def obo(path: str, check: bool):
"""Export CONSO as OBO."""
get_obo().write_obo(path)
if check:
import obonet
obonet.read_obo(path)
def get_ontology(obo_link = '../ontologies/go-basic.obo', reverse_graph = "false"):
try:
graph = obonet.read_obo(obo_link)
except Exception as es:
logging.info(es)
graph = obonet.read_obo(obo_link)
#obo_link = 'http://purl.obolibrary.org/obo/go/go-basic.obo'
logging.info(obo_link)
numberOfNodes = graph.number_of_nodes()
logging.info("Number of nodes: {}".format(numberOfNodes))
reverseGraph = nx.DiGraph()
## generate whole graph first, we'll specialize later.
wholeset = set()
for edge in list(graph.edges()):
edge_info = set(graph.get_edge_data(edge[0], edge[1]).keys())
wholeset = wholeset.union(edge_info)
for itype in edge_info:
if itype == "is_a" or itype == "part_of":
if reverse_graph == "true":
reverseGraph.add_edge(edge[1], edge[0], type=itype)
else:
reverseGraph.add_edge(edge[0], edge[1], type=itype)
logging.info(nx.info(reverseGraph))
tnum = len(wholeset)
logging.info("Found {} unique edge types, {}".format(tnum," | ".join(wholeset)))
return reverseGraph
def textualize_top_k_terms(json_data, mapping, obo_link, class_names, k_number = 5):
"""
This method prints the names of the *k_number* most important terms for each class (according to genQ)
"""
try:
graph = obonet.read_obo(obo_link)
except Exception as es:
logging.info(es)
graph = obonet.read_obo(obo_link)
id_to_name = {id_: data.get('name') for id_, data in graph.nodes(data=True)}
## go through mapping
mc = {}
all_terms = set()
mappings = read_generic_gaf(mapping)
for k, v in mappings.items():
for el in v:
all_terms.add(el)
if el in mc:
mc[el] += 1
else:
mc[el] = 1
normalization = len(all_terms)
counter = 0
for keyClass in json_data["resulting_generalization"].keys():
first = True
print()
if keyClass != "average_depth" and keyClass != "average_association":
genQ_dict = {}
for term in json_data["resulting_generalization"][keyClass]["terms"]:
IC = IC_of_a_term(term, mappings, mc, normalization)
genQ = 1 - IC / 9.82
genQ_dict[term] = genQ
for n in range(k_number):
max = 0
term = ""
for k,v in genQ_dict.items():
if v >= max:
max = v
term = k
if first:
print("Class " + str(keyClass) + " :− " + str(id_to_name[term]))
first = False
else:
print("^" + str(id_to_name[term]))
genQ_dict[term] = -1
counter += 1
def load_disease_file(disease_download_file, disease_output_file, disease_xref_output_file):
# parse ontology
ont = obonet.read_obo(disease_download_file)
# build child node lookup dictionary
child_dict = build_child_dict(ont)
# build filter
#do_filter = get_infectious_diseases(ont, child_dict)
do_filter = None
xref_list = []
with open(disease_output_file, "w", newline='') as outfile:
writer = csv.writer(outfile, delimiter="\t")
writer.writerow(["doid", "name", "definition", "parents", "link", "source", "license"])
for id_, data in ont.nodes(data=True):
if do_filter == None or id_ in do_filter:
row = parse_ontology_entry(id_, data)
writer.writerow(row)
parse_ontology_xref(id_, data, xref_list)
xref_df = pd.DataFrame(xref_list)
xref_df.to_csv(disease_xref_output_file, sep="\t", index=False)
def return_archived_ontology(version):
'''
This function returns an archived ontology based on the version number.
'''
url = 'ftp://ftp.ebi.ac.uk/pub/databases/chebi/archive/rel' + version + '/ontology/chebi.obo'
graph = obonet.read_obo(url)
return graph
def update_resource(directory,
url,
prefix,
*args,
remove_prefix=False,
allowed_synonyms=None):
"""Write the OBO information to files in the given directory."""
resource_path = _make_resource_path(directory, prefix)
obo_path = os.path.join(directory, '%s.obo.pkl' % prefix)
if os.path.exists(obo_path):
with open(obo_path, 'rb') as file:
g = pickle.load(file)
else:
g = obonet.read_obo(url)
with open(obo_path, 'wb') as file:
pickle.dump(g, file)
entries = \
OboClient.entries_from_graph(g, prefix=prefix,
remove_prefix=remove_prefix,
allowed_synonyms=allowed_synonyms)
entries = prune_empty_entries(
entries, {'synonyms', 'xrefs', 'alt_ids', 'relations'})
with open(resource_path, 'w') as file:
json.dump(entries, file, indent=1, sort_keys=True)
def _propagate_GO(gene_mapper_file, tree, out_file):
"""
Script for Propagation of GO terms.
