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check+annotate_metabolites.py
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import sys
import os
import libsbml
from tqdm import tqdm
import pandas as pd
from itertools import product
from bioservices.kegg import KEGG
from requests.exceptions import HTTPError, RequestException
import helper_functions as hf
'''
Usage: check+annotate_metabolites.py <path_input_sbml-file> <outfile-csv> <program_name> <program_version>
<tolerate_charge_hydrogen_balancing> : -chBal, if +1 charge should correspond to +1 H-atom
Takes formulas from the notes field and fbc-plugin, if none are found, BiGG-DB is searched for a formula.
If multiple or no possibilities are given in BiGG, a csv-formatted table with these metabolites is returned.
Only searches info, but does not change the model.
'''
def main(args):
# console access
if len(args) < 3:
print(main.__doc__)
sys.exit(1)
infile = args[1]
outfile_mismatches = args[2]
outfile_formula_search = args[3]
tolerate_ch_h_bal = "-chBal" in args
if not os.path.exists(infile):
print("[Error] %s : No such file." % infile)
sys.exit(1)
# create Readers and Writers
reader = libsbml.SBMLReader()
# Read SBML File
doc = reader.readSBML(infile)
model = doc.getModel()
# Knowledge base preparation
# bigg_db = pd.read_csv("Databases/BiGG/bigg_models_metabolites.tsv", sep='\t')
mnx_db = pd.read_csv("Databases/MetaNetX/chem_prop.tsv", header=351, sep='\t')
mnx_db.rename(columns={'#ID': 'id'}, inplace=True)
mnx_db.fillna("", inplace=True)
seed_db = pd.read_csv("Databases/SEED/compounds.tsv", header=0, sep="\t")
seed_db.fillna("", inplace=True)
kegg = KEGG()
req = kegg.lookfor_organism('finegoldia magna')[0].split(' ')
entry = req[0] # "T00661"
org_code = req[1] # 'fma'
# -------- formula check against knowledge bases ---------
start = 0
if os.path.exists(outfile_mismatches):
mismatches_old = pd.read_csv(outfile_mismatches, sep="\t", index_col=0)
start = max(start, int(mismatches_old.tail(1)["model_index"][1]) + 1)
mismatches_old = None
if os.path.exists(outfile_formula_search):
formula_searches_old = pd.read_csv(outfile_formula_search, sep="\t", index_col=0)
start = max(start, int(formula_searches_old.tail(1)["model_index"][1]) + 1)
mismatches_old = None
mismatches = pd.DataFrame(columns=["model_index", "name", "spec_id", "ids_biocyc", "ids_metanetx", "ids_seed",
"formula_bigg", "formula_biocyc", "formula_metanetx", "formula_seed",
"formula_model",
"charge_bigg", "charge_biocyc", "charge_metanetx", "charge_seed",
"charge_model", "matching_db"])
formula_search = pd.DataFrame(columns=["model_index", "name", "spec_id", "formula_model",
"ids_biocyc", "ids_mnx", "ids_seed", "ids_kegg"])
form_comp = [False, False, False, False, False]
num_spec = model.getNumSpecies()
for i in tqdm(range(start, num_spec)):
# --------- Knowledge collection ---------
spec_id = str(model.getSpecies(i).getId())
try:
# Check for formula in model
formula_model = []
charge_model = []
name_model = ""
if model.getSpecies(i).getPlugin('fbc').isSetChemicalFormula():
formula_model = str(model.getSpecies(i).getPlugin('fbc').getChemicalFormula())
if model.getSpecies(i).getPlugin('fbc').isSetCharge():
charge_model = model.getSpecies(i).getPlugin('fbc').getCharge()
name_model = model.getSpecies(i).getName()
# BiGG formulas - commented out: extraction from tsv (contains no formula and charge)
# bigg_query = bigg_db.loc[bigg_db['bigg_id'] == pruned_id]
bigg_query = hf.bigg_request(spec_id[2:-2], "metabolites")
formulas_bigg = bigg_query["formulae"]
charges_bigg = bigg_query["charges"]
try:
inchikey = bigg_query["database_links"]["InChi Key"]["id"]
except KeyError:
inchikey = False
# Biocyc
formulas_biocyc = []
charges_biocyc = []
ids_biocyc = []
biocyc_req = hf.biocyc_request("GCF_000010185", "BIGG", spec_id[2:-2])
if not biocyc_req[0]["STATUS"] == 1:
biocyc_req = hf.biocyc_get_from_formula("GCF_000010185", formula_model)
form_comp[1] = True
if biocyc_req[0]["STATUS"] == 1:
for res in biocyc_req[0]["RESULTS"]:
ids_biocyc.append(res["ID"])
try:
biocyc_tree = hf.biocyc_get(id_org="meta", id_biocyc=res["ID"], detail="low")
charges_biocyc.append(
int(biocyc_tree["ptools-xml"]["Compound"]["cml"]["molecule"]["@formalCharge"]))
formulas_biocyc_str = biocyc_tree["ptools-xml"]["Compound"]["cml"]["molecule"]["formula"][
"@concise"]
formulas_biocyc.append(formulas_biocyc_str.replace(" ", ""))
except KeyError:
print(spec_id + ": no simple compound.")
except HTTPError or RequestException:
print(spec_id + " failed in biocyc request.")
