def print_alignment_kegg(model):
f = open("cor.txt")
f_o = open("cor_readable.txt", "w")
kegg = KEGG()
for i in f:
if ":***:" in i:
k, b = i.split(":***:")
b = b.strip()
if not k == "MULTIR":
k = kegg.get(k)
i1 = k.find("NAME") + 4
i2 = k[i1:].find("\n")
k = k[i1:i1 + i2].strip()
if not b == "MULTIR":
b = model.reactions[b]
print(k, ":***:", b)
f_o.write(k + ":***:" + b + "\n")
f.close()
f_o.close()
def pathwayInfo(code):
# Function to get info about a pathway, from the code
# Intialize searcher
kSearcher = KEGG()
# Get result and parse it in a dictionnary
result = kSearcher.get(code)
# Add code at the begining of the list
dictResult = kSearcher.parse(result)
# Initialize an empty list
pathwayList = []
# If name exist as a key in dictionnary, else 'NA' insted
pathwayList.append(code)
if 'NAME' in dictResult.keys():
# If pathway name is a string comma separated, replace comma by semicolon
# Fix to avoid wrong column formating at the end of the script
nameStr = str(dictResult['NAME'][0].replace(',', ';'))
pathwayList.append(nameStr)
else:
pathwayList.append('NA')
# If class exist as a key in dictionnary, else 'NA' instead
if 'CLASS' in dictResult.keys():
# If pathway name is a string comma separated, replace comma by semicolon
# Fix to avoid wrong column formating at the end of the script
classStr = str(dictResult['CLASS']).replace(',', ';')
pathwayList.append(classStr)
else:
pathwayList.append('NA')
return pathwayList
def extract_sequences(dict, flist):
'''
Get orthologs sequences on KEGG and write to a fasta file for each kegg id
arg: dictionnary with keggid as key and orthologs as value (list)
'''
k = KEGG()
ocount = {}
#loop through orthologs dictionnary to get sequences from kegg
for key, list in dict.items():
#print(key)
if (key + ".fas") in flist:
print(key + " is already created !!!")
continue
#create string with sequences to write fasta file for each genes
string = ""
for x in range(0, len(list)):
for i in range(0, len(list[x])):
data_seq = k.get(list[x][i], option="ntseq", parse=True)
string = string + data_seq + "\n"
#print(data_seq)
print("writing : " + key + ".fas")
#write file
with open(os.path.join('orthologs_fastas/', key + '.fas'), 'w') as f:
read_data = f.write(string)
f.closed
def get_genes_from_kegg_pathway(pathway):
from bioservices.kegg import KEGG
k = KEGG()
k.organism = 'hsa'
pathway = k.get(pathway)
genes = k.parse(pathway)['GENE']
entrez, symbol = zip(*[i.split(' ') for i in genes])
return symbol
def get_kegg_info(stId):
"""
Get kegg dict by pathway id.
"""
k = KEGG()
data = k.get(stId)
dict_data = k.parse(data)
return dict_data
def retrieve_kegg_formula(reactome_compound_name):
k = KEGG()
compound_name = reactome_compound_name.replace('COMPOUND', 'cpd')
res = k.get(compound_name).split('\n')
for line in res:
if line.startswith('FORMULA'):
formula = line.split()[1] # get the second token
return formula
return None
def get_single_compound_metadata_online(compound_id):
if compound_id.upper().startswith('C'):
s = KEGG()
res = s.get(compound_id)
return s.parse(res)
else:
ch = ChEBI()
res = ch.getCompleteEntity('CHEBI:'+compound_id)
return res
def extract_orthologs(filename):
'''
Create dictionnary with keggid as key and list of orthologs as value
arg: csv with keggids
return : dict with orthologs
'''
orthos_dict = {}
k = KEGG()
#get list of gammaproteobacteria from csv
df = pd.read_csv(filename, sep="\t", tupleize_cols=1)
df_gamma = pd.read_csv('gammaproteo.csv', sep="\t", tupleize_cols=1)
gamma_list = df_gamma['KEGG'].tolist()
#loop through keggid to get orthologs
for keggid in df['kegg_id']:
if keggid == "no":
continue
print(str(keggid))
ortho_list = []
#get orthologs on kegg
data = k.get(keggid)
dict_data = k.parse(data)
if isinstance(dict_data, int):
continue
#loop through kegg orthologs data and verify that organisms are gammaproteobacteria
for key, value in dict_data['GENES'].items():
if key.lower() in gamma_list:
# print(key.lower(), value.split('(')[0].split())
para_num = len(value.split('(')[0].split())
para_list = []
for i in range(0, para_num):
#print(value.split('(')[0].split()[i])
para_list.append(key.lower() + ":" +
value.split('(')[0].split()[i])
ortho_list.append(para_list)
orthos_dict[keggid] = ortho_list
return orthos_dict
def id2seq(self, hsa):
s = KEGG()
d = s.get(hsa)
dict_d = s.parse(d)
pattern = re.compile(r'\s+')
try:
seq = re.sub(pattern, '', dict_d['AASEQ'])
except:
seq = ''
#print('SEQ:', seq)
text_file = open("dummy.txt", "w")
text_file.write('>' + str(hsa) + '\n' + seq)
text_file.close()
return None
def get_metabs(KEGG, reac_id):
subs_list = []
prod_list = []
# Get reaction data from KEGG using a KEGG reaction ID
r_data = KEGG.get(reac_id)
# Parse the information retrieved
r_parsed = KEGG.parse(r_data)