Input files required:
1) go graph
2) EggNOG GO predictions
3) Name of output file
Output:
1) propagated Gene ontologies
"""
with open(tree, 'r') as f:
go_graph = obonet.read_obo(f)
# load gene mapper table
mapped_genes = pd.read_csv(gene_mapper_file, sep="\t")
# drop NAN in the Gene ID column
mapped_genes = mapped_genes[mapped_genes['Gene ID'].notna()]
# subset data and drop NANs in the GOs column
genes_GO_df = mapped_genes[["Gene ID", "GOs"]].dropna()
# propagate GO terms
genes_GO_df['GOs_propagated'] = genes_GO_df['GOs'].str.split(',').\
apply(propagate_go, go_graph=go_graph)
# save the file
genes_GO_df.to_csv(out_file, sep='\t', index=False)
def __init__(self, path):
"""
Initialize an ontology class by providing the path.
:param path:
"""
self.graph = read_obo(path)
self.root_id = self._find_root_id()
def load_chebi(path="ftp://ftp.ebi.ac.uk/pub/databases/chebi/ontology/chebi.obo"):
print("loading chebi from {}...".format(path))
#graph = obonet.read_obo("data/chebi.obo")
graph = obonet.read_obo(path)
graph.add_node(root_concept, name="ROOT")
graph.add_edge(chemical_entity, root_concept, edgetype='is_a')
graph.add_edge(role, root_concept, edgetype='is_a')
graph.add_edge(subatomic_particle, root_concept, edgetype='is_a')
graph.add_edge(application, root_concept, edgetype='is_a')
#print([dir(d) for u,v,d in graph.edges(data=True)])
#sys.exit()
graph = graph.to_directed()
is_a_graph=networkx.MultiDiGraph([(u,v,d) for u,v,d in graph.edges(data=True) if d['edgetype'] == "is_a"] )
#print(networkx.is_directed_acyclic_graph(is_a_graph))
id_to_name = {id_: data['name'] for id_, data in graph.nodes(data=True)}
name_to_id = {data['name']: id_ for id_, data in graph.nodes(data=True)}
id_to_index = {e: i+1 for i, e in enumerate(graph.nodes())} # ids should start on 1 and not 0
id_to_index[""] = 0
synonym_to_id = {}
print("synonyms to ids...")
for n in graph.nodes(data=True):
# print(n[1].get("synonym"))
for syn in n[1].get("synonym", []):
syn_name = syn.split('"')
if len(syn_name) > 2:
syn_name = syn.split('"')[1]
synonym_to_id.setdefault(syn_name, []).append(n[0])
#else:
#print("not a synonym:", syn.split('"'))
#print(synonym_to_id)
print("done.", len(name_to_id), "ids", len(synonym_to_id), "synonyms")
return is_a_graph, name_to_id, synonym_to_id, id_to_name, id_to_index
def parse_hpo_obo(path):
"""Parse HPO obo file.
return a network MultiDiGraph object
"""
import obonet
graph = obonet.read_obo(path)
return graph
def scHCL_adata(adata, verbose=False, n_cores=1, n_min=10):
"""
previous main function
"""
ref_df = reference_hcl.load_HCL_reference()
transformed_adata = process_adata(adata, ref_df)
scHCL_df, scHCL_df_extended_Celltypes = call_celltypes(
transformed_adata, ref_df, n_cores)
scHCL_df = annotate_refined(scHCL_df, n_min)
# cell ontology
graph = obonet.read_obo('http://purl.obolibrary.org/obo/cl/cl-basic.obo')
id_to_name = {
id_: data.get('name')
for id_, data in graph.nodes(data=True)
}
scHCL_df['CLid'] = scHCL_df['hcl_refined'].apply(
lambda x: reference_hcl.refined_celltypes_to_cell_ontology[x] if x in
reference_hcl.refined_celltypes_to_cell_ontology else 'unknown')
scHCL_df['CL_name'] = scHCL_df['CLid'].apply(
lambda x: id_to_name[x] if x in id_to_name else 'unknown')
return scHCL_df, scHCL_df_extended_Celltypes
def test_read_obo(ontology):
"""
Test that reading ontology does not error.