# MetaNetX
charges_mnx = []
formulas_mnx = []
ids_mnx = []
if inchikey:
mnx_query = mnx_db.loc[mnx_db['InChiKey'] == inchikey]
elif name_model != "":
mnx_query = mnx_db.loc[mnx_db['name'] == name_model]
elif formula_model != "":
mnx_query = mnx_db.loc[mnx_db['formula'] == formula_model]
form_comp[2] = True
else:
mnx_query = pd.DataFrame({'formula': [], 'charge': []})
for idx, row in mnx_query.iterrows():
ids_mnx.append(row["id"])
if row["formula"] != "" and row["charge"] != "":
formulas_mnx.append(row['formula'])
charges_mnx.append(row['charge'])
# SEED
formulas_seed = []
charges_seed = []
ids_seed = []
search = seed_db['aliases'].str.contains("BiGG: " + spec_id[2:-2])
if search.any():
seed_query = seed_db.loc[search]
elif inchikey:
seed_query = seed_db.loc[seed_db['inchikey'] == inchikey]
elif name_model != "":
seed_query = seed_db.loc[seed_db['name'] == name_model]
elif formula_model != "":
seed_query = seed_db.loc[seed_db['formula'] == formula_model]
form_comp[3] = True
else:
seed_query = pd.DataFrame({'formula': [], 'charge': []})
for idx, row in seed_query.iterrows():
ids_seed.append(row['id'])
formulas_seed.append(row['formula'])
charges_seed.append(int(row['charge']))
# KEGG
ids_kegg = []
if formula_model != "":
kegg_query = kegg.find("compound", formula_model, "formula").split("\n")
form_comp[4] = True
else:
kegg_query = pd.DataFrame({'formula': [], 'charge': []})
for kq in kegg_query:
ids_kegg.append(kq.split("\t")[0])
except Exception as e:
print(spec_id)
raise e
# --------- Knowledge vs. current entry - comparison ---------
matching_dbs = []
formula_matching_ids = []
if not model.getSpecies(i).getPlugin('fbc').isSetChemicalFormula():
mismatches.loc[len(mismatches.index)] = [i, model.getSpecies(i).getName(),
spec_id, ids_biocyc, ids_mnx, ids_seed,
formulas_bigg, formulas_biocyc, formulas_mnx, formulas_seed,
formula_model,
charges_bigg, charges_biocyc, charges_mnx, charges_seed,
charge_model,
matching_dbs]
continue
formulas_all = [formulas_bigg, formulas_biocyc, formulas_mnx, formulas_seed]
charges_all = [charges_bigg, charges_biocyc, charges_mnx, charges_seed]
ids_all = [spec_id[2:-2], ids_biocyc, ids_mnx, ids_seed]
comparisons_bool = []
for j in range(len(formulas_all)):
if form_comp[j]:
formula_matching_ids.append(ids_all[j])
else:
if tolerate_ch_h_bal:
for_cha_product = list(product(formulas_all[j], charges_all[j]))
else:
for_cha_product = formulas_all[j]
for fcp in for_cha_product:
if tolerate_ch_h_bal and charges_all[j] and charge_model:
comparison = hf.compare_formulas([fcp[0], formula_model], [int(fcp[1]), charge_model])
comparisons_bool.append(comparison)
else:
comparison = hf.compare_formulas([fcp[0], formula_model])
comparisons_bool.append(comparison)
if comparison:
matching_dbs.append(j)
# --------- Collection in table ---------
if True not in comparisons_bool:
mismatches.loc[len(mismatches.index)] = [i, model.getSpecies(i).getName(),
spec_id, ids_biocyc, ids_mnx, ids_seed,
formulas_all[0], formulas_all[1], formulas_all[2], formulas_all[3],
formula_model,
charges_all[0], charges_all[1], charges_all[2], charges_all[3],
charge_model,
matching_dbs]
formula_search.loc[len(formula_search.index)] = [i, model.getSpecies(i).getName(), spec_id, formula_model,
ids_biocyc, ids_mnx, ids_seed, ids_kegg]
# in between saves
if i % 50 == 25:
if os.path.exists(outfile_mismatches):
mismatches_old = pd.read_csv(outfile_mismatches, sep="\t", index_col=0)
mismatches = pd.concat([mismatches_old, mismatches])
mismatches.reset_index(drop=True, inplace=True)
mismatches_old = None
mismatches.to_csv(outfile_mismatches, sep="\t")
mismatches = pd.DataFrame(
columns=["model_index", "name", "spec_id", "ids_biocyc", "ids_metanetx", "ids_seed",
"formula_bigg", "formula_biocyc", "formula_metanetx", "formula_seed",
"formula_model",
"charge_bigg", "charge_biocyc", "charge_metanetx", "charge_seed",
"charge_model", "matching_db"])
if os.path.exists(outfile_formula_search):
formula_search_old = pd.read_csv(outfile_formula_search, sep="\t", index_col=0)
formula_search = pd.concat([formula_search_old, formula_search])
formula_search.reset_index(drop=True, inplace=True)
formula_search_old = None
formula_search.to_csv(outfile_formula_search, sep="\t")
formula_search = pd.DataFrame(columns=["model_index", "name", "spec_id", "formula_model",
"ids_biocyc", "ids_mnx", "ids_seed", "ids_kegg"])
# Exporting mismatches and formula search results
mismatches_old = pd.read_csv(outfile_mismatches, sep="\t", index_col=0)
mismatches = pd.concat([mismatches_old, mismatches])
mismatches.reset_index(drop=True, inplace=True)
mismatches_old = None
mismatches.to_csv(outfile_mismatches, sep="\t")
formula_search_old = pd.read_csv(outfile_formula_search, sep="\t", index_col=0)
formula_search = pd.concat([formula_search_old, formula_search])
formula_search.reset_index(drop=True, inplace=True)
formula_search_old = None
formula_search.to_csv(outfile_formula_search, sep="\t")
if __name__ == '__main__':
main(sys.argv)