# Split the equation into substrates and products
split_eq = re.split('<=>', r_parsed['EQUATION'])
# Remove the plus signs between the metabolites
subs_list = [s.strip() for s in split_eq[0].split('+')]
prod_list = [p.strip() for p in split_eq[1].split('+')]
return [subs_list, prod_list]
def get_compound_metadata_online(kegg_ids):
s = KEGG()
metadata_map = {}
for i in range(len(kegg_ids)):
try:
if i % 10 == 0:
print("Retrieving %d/%d KEGG records" % (i, len(kegg_ids)))
kegg_id = kegg_ids[i]
res = s.get(kegg_id)
d = s.parse(res)
first_name = d['NAME'][0]
first_name = first_name.replace(';', '') # strip last ';' character
metadata_map[kegg_id] = {'display_name': first_name}
except TypeError:
print('kegg_id=%s parsed_data=%s' % (kegg_id, d))
return metadata_map
def get_seq(filename):
'''
Create dictionnary with species as keys and sequences as values for an alignment
arg: filename with gene name
return: organism dictionnary with sequences
'''
k = KEGG()
records = list(SeqIO.parse(os.path.join('alignments_nogaps/',filename), "fasta"))
idlist = []
orglist = []
seqlist = []
orgdict = {}
#go through sequences and search for organism name on kegg
for record in records:
idsplit = (record.id).split('_',1)
id = idsplit[0] + ':' + idsplit[1]
handle = k.get(id)
if isinstance( handle, int ):
print(id)
continue
org = k.parse(handle)['ORGANISM']
org = org.split()
org = org[1] +" "+ org[2]
seqlist.append(list(str(record.seq)))
orglist.append(org)
idlist.append(id)
duplist = set(orglist)
# create dict with organism as key and sequences for organism as values
for org in duplist:
indices = [i for i, x in enumerate(orglist) if x == org]
seqs = []
for e in indices:
seqs.append(seqlist[e])
orgdict[org] = seqs
#print(orgdict)
return orgdict
def queryKegg(theIDs):
print("Currently querying KEGG...")
k = KEGG()
keggData = list()
IDlist = list()
for id in theIDs:
ids = id[3:]
query = k.find("acb", ids)
query = query.split('\t')
finalQuery = query[0]
data = k.get(finalQuery)
dictData = k.parse(data)
keggData.append(dictData)
IDlist.append(ids)
return keggData, IDlist
def main():
# Start KEGG interface
k = KEGG()
# Create a dict to store final result
data = dict()
# Read in KEGG gene ID & gene symbol pairs
with open("hsa_gene_list.json", "r") as g:
gene_data = json.load(g)
for gene in gene_data.keys():
print gene
g_data = k.get(gene)
g_prsd = k.parse(g_data)
data[gene] = g_prsd
with open('ginfo.json', 'w') as fw:
json.dump(data, fw)
def get_reaction_ECs_from_kegg(self):
self.reaction_ECs = defaultdict(set)
kegg = KEGG()
for r in self.model.reactions:
ECs = []
try:
reacts = r.split(" ")
for i in reacts:
if i not in self.reaction_ECs:
print("KEGG reaction", i)
ECs += kegg.parse(kegg.get(i))['ENZYME']
for e in ECs:
self.reaction_ECs[i].add(e)
except Exception as inst:
print(inst)
#for e in ECs:
# self.reaction_ECs[r].add(e)
print("EC data loaded from KEGG")
def enzymeInfo(code, ignored, stats, verbosity):
# Function to get info about an enzyme, from the code
# This function return a double list
# Intialize KEGG searcher
kSearch = KEGG(verbose=verbosity)
# Get result and parse it in a dictionnary
print(f"[+] Get info about enzyme {code}")
result = kSearch.get(code)
# If KEGG return an int, the enzyme code doesn't match in databases
if type(result) is int:
return False
else:
dictResult = kSearch.parse(result)
# Create prefix list, info about enzyme herself
prefixList = []
# Add code at the begining of the list
prefixList.append(code)
# If name is present as key, else 'NA' insted
if 'NAME' in dictResult.keys():
#prefixList.append(dictResult['NAME'])
# Convert names from list into a string
# with strop '[]' part, and replace initial separator , by ;
namesStr = str(dictResult['NAME']).strip("'[]'").replace(',', ';')
prefixList.append(namesStr)
else:
prefixList.append('NA')
# If definition is present as key, else 'NA' insted
if 'DEFINITION' in dictResult.keys():
# If definition is a string comma separated, replace comma by semicolon
# Fix to avoid wrong column formating at the end of the script
definitionStr = str(dictResult['DEFINITION']).replace(',', ';')
prefixList.append(definitionStr)
else:
prefixList.append('NA')
# If pathway exist as a key in result
if 'PATHWAY' in dictResult.keys():
# Get all pathways as keys in dictionnary
pathwayList = list(dictResult['PATHWAY'].keys())
# Create final list, which contain :
# - prefix (info about enzyme)
# - suffix list (info about each enzyme's pathways)
finalList = []
# Add suffix in final list
finalList.append(prefixList)
for pathway in pathwayList:
# If pathway not in ignored list
if pathway not in ignored:
print(f" [-] Get info about {pathway} pathway")
suffixList = pathwayInfo(pathway)
# Add number of pathway for stats
stats['NB_PATHWAY'] = stats['NB_PATHWAY'] + 1
# Add suffix of pathway in final list
finalList.append(suffixList)