"""
url = 'http://purl.obolibrary.org/obo/{}.obo'.format(ontology)
graph = obonet.read_obo(url)
assert graph
def __init__(self,
obo_path=OBOURL,
):
"""
Loads and interprets a PSI-MS obo file into a python-interpretable format.
:param obo_path: file path or url to an obo file
"""
CVParameterSet.__init__(self)
try:
self.obo_file = obonet.read_obo(obo_path) # read the obo file
except (FileNotFoundError, urllib.error.HTTPError):
raise FileNotFoundError(f'An obo file could not be found at the provided path or URL: {obo_path}')
if obo_path == OBOURL: # remind the user to cite the pulication
print('Data was read from PSI-MS, please cite DOI: 10.1093/database/bat009')
self.format_version = self.obo_file.graph['format-version']
self.data_version = self.obo_file.graph['data-version']
for acc in self.obo_file:
dct = self.obo_file.node[acc]
if 'def' in dct: # if the invalid key def is in the dictionary, convert and remove
dct['definition'] = dct['def']
del dct['def']
self.cv_values[acc] = CVParam(
id=acc,
**dct,
)
def load(obo_file):
"""
Load OBO file into a networkx graph.
:param obo_file: OBO definition file.
:param logger: Python `logging` logger instance.
:return: `networkx.MultiDiGraph`
"""
try:
hpo_network = obonet.read_obo(obo_file)
#return nx.MultiDiGraph(hpo_network.subgraph(['HP:0000118'] + list(nx.ancestors(hpo_network, 'HP:0000118'))))
except (FileNotFoundError, PermissionError) as e:
if logger is not None:
logger.critical(e)
else:
sys.stderr.write(str(e))
exit(1)
# roots for non-phenotype nodes
non_phenotypes = {
'mortality_aging': 'HP:0040006',
'mode_of_inheritance': 'HP:0000005',
'clinical_modifier': 'HP:0012823',
'frequency': 'HP:0040279',
'clinical_course': 'HP:0031797',
}
# remove non-phenotype branches
for _, hpo_id in non_phenotypes.items():
if hpo_id in hpo_network.nodes:
children = nx.ancestors(hpo_network, hpo_id)
hpo_network.remove_nodes_from([hpo_id] + list(children))
return hpo_network
def handle(self, file: str, verbosity: int = 1, **options):
"""Execute the main function."""
try:
FileValidator().validate(file)
except ImportingError as e:
raise CommandError(e)
# Load the ontology file
with open(file) as obo_file:
G = obonet.read_obo(obo_file)
if verbosity > 0:
self.stdout.write("Preprocessing")
cv_name = "relationship"
# Initializing ontology
ontology = OntologyLoader(cv_name)
# Load typedefs as Dbxrefs and Cvterm
if verbosity > 0:
self.stdout.write("Loading typedefs")
for data in tqdm(G.graph["typedefs"],
disable=False if verbosity > 0 else True):
ontology.store_type_def(data)
if verbosity > 0:
self.stdout.write(self.style.SUCCESS("Done"))
def __init__(
self,
obo_path=OBOURL,
):
"""
Loads and interprets a PSI-MS obo file into a python-interpretable format.
:param obo_path: file path or url to an obo file
"""
CVParameterSet.__init__(self)
try:
self.obo_file = obonet.read_obo(obo_path) # read the obo file
except (FileNotFoundError, urllib.error.HTTPError):
raise FileNotFoundError(
f'An obo file could not be found at the provided path or URL: {obo_path}'
)
if obo_path == OBOURL: # remind the user to cite the pulication
print(
'Data was read from PSI-MS, please cite DOI: 10.1093/database/bat009'
)
self.format_version = self.obo_file.graph['format-version']
self.data_version = self.obo_file.graph['data-version']
for acc in self.obo_file:
dct = self.obo_file.nodes[acc]
if 'def' in dct: # if the invalid key def is in the dictionary, convert and remove
dct['definition'] = dct['def']
del dct['def']
self.cv_values[acc] = CVParam(
id=acc,
**dct,
)
def test_store_type_def(self):
"""Tests - store type_def."""
