def extract_phos():
with open(stmts_fname, 'rb') as fh:
model = pickle.load(fh)
stmts = []
for pmid, pmid_stmts in model.items():
for stmt in pmid_stmts:
if isinstance(stmt, Phosphorylation):
stmts.append(stmt)
logger.info('%d phosphorylations in RAS Machine' % len(stmts))
stmts = [s for s in stmts if s.enz is not None]
logger.info('%d phosphorylations with enzyme in RAS Machine' % len(stmts))
stmts_grounded = filter_grounded(stmts)
logger.info('%d grounded phosphorylations in RAS Machine' %
len(stmts_grounded))
stmts_enzkinase = filter_enzkinase(stmts_grounded)
logger.info('%d phosphorylations with kinase enzyme in RAS Machine' %
len(stmts_enzkinase))
sm = SiteMapper(default_site_map)
stmts_valid, _ = sm.map_sites(stmts_enzkinase)
logger.info('%d valid-sequence phosphorylations in RAS Machine' %
len(stmts_valid))
pa = Preassembler(hierarchies, stmts_valid)
stmts_unique = pa.combine_duplicates()
logger.info('%d unique phosphorylations in RAS Machine' %
len(stmts_unique))
stmts_unique = pa.combine_related()
logger.info('%d top-level phosphorylations in RAS Machine' %
len(stmts_unique))
with open('mapped_unique_phos.pkl', 'wb') as fh:
pickle.dump(stmts_unique, fh, protocol=2)
# Filter RAS Machine statements for direct and not hypothesis
stmts = filter_direct(stmts_unique)
logger.info('%d direct phosphorylations in RAS Machine' % len(stmts))
stmts = filter_non_hypothesis(stmts)
logger.info('%d non-hypothesis phosphorylations in RAS Machine' %
len(stmts))
with open('filtered_phos.pkl', 'wb') as fh:
pickle.dump(stmts, fh, protocol=2)
return stmts
def map_sequence(stmts_in, **kwargs):
"""Map sequences using the SiteMapper.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to map.
do_methionine_offset : boolean
Whether to check for off-by-one errors in site position (possibly)
attributable to site numbering from mature proteins after
cleavage of the initial methionine. If True, checks the reference
sequence for a known modification at 1 site position greater
than the given one; if there exists such a site, creates the
mapping. Default is True.
do_orthology_mapping : boolean
Whether to check sequence positions for known modification sites
in mouse or rat sequences (based on PhosphoSitePlus data). If a
mouse/rat site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
do_isoform_mapping : boolean
Whether to check sequence positions for known modifications
in other human isoforms of the protein (based on PhosphoSitePlus
data). If a site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of mapped statements.
"""
logger.info('Mapping sites on %d statements...' % len(stmts_in))
kwarg_list = ['do_methionine_offset', 'do_orthology_mapping',
'do_isoform_mapping']
sm = SiteMapper(default_site_map)
valid, mapped = sm.map_sites(stmts_in, **_filter(kwargs, kwarg_list))
correctly_mapped_stmts = []
for ms in mapped:
if all([True if mm[1] is not None else False
for mm in ms.mapped_mods]):
correctly_mapped_stmts.append(ms.mapped_stmt)
stmts_out = valid + correctly_mapped_stmts
logger.info('%d statements with valid sites' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out
gene_counts = make_bar_plot(sites)
"""
# This script does two things:
# 1) Plots stats on invalid sites from databases
# - showing their frequency
# - per site
# - per reaction
# 2) Showing the fraction of the invalid sites in DBs that are mapped
# - per site
# - per reaction
# 3) Showing accuracy:
# - that the mapped sites are likely legit
# - and that the unmapped sites are likely errors
sm = SiteMapper(default_site_map)
with open('smcache.pkl', 'rb') as f:
(sm._cache, sm._sitecount) = pickle.load(f)
# Load the agent files
agent_files = [
'pc_pid_modified_agents.pkl', 'pc_psp_modified_agents.pkl',
'pc_reactome_modified_agents.pkl'
]
# For each set of mods
all_sites = []
for agent_file in agent_files:
db_name = agent_file.split('_')[1]
sites = map_agents(agent_file, sm, db_name)
all_sites += sites
print("Stats for %s -------------" % db_name)