# If enzyme have only 1 pathway and this pathway is in ignored list
# Bad luck !
elif len(pathwayList) == 1 and pathway in ignored:
print(
f" [!] Enzyme {code} have only 1 pathway : {pathway}")
print(f" [!] and this pathway is ignored")
# Add entries for stats
stats['ENZYME_ONLY_IGNORED_PATHWAY'] = stats[
'ENZYME_ONLY_IGNORED_PATHWAY'] + 1
stats['LIST_ENZYME_ONLY_IGNORED_PATHWAY'].append(code)
# Artificially create pathway entry, but empty
suffixList = ['NA']
# Add suffix of pathway in final list
finalList.append(suffixList)
else:
print(f" [!] Ignored pathway : {pathway}")
# Else, if pathway doesn't exist as a key in result
elif 'PATHWAY' not in dictResult.keys():
# Initialize an empty list
finalList = []
# Add suffix in final list
finalList.append(prefixList)
# Display a alert message
print(f"[!] No pathway detected for enzyme {code}\n")
# Increment number of failed pathway in stats and add enzyme in list
stats['MISSING_PATHWAY_IN_KEGG'] = stats[
'MISSING_PATHWAY_IN_KEGG'] + 1
stats['LIST_MISSING_PATHWAY_IN_KEGG'].append(code)
# Artificially create pathway entry, but empty
suffixList = ['NA']
# Add suffix of pathway in final list
finalList.append(suffixList)
return finalList
enzymes = ReadFile(args.include)
else:
log("Fetch enzymes from kegg")
enzymes = p.enzymeIds
log("%s enzymes fetched" % len(enzymes))
ecs = {}
if args.outfile:
hout = open(args.outfile, 'w')
else:
hout = sys.stdout
houtcsv = csv.writer(hout, delimiter='\t')
log("Fetch enzymes from kegg")
a = k.get(' '.join(enzymes))
log("Fetch enzymes from kegg")
for ec in enzymes:
ec = ec.replace("ec:", "")
if ec in exclude:
continue
l = []
try:
log("Fecthing %s from kegg" % ec)
result = k.get(ec)
except urllib2.HTTPError:
continue
parsed = p.parse(result)
# # Check if the enzyme is obsolete
if "Obsolete" in parsed["entry"]:
continue
t_feat = DataFrame(t_fa.transform(trans_n.T), index=trans_n.columns, columns=['Factor %d' % (i + 1) for i in range(3)])
print t_feat['Factor 2'].sort_values()
sns.set(style='ticks', context='paper', rc={'axes.linewidth': .3, 'xtick.major.width': .3, 'ytick.major.width': .3})
g = sns.pairplot(t_hfac, hue='type', palette=pal)
plt.savefig('%s/reports/transcriptomics_pairplot.pdf' % wd, bbox_inches='tight')
plt.close('all')
print '[INFO] Corr plotted!'
# -- Bioservices KEGG infomration
bioser = KEGG(cache=True)
bioser.organism = 'hsa'
# Get pathways
keggp = {p: bioser.get(p) for p in bioser.pathwayIds}
print '[INFO] Pathways fetched'
keggp_name = {p: re.findall('NAME\s+(.*)?\n', keggp[p])[0].split(' - ')[0] for p in keggp}
keggp_comp = {p: {c for keggc in re.findall('(COMPOUND.*?)\n[A-Z]', keggp[p], re.S)[0].split('\n') for c in re.findall('\s+(C[0-9]+)\s+', keggc)} for p in keggp if 'COMPOUND' in keggp[p]}
keggp_gene = {p: {g for keggg in re.findall('(GENE.*?)\n[A-Z]', keggp[p], re.S)[0].split('\n') for g in re.findall('\s+([A-Z]+.+);', keggg)} for p in keggp if 'GENE' in keggp[p]}
keggp_tf = {p: {tf for tf in tf_targets_dict if len(tf_targets_dict[tf].intersection(keggp_gene[p])) > 0} for p in keggp_gene}
print '[INFO] Pathways genes fetched'
keggp_comp_m = DataFrame([(p, m, 1) for p in keggp_comp for m in keggp_comp[p]], columns=['pathway', 'metabolite', 'value'])
keggp_comp_m = pivot_table(keggp_comp_m, index='pathway', columns='metabolite', values='value', fill_value=0)
print keggp_comp_m.head
keggp_gene_m = DataFrame([(p, m, 1) for p in keggp_comp for m in keggp_gene[p]], columns=['pathway', 'gene', 'value'])
keggp_gene_m = pivot_table(keggp_gene_m, index='pathway', columns='gene', values='value', fill_value=0)
print keggp_gene_m.head()
from bioservices.kegg import KEGG
output = open('eclist.txt', 'w')
kegg = KEGG()
pathway = kegg.get('ath00900')
dict_data = kegg.parse(pathway)
# print(dict_data)
# g = x.get('tbr03440:Tb11.01.0910/aaseq')
# print(g)
# res = x.parse_kgml_pathway("tbr03440")
# print(res['entries'][0])
# for key, value in dict_data['GENE'].items():
# print(key, value)
# for gene in dict_data['GENE']:
# output.write(gene + '\n')
for value in dict_data['GENE'].values():
EC = value.split('[EC:')[1]
EC = EC.split(']')[0]
EC = EC.replace(' ', '\n')
output.write(EC + '\n')
from bioservices.kegg import KEGG
kegg = KEGG()
pathway = kegg.get("ko01230")
dict_data = kegg.parse(pathway)
print(dict_data)
output = open("modules.txt", "w")
modules_dict = {}
for key in dict_data['MODULE'].keys():
pathway = kegg.get(key)
module_data = kegg.parse(pathway)
#print(module_data)
orthologs = []
for ortholog in module_data['ORTHOLOGY'].keys():
data = [ortholog, module_data['ORTHOLOGY'][ortholog]]
orthologs.append("_".join(data))
modules_dict[ortholog] = key
output.write('{}\t{}\t{}\t{}\n'.format(key, module_data['NAME'],
module_data['DEFINITION'],
"//".join(orthologs)))
def mapSpecies(mousepeptrackfilename):
RETRY_TIME = 20.0
mouseTohumanfilepath = os.path.join(os.getcwd(), 'MouseToHuman.tsv')
print("Extracting Mouse to Human Map data, job starts",
str(datetime.datetime.now()))
#increase the field size of CSV
csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2))
try:
urllib.urlretrieve(
'http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt',
mouseTohumanfilepath)
urllib.urlcleanup()
except:
print("Can't able to download MouseToHuman.tsv file!!")
