def assemble_pybel(stmts, out_file_prefix):
"""Return a PyBEL Assembler"""
stmts = ac.filter_belief(stmts, 0.95)
stmts = ac.filter_top_level(stmts)
pba = PybelAssembler(stmts,
name='INDRA/REACH Korkut Model',
description='Automatically assembled model of '
'cancer signaling.',
version='0.0.10')
pba.make_model()
pybel.to_bel_path(pba.model, out_file_prefix + '.bel')
with open(out_file_prefix, 'wt') as f:
pybel.to_json_file(pba.model, f)
url = 'https://pybel.scai.fraunhofer.de/api/receive'
headers = {'content-type': 'application/json'}
requests.post(url, json=pybel.to_json(pba.model), headers=headers)
def from_indra_statements(statements, name=None, version=None, description=None):
"""Imports a model from :mod:`indra`.
:param list[indra.statements.Statement] statements: A list of statements
:param str name: The name for the BEL graph
:param str version: The version of the BEL graph
:param str description: The description of the BEL graph
:rtype: pybel.BELGraph
"""
from indra.assemblers import PybelAssembler
pba = PybelAssembler(
stmts=statements,
name=name,
version=version,
description=description
)
graph = pba.make_model()
return graph
ActiveForm(elk1_p, 'transcription', True),
IncreaseAmount(elk1_tscript, fos),
Conversion(hk1, [glu], [g6p]),
Complex([egfr, grb2, sos1]),
Autophosphorylation(p38_tab1, 'Y', '100'),
Transphosphorylation(egfr_dimer, 'Y', '1173'),
]
ev = Evidence('assertion', 'assertion', 'assertion', 'assertion')
for stmt in stmts:
stmt.evidence = [ev]
model_description = 'Test of INDRA Statement assembly into PyBEL.'
print("Assembling to PyBEL...")
pba = PybelAssembler(stmts,
name='INDRA_PyBEL_test',
description=model_description,
version='0.0.22')
belgraph = pba.make_model()
# Write to BEL file
pybel.to_bel_path(belgraph, 'simple_pybel.bel')
# Upload to PyBEL web
with open('pybel_model.json', 'wt') as f:
pybel.to_json_file(pba.model, f)
url = 'https://pybel.scai.fraunhofer.de/api/receive'
headers = {'content-type': 'application/json'}
requests.post(url, json=pybel.to_json(pba.model), headers=headers)
Complex([egfr, grb2, sos1]),
ActiveForm(sos1_bound, 'gef', True),
Autophosphorylation(p38_tab1, 'Y', '100'),
Transphosphorylation(egfr_dimer, 'Y', '1173'),
]
ev = Evidence('assertion', 'assertion', 'assertion', 'assertion')
for stmt in stmts:
stmt.evidence = [ev]
model_description = 'Test of INDRA Statement assembly into PyBEL.'
print("Assembling to PyBEL...")
pba = PybelAssembler(
stmts,
name='INDRA_PyBEL_test',
description=model_description,
authors='John Bachman',
)
belgraph = pba.make_model()
# Write to BEL file
#pybel.to_bel_path(belgraph, 'simple_pybel.bel')
# Upload to PyBEL web
#with open('pybel_model.json', 'wt') as f:
# pybel.to_json_file(pba.model, f)
#url = 'https://pybel.scai.fraunhofer.de/api/receive'
#headers = {'content-type': 'application/json'}
#requests.post(url, json=pybel.to_json(pba.model), headers=headers)
# Put in local database
prize_outpath = "../work/pybel_prize.tsv"
interactome_path = "../work/big_pybel_interactome2.tsv"
site_file = "../work/gsea_sites.rnk"
# Load the statements linking kinases/regulators to phospho sites
# in the data
stmts = ac.load_statements(stmts)
# Employ filters to reduce network size
stmts = ac.filter_grounded_only(stmts)
stmts = ac.filter_human_only(stmts)
stmts = ac.filter_genes_only(stmts)
# In this data, statements of these two types will not act on
# a short enough timescale to play a meaningful role
stmts = ac.filter_by_type(stmts, DecreaseAmount, invert=True)
stmts = ac.filter_by_type(stmts, IncreaseAmount, invert=True)
stmts = ac.filter_by_type(stmts, Complex, invert=True)
stmts = ac.filter_enzyme_kinase(stmts)
# Assemble a pybel graph from statements
pba = PybelAssembler(stmts)
pb_graph = make_model(pba)
signed_graph = to_signed_nodes(pb_graph)
gn_dict = get_gene_node_dict(signed_graph)
# Next we have to load the data file and assign values to
site_data = read_site_file(site_file)
dump_steiner_files(signed_graph, site_data, prize_outpath,
interactome_path)
def stmts_to_pybel_graph(stmts):
pba = PybelAssembler(stmts, name='INDRA/REACH Fallahi Eval Model',
description='Automatically assembled model.',
version='0.0.1')
pba.make_model()
return pba.to_signed_graph()
parser.add_argument("--grounded", action="store_true")
parser.add_argument("--human", action="store_true")
parser.add_argument("--gene", action="store_true")
parser.add_argument("--stmts")
parser.add_argument("--prize_outpath")
parser.add_argument("--interactome_outpath")
parser.add_argument("--site_file")
args = parser.parse_args()
stmts = args.stmts
# Load the statements linking kinases/regulators to phospho sites in the data
stmts = ac.load_statements(stmts)
if args.grounded:
stmts = ac.filter_grounded_only(stmts)
if args.human:
stmts = ac.filter_human_only(stmts)
if args.gene:
stmts = ac.filter_genes_only(stmts)
# Assemble a PyBEL graph from the stmts
pba = PybelAssembler(stmts)
pb_graph = pba.make_model()
signed_graph = to_signed_nodes(pb_graph)
gn_dict = get_gene_node_dict(signed_graph)
# Next we have to load the data file and assign values to
site_data = read_site_file(args.site_file)
dump_steiner_files(signed_graph, site_data, args.prize_outpath,
args.interactome_outpath)