directory = os.path.dirname(os.path.abspath(__file__))
file = os.path.join(directory, "data", "so_fake.obo")
with open(file) as obo_file:
G = obonet.read_obo(obo_file)
cv_name = G.graph["default-namespace"][0]
cv_definition = G.graph["data-version"]
# Initializing ontology
ontology = OntologyLoader(cv_name, cv_definition)
for typedef in G.graph["typedefs"]:
ontology.store_type_def(typedef)
# Testing cv
test_cv = Cv.objects.get(name="sequence")
self.assertEqual("sequence", test_cv.name)
self.assertEqual("so.obo(fake)", test_cv.definition)
# Testing store_type_def
test_db = Db.objects.get(name="_global")
self.assertEqual("_global", test_db.name)
test_dbxref = Dbxref.objects.get(db=test_db, accession="derives_from")
self.assertEqual("derives_from", test_dbxref.accession)
test_cvterm = Cvterm.objects.get(dbxref=test_dbxref)
self.assertEqual("derives_from", test_cvterm.name)
self.assertEqual(
'"testing def loading." [PMID:999090909]', test_cvterm.definition
)
test_type = Cvterm.objects.get(name="comment")
test_comment = Cvtermprop.objects.get(
cvterm_id=test_cvterm.cvterm_id, type_id=test_type.cvterm_id
)
self.assertEqual("Fake typedef data.", test_comment.value)
test_type = Cvterm.objects.get(name="is_class_level")
test_prop = Cvtermprop.objects.get(
cvterm_id=test_cvterm.cvterm_id, type_id=test_type.cvterm_id
)
self.assertEqual("1", test_prop.value)
test_type = Cvterm.objects.get(name="is_metadata_tag")
test_prop = Cvtermprop.objects.get(
cvterm_id=test_cvterm.cvterm_id, type_id=test_type.cvterm_id
)
self.assertEqual("1", test_prop.value)
test_type = Cvterm.objects.get(name="is_symmetric")
test_prop = Cvtermprop.objects.get(
cvterm_id=test_cvterm.cvterm_id, type_id=test_type.cvterm_id
)
self.assertEqual("1", test_prop.value)
test_type = Cvterm.objects.get(name="is_transitive")
test_prop = Cvtermprop.objects.get(
cvterm_id=test_cvterm.cvterm_id, type_id=test_type.cvterm_id
)
self.assertEqual("1", test_prop.value)
test_dbxref = Dbxref.objects.get(accession="0123")
test_cvterm_dbxref = CvtermDbxref.objects.get(
cvterm=test_cvterm, dbxref=test_dbxref
)
self.assertEqual(0, test_cvterm_dbxref.is_for_definition)
def run(self):
self.node_header.extend(["comments", "description"])
hpo_node_type = "biolink:PhenotypicFeature"
hpo_edge_label = "rdfs:subClassOf"
hpo_ro_relation = "RO:0002351"
hpo_obo_file = os.path.join(self.input_base_dir, "hp.obo")
# transform data, something like:
with open(self.output_node_file, 'w') as node, \
open(self.output_edge_file, 'w') as edge:
# write headers (change default node/edge headers if necessary
node.write("\t".join(self.node_header) + "\n")
edge.write("\t".join(self.edge_header) + "\n")
graph = obonet.read_obo(hpo_obo_file)
for id_, data in graph.nodes(data=True):
# Write HPO nodes
self.write_hpo_node(node, id_, data, hpo_node_type)
# if we see is_a relationship(s), write parent-child edge(s)
if 'is_a' in data:
for parent in data['is_a']:
self.write_hpo_edge(edge, id_, hpo_edge_label, parent,
hpo_ro_relation)
def _sample_graph(prefix):
from obonet import read_obo
from urllib.error import HTTPError
url = f'http://purl.obolibrary.org/obo/{prefix}.obo'
try:
graph = read_obo(url)
except HTTPError:
print(
f'{prefix} URL invalid {url}. See: http://www.obofoundry.org/ontology/{prefix}'
)
return False
except ValueError:
print(
f'Issue parsing {url}. See: http://www.obofoundry.org/ontology/{prefix}'
)
return False
nodes = (node for node in graph if node.lower().startswith(prefix))
nodes = [node for node, _ in zip(nodes, range(10))]
if not nodes:
print(f'No own terms in {prefix}')
for node in nodes:
print(' example', node)
if all(len(nodes[0]) == len(node) for node in nodes[1:]):
return len(nodes[0]) - 1 - len(prefix)
def load_chebi():
"""Load ChEBI ontology from local file 'chebi.obo' or from online source.