colnameMousHu = [
'HomoloGene ID', 'Common Organism Name', 'NCBI Taxon ID', 'Symbol',
'EntrezGene ID', 'Mouse MGI ID', 'HGNC ID', 'OMIM Gene ID',
'Genetic Location', 'Genomic Coordinates (mouse: , human: )',
'Nucleotide RefSeq IDs', 'Protein RefSeq IDs', 'SWISS_PROT IDs'
]
mouseHumandata = []
homologID = []
with open(mouseTohumanfilepath) as mhtsvfile:
mhreader = csv.DictReader(mhtsvfile, delimiter='\t')
for mhrow in mhreader:
mhtemplist = []
for i in colnameMousHu:
mhtempdata = str(mhrow[i]).strip()
mhtemplist.append(mhtempdata)
if len(mhtemplist[-1].strip()) > 0:
homologID.append(mhtemplist[0])
mouseHumandata.append(mhtemplist)
homologID = list(set(homologID))
homologID.sort()
mousehumandic = {}
for homologidItem in homologID:
tempHumanHomoUniID = ''
tempMouseHomoUniID = ''
for item in mouseHumandata:
if homologidItem == item[0]:
if 'mouse' in item[1].strip().lower():
tempMouseHomoUniID = item[-1].strip()
else:
tempHumanHomoUniID = item[-1].strip()
if len(tempMouseHomoUniID.strip()) > 0 and len(
tempHumanHomoUniID.strip()) > 0 and tempHumanHomoUniID.strip(
).upper() != 'NA':
mousehumandic[tempMouseHomoUniID] = tempHumanHomoUniID
colname=['UniProtKB Accession','Protein','Gene','Organism','Peptide Sequence','Summary Concentration Range Data','All Concentration Range Data','All Concentration Range Data-Sample LLOQ Based','Peptide ID',\
'Special Residues','Molecular Weight','GRAVY Score','Transitions','Retention Time','Analytical inofrmation',\
'Gradients','AAA Concentration','CZE Purity','Panel','Knockout','LLOQ','ULOQ','Sample LLOQ','Protocol','Trypsin','QC. Conc. Data','Human UniProtKB Accession']
finalresult = []
finalresult.append(colname)
humanUniprotID = []
with open(mousepeptrackfilename) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
templist = []
for i in colname[:-1]:
tempdata = str(row[i]).strip()
templist.append(tempdata)
if len(str(templist[0]).strip()) > 0:
if templist[0].split('-')[0] in mousehumandic:
humanUniprotID.append(
mousehumandic[templist[0].split('-')[0]])
templist.append(mousehumandic[templist[0].split('-')[0]])
else:
templist.append('NA')
finalresult.append(templist)
with open(mousepeptrackfilename, 'wb') as pf:
pwriter = csv.writer(pf, delimiter='\t')
pwriter.writerows(finalresult)
disGenDataDicName = disGenData()
#disGenDataDicName='disGen.obj'
disGenDataDic = cPickle.load(open(disGenDataDicName, 'rb'))
unqhumanUniprotID = list(set(humanUniprotID))
humanUniprotfuncinfodic = {}
countProt = 0
for subcode in unqhumanUniprotID:
time.sleep(2)
drugbanklist = []
PN = 'NA'
GN = 'NA'
OG = 'NA'
OGID = 'NA'
dislist = []
unidislist = []
unidisURLlist = []
disgendislist = []
disgendisURLlist = []
GoIDList = []
GoNamList = []
GoTermList = []
GOinfo = []
try:
countProt += 1
if countProt % 1000 == 0:
print str(
countProt
), "th protein Protein Name, Gene, Organism Name,drug bank data,disease data job starts", str(
datetime.datetime.now())
SGrequestURL = "https://www.uniprot.org/uniprot/" + str(
subcode) + ".xml"
SGunifile = urllib.urlopen(SGrequestURL)
SGunidata = SGunifile.read()
SGunifile.close()
try:
SGunidata = minidom.parseString(SGunidata)
try:
drugdata = (SGunidata.getElementsByTagName('dbReference'))
for duItem in drugdata:
if (duItem.attributes['type'].value
).upper() == 'DRUGBANK':
try:
drugname = (str(
duItem.getElementsByTagName('property')
[0].attributes['value'].value).strip())
drugid = str(
duItem.attributes['id'].value).strip()
durl = '<a target="_blank" href="https://www.drugbank.ca/drugs/' + drugid + '">' + drugname + '</a>'
drugbanklist.append(durl)
except:
pass
if (duItem.attributes['type'].value
).strip() == 'NCBI Taxonomy':
try:
OGID = str(
duItem.attributes['id'].value).strip()
except:
pass
except IndexError:
pass
try:
godata = (SGunidata.getElementsByTagName('dbReference'))
for gItem in godata:
if (gItem.attributes['type'].value).upper() == 'GO':
try:
gonamedetails = (str(
gItem.getElementsByTagName('property')
[0].attributes['value'].value).strip()
).split(':')[1]
gotermdetails = (str(
gItem.getElementsByTagName('property')
[0].attributes['value'].value).strip()
).split(':')[0]
GoNamList.append(gonamedetails)
goid = str(
gItem.attributes['id'].value).strip()
GoIDList.append(goid)
tempGoTerm = None
if gotermdetails.lower() == 'p':
tempGoTerm = 'Biological Process'
if gotermdetails.lower() == 'f':
tempGoTerm = 'Molecular Function'
if gotermdetails.lower() == 'c':
tempGoTerm = 'Cellular Component'
GoTermList.append(tempGoTerm)
tempGOData = gonamedetails + ';' + goid + ';' + tempGoTerm
GOinfo.append(tempGOData)
except:
pass
if (gItem.attributes['type'].value
).strip() == 'NCBI Taxonomy':
try:
OGID = str(
