXONOMY_URL, version=version, force=force) | ||
tax_df = pd.read_csv( | ||
taxonomy_path, | ||
sep="\t", | ||
header=None, | ||
names=["taxonomy", "taxon_id", "rank", "ignore", "introduced"], | ||
dtype=str, | ||
) | ||
tax_df.fillna("", inplace=True) | ||
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#: a dictionary that maps the joined taxonomy path (with trailing ";") to taxon_id | ||
tax_path_to_id: dict[str, str] = {} | ||
#: maps taxon_id to the Term object | ||
terms_by_id = {} | ||
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for idx, row in tqdm( | ||
tax_df.iterrows(), | ||
total=len(tax_df), | ||
desc=f"[{PREFIX}] processing main taxonomy", | ||
unit="row", | ||
): | ||
tax_str = row["taxonomy"].strip() | ||
taxon_id = row["taxon_id"].strip() | ||
rank_raw = row["rank"].strip() | ||
rank = rank_raw.lower() | ||
# Split taxonomy string by ";" and discard empty parts. | ||
parts = [p.strip() for p in tax_str.split(";") if p.strip()] | ||
if not parts: | ||
logger.warning(f"Row {idx}: empty taxonomy string: {tax_str}") | ||
continue | ||
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# The term's name is the last element (e.g. for "Bacteria;Actinomycetota;", name is "Actinomycetota"). | ||
name = parts[-1] | ||
term = Term(reference=Reference(prefix=PREFIX, identifier=taxon_id, name=name)) | ||
if rank in SILVA_RANK_TO_TAXRANK: | ||
term.annotate_object(has_taxonomy_rank, SILVA_RANK_TO_TAXRANK[rank]) | ||
else: | ||
logger.warning( | ||
f"Row {idx}: unknown rank '{rank_raw}' for taxonomy: {tax_str} (taxon id: {taxon_id})" | ||
) | ||
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# Determine the parent by joining all but the last element. | ||
if len(parts) > 1: | ||
parent_key = ";".join(parts[:-1]) + ";" # e.g. "Bacteria;" | ||
parent_id = tax_path_to_id.get(parent_key) | ||
if parent_id: | ||
term.append_parent(Reference(prefix=PREFIX, identifier=parent_id)) | ||
full_key = ";".join(parts) + ";" | ||
tax_path_to_id[full_key] = taxon_id | ||
terms_by_id[taxon_id] = term | ||
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# --- Process the taxmap file --- | ||
# This file has a header with columns: primaryAccession, start, stop, path, organism_name, taxid | ||
taxmap_path = ensure_path(PREFIX, url=SILVA_TAXMAP_URL, version=version, force=force) | ||
taxmap_df = pd.read_csv(taxmap_path, sep="\t", dtype=str) | ||
taxmap_df.rename( | ||
columns={ | ||
"primaryAccession": "accession", | ||
"organism_name": "organism", | ||
"taxid": "species_taxon_id", | ||
"path": "taxonomy", | ||
}, | ||
inplace=True, | ||
) | ||
taxmap_df.fillna("", inplace=True) | ||
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for idx, row in tqdm( | ||
taxmap_df.iterrows(), total=len(taxmap_df), desc=f"[{PREFIX}] processing taxmap", unit="row" | ||
): | ||
accession = row["accession"].strip() | ||
species_taxon_id = row["species_taxon_id"].strip() | ||
organism = row["organism"].strip() | ||
if not accession or not species_taxon_id: | ||
continue | ||
if species_taxon_id in terms_by_id: | ||
# Create a new term for the ENA accession. | ||
new_term = Term( | ||
reference=Reference(prefix="ena.embl", identifier=accession, name=organism) | ||
) | ||
# Do NOT annotate the new term with a rank (leave it unranked). | ||
new_term.append_parent(Reference(prefix=PREFIX, identifier=species_taxon_id)) | ||
yield new_term | ||
else: | ||
logger.warning( | ||
f"Row {idx} in taxmap: species_taxon_id {species_taxon_id} not found in main taxonomy" | ||
) | ||
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# Yield all terms from the main taxonomy. | ||
for term in terms_by_id.values(): | ||
yield term | ||
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if __name__ == "__main__": | ||
SILVAGetter().cli() |
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Don't ENA terms represent nucleotide sequences derived from experiments? Can they also represent projects?
From what I understand, they aren't actually themselves representing taxa. Therefore this parent/child relationship doesn't make sense.
The hard work of making a PyOBO source is really understanding what is the relationship SILVA means when it mentions its internal taxonomy and ENA sequences. I can't do this hard work for you in detail, but from a high level it seems like the sequence was derived from an individual of the taxonomy.
Then, there's two options:
pyobo/src/pyobo/sources/clinicaltrials.py
Line 23 in ada760b
If you go the second route, make sure that you do a good job describing what the relationship means (in a concise way)
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Thank you for your detailed feedback. I completely understand where the hard work lies, and I truly appreciate the guidance you provided. Your suggestions—either reusing an existing RO relationship (like RO_0001001) or minting an ad-hoc one (as in clinicaltrials.py)—are exactly the direction I was hoping for.
I’ll explore those options further. Alternatively, I might start by representing only down to the genus level (as shown in the taxonomy files) until I fully understand the nuances of the lower levels.
Thanks again for steering this work in the right direction!
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I think it's good to surface decision like this higher up, and ideally all of pyobo would be biolink compliant. biolink:has_biological_sequence is the right KG relationship to use.
If you are going to use RO you need to use it consistently with how it's intended and not just pick a label that sounds right.