Ensures:
ontology_graph: is a MultiDiGraph object from Networkx representing ChEBI ontology;
name_to_id: is dict with mappings between each ontology concept name and the respective ChEBI id;
synonym_to_id: is dict with mappings between each ontology concept name and the respective ChEBI id;
"""
print("Loading ChEBI ontology...")
graph = obonet.read_obo("chebi.obo") # Load the ontology from local file
# Add root concept to the graph
root_concept = "CHEBI:00000"
graph.add_node(root_concept, name="ROOT")
graph = graph.to_directed()
# Create mappings
name_to_id, synonym_to_id, edge_list = {}, {}, []
for node in graph.nodes(data=True):
node_id, node_name = node[0], node[1]["name"]
name_to_id[node_name] = node_id
if 'is_a' in node[1].keys(
): # The root node of the ontology does not have is_a relationships
for related_node in node[1][
'is_a']: # Build the edge_list with only "is-a" relationships
relationship = (node[0], related_node)
edge_list.append(relationship)
if "synonym" in node[1].keys(
): # Check for synonyms for node (if they exist)
for synonym in node[1]["synonym"]:
synonym_name = synonym.split("\"")[1]
synonym_to_id[synonym_name] = node_id
# Create a MultiDiGraph object with only "is-a" relations - this will allow the further calculation of shorthest path lenght
ontology_graph = nx.MultiDiGraph([edge for edge in edge_list])
# Add edges between the ontology root and sub-ontology roots
chemical_entity = "CHEBI:24431"
role = "CHEBI:50906"
subatomic_particle = "CHEBI:36342"
application = "CHEBI:33232"
ontology_graph.add_node(root_concept, name="ROOT")
ontology_graph.add_edge(chemical_entity, root_concept, edgetype='is_a')
ontology_graph.add_edge(role, root_concept, edgetype='is_a')
ontology_graph.add_edge(subatomic_particle, root_concept, edgetype='is_a')
ontology_graph.add_edge(application, root_concept, edgetype='is_a')
print("Is ontology_graph acyclic:",
nx.is_directed_acyclic_graph(ontology_graph))
print("ChEBI loading complete")
return ontology_graph, name_to_id, synonym_to_id
def test_store_relationship(self):
"""Tests - store relationship."""
directory = os.path.dirname(os.path.abspath(__file__))
file = os.path.join(directory, "data", "so_fake.obo")
with open(file) as obo_file:
G = obonet.read_obo(obo_file)
cv_name = G.graph["default-namespace"][0]
cv_definition = G.graph["data-version"]
# Initializing ontology
ontology = OntologyLoader(cv_name, cv_definition)
for typedef in G.graph["typedefs"]:
ontology.store_type_def(typedef)
for n, data in G.nodes(data=True):
ontology.store_term(n, data)
for u, v, type in G.edges(keys=True):
ontology.store_relationship(u, v, type)
# Testing store_term
test_subject_dbxref = Dbxref.objects.get(accession="0000013")
test_subject_cvterm = Cvterm.objects.get(dbxref=test_subject_dbxref)
self.assertEqual("scRNA", test_subject_cvterm.name)
test_object_dbxref = Dbxref.objects.get(accession="0000012")
test_object_cvterm = Cvterm.objects.get(dbxref=test_object_dbxref)
self.assertEqual("scRNA_primary_transcript", test_object_cvterm.name)
test_type = CvtermRelationship.objects.get(
subject=test_subject_cvterm, object=test_object_cvterm
)
test_type_cvterm = Cvterm.objects.get(cvterm_id=test_type.type_id)
self.assertEqual("derives_from", test_type_cvterm.name)
def test_read_obo(ontology):
"""
Test that reading ontology does not error.