gItem.attributes['id'].value).strip()
except:
pass
except IndexError:
pass
try:
try:
PN = (((SGunidata.getElementsByTagName('protein')[0]
).getElementsByTagName('recommendedName')[0]
).getElementsByTagName('fullName')[0]
).firstChild.nodeValue
except:
PN = (((SGunidata.getElementsByTagName('protein')[0]
).getElementsByTagName('submittedName')[0]
).getElementsByTagName('fullName')[0]
).firstChild.nodeValue
except IndexError:
pass
try:
try:
GN = ((
SGunidata.getElementsByTagName('gene')[0]
).getElementsByTagName('name')[0]).firstChild.nodeValue
except:
GN = 'NA'
except IndexError:
pass
try:
try:
OG = ((
SGunidata.getElementsByTagName('organism')[0]
).getElementsByTagName('name')[0]).firstChild.nodeValue
except:
OG = 'NA'
except IndexError:
pass
try:
disdata = SGunidata.getElementsByTagName('disease')
for dItem in disdata:
disname = ''
disshort = ''
disURL = ''
disID = ''
try:
disname = (dItem.getElementsByTagName('name')[0]
).firstChild.nodeValue
disID = (dItem.attributes['id'].value).upper()
except:
pass
try:
disshort = (dItem.getElementsByTagName('acronym')
[0]).firstChild.nodeValue
except:
pass
if len(disname.strip()) > 0:
disURL = '<a target="_blank" href="https://www.uniprot.org/diseases/' + disID + '">' + str(
disname.strip()) + '(' + str(
disshort) + ')' + '</a>'
dislist.append(
str(disname.strip()) + '(' + str(disshort) +
')')
unidislist.append(
str(disname.strip()) + '(' + str(disshort) +
')')
unidisURLlist.append(disURL)
except IndexError:
pass
except ExpatError:
pass
except IOError:
pass
drugbankdata = 'NA'
disdata = 'NA'
uniDisData = 'NA'
uniDisURLData = 'NA'
disgenDisData = 'NA'
disgenDisURLData = 'NA'
goiddata = 'NA'
gonamedata = 'NA'
gotermdata = 'NA'
goData = 'NA'
if GN != 'NA' and GN in disGenDataDic:
disgendislist = disGenDataDic[GN][0]
disgendisURLlist = disGenDataDic[GN][1]
if len(dislist) > 0:
dislist = dislist + disGenDataDic[GN][0]
else:
dislist = disGenDataDic[GN][0]
if len(GoIDList) > 0:
goiddata = '|'.join(list(set(GoIDList)))
if len(GoNamList) > 0:
gonamedata = '|'.join(list(set(GoNamList)))
if len(GoTermList) > 0:
gotermdata = '|'.join(list(set(GoTermList)))
if len(GOinfo) > 0:
goData = '|'.join(list(set(GOinfo)))
if len(drugbanklist) > 0:
drugbankdata = '|'.join(list(set(drugbanklist)))
if len(dislist) > 0:
disdata = '|'.join(list(set(dislist)))
if len(unidislist) > 0:
uniDisData = '|'.join(list(set(unidislist)))
if len(unidisURLlist) > 0:
uniDisURLData = '|'.join(list(set(unidisURLlist)))
if len(disgendislist) > 0:
disgenDisData = '|'.join(list(set(disgendislist)))
if len(disgendisURLlist) > 0:
disgenDisURLData = '|'.join(list(set(disgendisURLlist)))
humanUniprotfuncinfodic[subcode] = [
PN, GN, OG, OGID, disdata, uniDisData, uniDisURLData,
disgenDisData, disgenDisURLData, drugbankdata, goiddata,
gonamedata, gotermdata, goData
]
hudicfile = 'humanUniprotfuncinfodic.obj'
hudicf = open(hudicfile, 'wb')
pickle.dump(humanUniprotfuncinfodic, hudicf, pickle.HIGHEST_PROTOCOL)
hudicf.close()
print("Extracting KEGG pathway name, job starts",
str(datetime.datetime.now()))
hkeggdictfile = {}
huniproturl = 'https://www.uniprot.org/uploadlists/'
hk = KEGG()
for hkx in range(0, len(unqhumanUniprotID), 2000):
countProt += hkx + 2000
if countProt % 2000 == 0:
print(str(countProt), "th protein kegg job starts",
str(datetime.datetime.now()))
huniprotcodes = ' '.join(unqhumanUniprotID[hkx:hkx + 2000])
huniprotparams = {
'from': 'ACC',
'to': 'KEGG_ID',
'format': 'tab',
'query': huniprotcodes
}
while True:
try:
hkuniprotdata = urllib.urlencode(huniprotparams)
hkuniprotrequest = urllib2.Request(huniproturl, hkuniprotdata)
hkuniprotresponse = urllib2.urlopen(hkuniprotrequest)
for hkuniprotline in hkuniprotresponse:
hkudata = hkuniprotline.strip()
if not hkudata.startswith("From"):
hkuinfo = hkudata.split("\t")
if len(hkuinfo[1].strip()):
hkegg = hk.get(hkuinfo[1].strip())
hkudict_data = hk.parse(hkegg)
try:
try:
if len(str(hkuinfo[0]).strip()) > 5:
tempkeggData = '|'.join(
'{};{}'.format(key, value)
for key, value in
hkudict_data['PATHWAY'].items())
hkeggdictfile[hkuinfo[0].strip()] = [
hkudict_data['PATHWAY'].values(),
tempkeggData
]
except TypeError:
pass
except KeyError:
pass
break
except urllib2.HTTPError:
time.sleep(RETRY_TIME)
print(
'Hey, I am trying again until succeeds to get data from KEGG!',
str(datetime.datetime.now()))
pass
hkdicfile = 'humankeggdic.obj'
hkdicf = open(hkdicfile, 'wb')
pickle.dump(hkeggdictfile, hkdicf, pickle.HIGHEST_PROTOCOL)
hkdicf.close()
from bioservices.kegg import KEGG
k = KEGG()
path = k.get("K00855")
kdict = k.parse(path)
print(kdict)
help(kdict)
with open("play.out", "wt") as result:
result.write("\n".join(kdict.keys()))
def get_kegg_info(stId):
k = KEGG()
data = k.get(stId)
dict_data = k.parse(data)
return dict_data
import re
from bioservices.kegg import KEGG
# -- KEGG bioservice
bioser = KEGG(cache=True)
bioser.organism = 'hsa'
# Get pathways
keggp = {p: bioser.get(p) for p in bioser.pathwayIds}
print '[INFO] Pathways fetched'
# Get reactions
keggr = {r: bioser.get(r) for r in bioser.reactionIds}
print '[INFO] Reactions fetched'
# Get enzymes
kegge = {e: bioser.get(e) for e in bioser.enzymeIds}
print '[INFO] Enzymes fetched'
# keggc = {c: bioser.get(c) for c in bioser.compoundIds}
# print '[INFO] Compounds fetched'
#
# # Get modules
# keggm = {m: bioser.get(m) for m in bioser.moduleIds}
# print '[INFO] Modules fetched'
# -- KEGG methods
def get_pathway_names(pathways=None):
pathways_ = pathways if pathways else set(keggp)
return {p: re.findall('NAME\s+(.*)?\n', keggp[p])[0].split(' - ')[0] for p in pathways_}
def get_pathways_from_KEGG(model, update_existing=False):
"""
This function extracts pathway and subsystem information from KEGG by using the KEGG annotation of each reaction.
The pathways we use are the ones given here: https://www.genome.jp/kegg/pathway.html,
under heading 1.: Metabolism. However we don't use the *1.0 Global and overview maps* or
*1.12 Chemical structure and transformation maps*, because they don't
represent metabolic subsystems. What we here refer to as *subsustems* are the subheadings under Metabolism, i.e.:
- Carbohydrate metabolism
- Energy metabolism
- Lipid metabolism
- Nucleotide metabolism
- Amino acid metabolism
- Metabolism of other amino acids
- Glycan biosynthesis and metabolism
- Metabolism of cofactors and vitamins
- Metabolism of terpenoids and polyketides
- Biosynthesis of other secondary metabolites
- Xenobiotics biodegradation and metabolism
"""
from bioservices.kegg import KEGG
kegg = KEGG()
kegg_dict, kegg_overview_maps = _get_KEGG_pathways()
inverse_pathway_dict = _get_inverse_pathway_dict(kegg_dict)
for reaction in model.reactions:
# Skip reactions which already have an kegg.pathway annoatation
# if update_existing = False
if not update_existing:
try:
reaction.annotation["kegg.pathway"]
except KeyError:
pass
else:
# Skip this one
continue
try:
kegg_id = reaction.annotation["kegg.reaction"]
except KeyError:
continue
kegg_info = kegg.get(kegg_id, parse=True)
try:
full_kegg_pathways = kegg_info["PATHWAY"].values()
except:
continue
kegg_pathways = [
x for x in full_kegg_pathways if not x in kegg_overview_maps
]
try:
subsystem = list(
set([inverse_pathway_dict[x] for x in kegg_pathways]))
except:
print("Error!: ", reaction.id, kegg_pathways)
continue
print("KEGG Subsystem ", reaction.id, subsystem)
reaction.annotation["kegg.pathway"] = kegg_pathways
reaction.annotation["kegg.subsystem"] = subsystem
return model
def mapSpecies(mousepeptrackfilename):
#increase the field size of CSV
csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2))
uniproturl = 'https://www.uniprot.org/uploadlists/'
RETRY_TIME = 20.0
mdf= pd.read_csv(mousepeptrackfilename, delimiter='\t')
mouseGenes=list(mdf['Gene'].unique())
mouseGenes=[g for g in mouseGenes if str(g) !='nan']
mousehumandic={}
for gx in range(0,len(mouseGenes),1000):
genecodes=' '.join(mouseGenes[gx:gx+1000])
geneuniprotparams = {
'from':'GENENAME',
'to':'ACC',
'format':'tab',
'query':genecodes,
'columns':'id,genes(PREFERRED),organism-id,reviewed'
}
while True:
try:
geneuniprotdata = urllib.urlencode(geneuniprotparams)
geneuniprotrequest = urllib2.Request(uniproturl, geneuniprotdata)
geneuniprotresponse = urllib2.urlopen(geneuniprotrequest)
for guniprotline in geneuniprotresponse:
gudata=guniprotline.strip()
if not gudata.startswith("Entry"):
guinfo=gudata.split("\t")
if '9606' == guinfo[2].lower() and 'reviewed' == guinfo[3].lower() and guinfo[-1].lower() ==guinfo[1].lower() and len(guinfo[0].strip())>1:
mousehumandic[guinfo[-1].strip()]=guinfo[0].strip()
break
except urllib2.HTTPError:
time.sleep(RETRY_TIME)
print ('Hey, I am trying again until succeeds to get data from uniprot data!',str(datetime.datetime.now()))
except httplib.BadStatusLine:
time.sleep(RETRY_TIME)
print ('Hey, I am trying again until succeeds to get data from uniprot data!',str(datetime.datetime.now()))
colname=['UniProtKB Accession','Protein','Gene','Organism','Peptide Sequence','Summary Concentration Range Data','All Concentration Range Data','All Concentration Range Data-Sample LLOQ Based','Peptide ID',\
'Special Residues','Molecular Weight','GRAVY Score','Transitions','Retention Time','Analytical inofrmation',\