"""
url = 'http://purl.obolibrary.org/obo/{}.obo'.format(ontology)
graph = obonet.read_obo(url)
assert graph
def main():
"""
Main block
"""
# Parse arguments
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--obo',
help='Path to HPO .obo file. [default: ' +
'http://purl.obolibrary.org/obo/hp.obo]',
default='http://purl.obolibrary.org/obo/hp.obo',
metavar='(file|url)')
parser.add_argument('-o',
'--outfile',
help='Path to outfile. ' + '[default: stdout]',
metavar='file')
args = parser.parse_args()
# Open connection to obo
hpo_g = obonet.read_obo(args.obo)
# Open connection to outfile
if args.outfile is None:
outfile = stdout
else:
outfile = open(args.outfile, 'w')
# Add header to outfile
outfile.write('#HPO\tdescription\n')
# Convert obo to tsv
obo2tsv(hpo_g, outfile)
def get_obo_graph(dir, restrict_go_nodes_list):
obo_graph = obonet.read_obo(dir)
alt_ids_dic = {}
for idx, node in obo_graph.nodes(data=True):
if 'alt_id' in node:
if type(node['alt_id']) == list:
for alt_id in node['alt_id']:
alt_ids_dic[alt_id] = idx
else:
alt_ids_dic[node['alt_id']] = idx
if restrict_go_nodes_list:
go_nodes_list = import_go_nodes_list()
curated_go_nodes_list = set()
for node in go_nodes_list:
if node in alt_ids_dic:
curated_go_nodes_list.add(alt_ids_dic[node])
elif node in obo_graph.nodes():
curated_go_nodes_list.add(node)
# TAKES TOO LONG; BETTER WORK WITH WHOLE GRAPH
# Refactor graph to reconnect nodes linked to deleted nodes.
# nodes_to_remove = []
# for node in obo_graph.nodes().keys():
# if node not in curated_go_nodes_list \
# and len(obo_graph._pred[node]) > 0 and len(obo_graph._succ[node]) > 0:
# for incoming_node in obo_graph._pred[node]:
# for outgoing_node, value in obo_graph._succ[node].items():
# obo_graph.add_edge(incoming_node, outgoing_node, key=list(value)[0])
# nodes_to_remove.append(node)
# obo_graph.remove_nodes_from(curated_go_nodes_list)
return obo_graph, alt_ids_dic, curated_go_nodes_list
def __init__(self, obo_file, hpo2gene, disease2hpo):
self._obofile = obo_file
self._disease_df = pd.read_csv(
disease2hpo,
sep="\t",
usecols=[0, 1, 2, 4],
names=['source', 'disease_id', 'disease', 'HPO_id'],
comment="#")
self._gene_df = pd.read_csv(hpo2gene,
sep="\t",
usecols=[0, 3],
names=['HPO_id', 'gene'],
comment="#")
self._HPO2gene = self._gene_df.groupby(
by='HPO_id')['gene'].apply(list).reset_index(name='genes')
self._disease2genes = self._disease_df.merge(self._HPO2gene,
on='HPO_id')
self._disease2genes = self._disease2genes.groupby(
by=['source', 'disease_id'])['genes'].agg(
genes=pd.NamedAgg(column='genes', aggfunc='sum'))
self._disease_df.set_index(['source', 'disease_id'], inplace=True)
self._gene_df.set_index('gene', inplace=True)
self._HPO2gene.set_index('HPO_id', inplace=True)
self._ontology, self._obsoletes = obonet.read_obo(obo_file)
self.id_to_name = {
id_: data.get('name')
for id_, data in self._ontology.nodes(data=True)
}
self.name_to_id = {
data['name']: id_
for id_, data in self._ontology.nodes(data=True) if 'name' in data
}
self.n_terms = len(self._ontology)
def _get_obo_via_obonet(prefix: str,
*,
url: Optional[str] = None,
local: bool = False) -> Obo:
"""Get the OBO file by prefix or URL."""
if url is None:
path = _ensure_obo_path(prefix)
elif local:
path = url
else:
path = get_prefix_obo_path(prefix)
if not os.path.exists(path):
logger.info('[%s] downloading OBO from %s to %s', prefix, url,
path)
urlretrieve(url, path)
logger.info('[%s] parsing with obonet from %s', prefix, path)
with open(path) as file:
graph = obonet.read_obo(
tqdm(file, unit_scale=True, desc=f'[{prefix}] parsing obo'))
if 'ontology' not in graph.graph:
logger.warning('[%s] missing "ontology" key', prefix)
graph.graph['ontology'] = prefix
elif not graph.graph['ontology'].isalpha():
logger.warning(
'[%s] ontology=%s has a strange format. replacing with prefix',
prefix, graph.graph['ontology'])
graph.graph['ontology'] = prefix
return Obo.from_obonet(graph)
def load_network(self, file_resources):
for file in file_resources:
if ".obo" in file:
network = obonet.read_obo(file_resources[file])
# network = network.reverse(copy=True)
node_list = np.array(network.nodes)
return network, node_list
def __init__(
self,
url='https://raw.githubusercontent.com/The-Sequence-Ontology/SO-Ontologies/master/so.obo'
):
self.url = url
self.graph = obonet.read_obo(url)
assert networkx.is_directed_acyclic_graph(self.graph)
self.ancestor_cache = dict()
def test_read_taxrank_path(extension):
"""
Test reading the taxrank ontology OBO file from paths. Includes reading
compressed paths.
"""
path = os.path.join(directory, 'data', 'taxrank.obo' + extension)
taxrank = obonet.read_obo(path)
assert len(taxrank) == 61
def test_read_brenda_subset():
"""
Test reading a subset of the BrendaTissue.obo file. This file does not set
the ontology tag. See https://github.com/dhimmel/obonet/issues/10.
"""
path = os.path.join(directory, 'data', 'brenda-subset.obo')
brenda = obonet.read_obo(path)
assert len(brenda) == 1
assert brenda.graph['name'] is None
def test_read_taxrank_url(extension):
"""
Test reading the taxrank ontology OBO file from paths. Includes reading
compressed paths.
"""
url = 'https://github.com/dhimmel/obonet/raw/master/tests/data/taxrank.obo'
url += extension
taxrank = obonet.read_obo(url)
assert len(taxrank) == 61
def test_presence_of_obsolete_nodes():
"""Test that we did, indeed, capture those obsolete entries"""
pytest.importorskip("networkx", minversion="2.0")
path = os.path.join(directory, 'data', 'brenda-subset.obo')
brenda = obonet.read_obo(path, ignore_obsolete=False)
nodes = brenda.nodes(data=True)
assert "BTO:0000311" in nodes
node = nodes['BTO:0000311']
assert node['is_obsolete'] == 'true'
def test_read_brenda_subset():
"""
Test reading a subset of the BrendaTissue.obo file. This file does not set
the ontology tag. See https://github.com/dhimmel/obonet/issues/10.
"""
path = os.path.join(directory, 'data', 'brenda-subset.obo')
brenda = obonet.read_obo(path)
assert len(brenda) == 1
assert brenda.graph['name'] is None
def test_read_taxrank_url(extension):
"""
Test reading the taxrank ontology OBO file from paths. Includes reading
compressed paths.
"""
url = 'https://github.com/dhimmel/obonet/raw/master/tests/data/taxrank.obo'
url += extension
taxrank = obonet.read_obo(url)
assert len(taxrank) == 61
def test_read_taxrank_file():
"""
Test reading the taxrank ontology OBO file.
"""
path = os.path.join(directory, 'data', 'taxrank.obo')
with open(path, 'rt') as read_file:
taxrank = obonet.read_obo(read_file)
assert len(taxrank) == 61
assert taxrank.node['TAXRANK:0000001']['name'] == 'phylum'
assert 'NCBITaxon:kingdom' in taxrank.node['TAXRANK:0000017']['xref']
def test_read_taxrank_path(extension, pathlike):
"""
Test reading the taxrank ontology OBO file from paths. Includes reading
compressed paths.
"""
path = os.path.join(directory, 'data', 'taxrank.obo' + extension)
if pathlike:
path = pathlib.Path(path)
taxrank = obonet.read_obo(path)
assert len(taxrank) == 61
def verify_obo(path):
"""
Verifies that the provided obo is a HUPO-PSI obo
:param path: path to obo file
:return: whether the file schema is HUPO-PSI
:rtype: bool
"""
loaded = obonet.read_obo(path)
return all([
loaded.graph['ontology'] == 'ms', # MS ontology
'publisher: HUPO Proteomics Standards Initiative Mass Spectrometry Standards Working Group and HUPO Proteomics '
'Standards Initiative Proteomics Informatics Working Group' in loaded.graph['remark'], # correct publisher
])