'Gradients','AAA Concentration','CZE Purity','Panel','Knockout','LLOQ','ULOQ','Sample LLOQ','Protocol','Trypsin','QC. Conc. Data','Human UniProtKB Accession']
finalresult=[]
finalresult.append(colname)
humanUniprotID=[]
with open(mousepeptrackfilename) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
templist=[]
for i in colname[:-1]:
tempdata=str(row[i]).strip()
templist.append(tempdata)
if len(str(templist[2]).strip())>0:
if templist[2] in mousehumandic:
huUniId=mousehumandic[templist[2]]
humanUniprotID.append(huUniId)
templist.append(huUniId)
else:
templist.append('NA')
finalresult.append(templist)
with open(mousepeptrackfilename,'wb') as pf:
pwriter =csv.writer(pf,delimiter='\t')
pwriter.writerows(finalresult)
unqhumanUniprotID=list(set(humanUniprotID))
humanUniprotfuncinfodic={}
countProt=0
for subcode in unqhumanUniprotID:
time.sleep(2)
drugbanklist=[]
PN='NA'
GN='NA'
OG='NA'
OGID='NA'
dislist=[]
GoIDList=[]
GoNamList=[]
GoTermList=[]
try:
countProt+=1
if countProt%1000 ==0:
print str(countProt), "th protein Protein Name, Gene, Organism Name,drug bank data,disease data job starts",str(datetime.datetime.now())
SGrequestURL="https://www.uniprot.org/uniprot/"+str(subcode)+".xml"
SGunifile=urllib.urlopen(SGrequestURL)
SGunidata= SGunifile.read()
SGunifile.close()
try:
SGunidata=minidom.parseString(SGunidata)
try:
drugdata=(SGunidata.getElementsByTagName('dbReference'))
for duItem in drugdata:
if (duItem.attributes['type'].value).upper() == 'DRUGBANK':
try:
drugname=(str(duItem.getElementsByTagName('property')[0].attributes['value'].value).strip())
drugid=str(duItem.attributes['id'].value).strip()
durl='<a target="_blank" href="https://www.drugbank.ca/drugs/'+drugid+'">'+drugname+'</a>'
drugbanklist.append(durl)
except:
pass
if (duItem.attributes['type'].value).strip() == 'NCBI Taxonomy':
try:
OGID=str(duItem.attributes['id'].value).strip()
except:
pass
except IndexError:
pass
try:
godata=(SGunidata.getElementsByTagName('dbReference'))
for gItem in godata:
if (gItem.attributes['type'].value).upper() == 'GO':
try:
gonamedetails=(str(gItem.getElementsByTagName('property')[0].attributes['value'].value).strip()).split(':')[1]
gotermdetails=(str(gItem.getElementsByTagName('property')[0].attributes['value'].value).strip()).split(':')[0]
GoNamList.append(gonamedetails)
goid=str(gItem.attributes['id'].value).strip()
GoIDList.append(goid)
if gotermdetails.lower()=='p':
GoTermList.append('Biological Process')
if gotermdetails.lower()=='f':
GoTermList.append('Molecular Function')
if gotermdetails.lower()=='c':
GoTermList.append('Cellular Component')
except:
pass
if (gItem.attributes['type'].value).strip() == 'NCBI Taxonomy':
try:
OGID=str(gItem.attributes['id'].value).strip()
except:
pass
except IndexError:
pass
try:
try:
PN=(((SGunidata.getElementsByTagName('protein')[0]).getElementsByTagName('recommendedName')[0]).getElementsByTagName('fullName')[0]).firstChild.nodeValue
except:
PN=(((SGunidata.getElementsByTagName('protein')[0]).getElementsByTagName('submittedName')[0]).getElementsByTagName('fullName')[0]).firstChild.nodeValue
except IndexError:
pass
try:
try:
GN=((SGunidata.getElementsByTagName('gene')[0]).getElementsByTagName('name')[0]).firstChild.nodeValue
except:
GN='NA'
except IndexError:
pass
try:
try:
OG=((SGunidata.getElementsByTagName('organism')[0]).getElementsByTagName('name')[0]).firstChild.nodeValue
except:
OG='NA'
except IndexError:
pass
try:
disdata=SGunidata.getElementsByTagName('disease')
for dItem in disdata:
disname=''
disshort=''
try:
disname=(dItem.getElementsByTagName('name')[0]).firstChild.nodeValue
except:
pass
try:
disshort=(dItem.getElementsByTagName('acronym')[0]).firstChild.nodeValue
except:
pass
if len(disname.strip())>0:
dislist.append(str(disname.strip())+'('+str(disshort)+')')
except IndexError:
pass
except ExpatError:
pass
except IOError:
pass
drugbankdata='NA'
disdata='NA'
goiddata='NA'
gonamedata='NA'
gotermdata='NA'
if len(GoIDList)>0:
goiddata='|'.join(list(set(GoIDList)))
if len(GoNamList)>0:
gonamedata='|'.join(list(set(GoNamList)))
if len(GoTermList)>0:
gotermdata='|'.join(list(set(GoTermList)))
if len(drugbanklist)>0:
drugbankdata='|'.join(list(set(drugbanklist)))
if len(dislist)>0:
disdata='|'.join(list(set(dislist)))
humanUniprotfuncinfodic[subcode]=[PN,GN,OG,OGID,disdata,drugbankdata,goiddata,gonamedata,gotermdata]
hudicfile='humanUniprotfuncinfodic.obj'
hudicf = open(hudicfile, 'wb')
pickle.dump(humanUniprotfuncinfodic, hudicf , pickle.HIGHEST_PROTOCOL)
hudicf.close()
print ("Extracting KEGG pathway name, job starts",str(datetime.datetime.now()))
hkeggdictfile={}
hk = KEGG()
for hkx in range(0,len(unqhumanUniprotID),2000):
countProt+=hkx+2000
if countProt%2000 ==0:
print (str(countProt), "th protein kegg job starts",str(datetime.datetime.now()))
huniprotcodes=' '.join(unqhumanUniprotID[hkx:hkx+2000])
huniprotparams = {
'from':'ACC',
'to':'KEGG_ID',
'format':'tab',
'query':huniprotcodes
}
while True:
try:
hkuniprotdata = urllib.urlencode(huniprotparams)
hkuniprotrequest = urllib2.Request(uniproturl, hkuniprotdata)
hkuniprotresponse = urllib2.urlopen(hkuniprotrequest)
for hkuniprotline in hkuniprotresponse:
hkudata=hkuniprotline.strip()
if not hkudata.startswith("From"):
hkuinfo=hkudata.split("\t")
if len(hkuinfo[1].strip()):
hkegg=hk.get(hkuinfo[1].strip())
hkudict_data = hk.parse(hkegg)
try:
try:
if len(str(hkuinfo[0]).strip()) >5:
hkeggdictfile[hkuinfo[0].strip()]=hkudict_data['PATHWAY'].values()
except TypeError:
pass
except KeyError:
pass
break
except urllib2.HTTPError:
time.sleep(RETRY_TIME)
print ('Hey, I am trying again until succeeds to get data from KEGG!',str(datetime.datetime.now()))
pass
hkdicfile='humankeggdic.obj'
hkdicf = open(hkdicfile, 'wb')
pickle.dump(hkeggdictfile, hkdicf , pickle.HIGHEST_PROTOCOL)
hkdicf.close()
EC2KO_dic[item].append(
ko
) #here append may be used, bc only one item is added at a time
else:
EC2KO_dic[item] = [ko]
else:
KO_dic[ko] = [definition]
#here a list of KOs is made to be searched for - if pathway is defined
kostoget = {}
if args.pathway != 'none':
output = open('eclist.txt', 'w')
kegg = KEGG()
pathway = kegg.get(args.pathway)
dict_data = kegg.parse(pathway)
if dict_data == 404:
print("WARNING: BAD PATHWAY SUBMITTED TO KEGG!")
elif dict_data == None:
print("WARNING: ERROR CONNECTING TO KEGG SERVER!")
#print(dict_data)
print("ECQUERY: PROCESSING PATHWAY/MODULE")
try:
for key in dict_data['ORTHOLOGY'].keys():
print("adding ortholog {} to eclist".format(key))
value = dict_data['ORTHOLOGY'][key]
print(value)
if "," in key:
for item in key.split(","):
kostoget[item] = value
def search(query, source="wikipathways", result_format="xml", species=None, genes=None, user=None):
path_array = []
if source.lower() in ["wikipathways", "all"] and species is None:
url = "http://webservice.wikipathways.org/"
ext = "/findPathwaysByText?query=" + str(query)
r = requests.get(url+ext, headers={"Content-Type": "application/json"})
if not r.ok:
r.raise_for_status()
sys.exit()
tree = ET.ElementTree(ET.fromstring(r.text))
root = tree.getroot()
for child in root:
temp_path_dict = {}
for subchild in child:
if subchild.tag == "{http://www.wikipathways.org/webservice}id":
temp_path_dict["identifier"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}score":
temp_path_dict["score"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}url":
temp_path_dict["url"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}name":
temp_path_dict["name"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}species":
temp_path_dict["species"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}revision":
temp_path_dict["revision"] = subchild.text
temp_path = gnomics.objects.pathway.Pathway(identifier = temp_path_dict["identifier"], identifier_type = "WikiPathways ID", name = temp_path_dict["name"], taxon = temp_path_dict["species"], source = "WikiPathways")
if temp_path_dict["identifier"] not in path_array:
path_array.append(temp_path)
elif source.lower() in ["wikipathways", "all"] and species is not None:
url = "http://webservice.wikipathways.org/"
ext = "/findPathwaysByText?query=" + str(query) + "&species=" + str(species)
r = requests.get(url+ext, headers={"Content-Type": "application/json"})
if not r.ok:
r.raise_for_status()
sys.exit()
tree = ET.ElementTree(ET.fromstring(r.text))
root = tree.getroot()
path_array = []
for child in root:
temp_path_dict = {}
for subchild in child:
if subchild.tag == "{http://www.wikipathways.org/webservice}id":
temp_path_dict["identifier"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}score":
temp_path_dict["score"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}url":
temp_path_dict["url"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}name":
temp_path_dict["name"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}species":
temp_path_dict["species"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}revision":
temp_path_dict["revision"] = subchild.text
temp_path = gnomics.objects.pathway.Pathway(identifier = temp_path_dict["identifier"], identifier_type = "WikiPathways ID", name = temp_path_dict["name"], taxon = temp_path_dict["species"], source = "WikiPathways")
if temp_path_dict["identifier"] not in path_array:
path_array.append(temp_path)
if source.lower() in ["kegg", "all"] and genes is not None:
k = KEGG()
elif source.lower() in ["kegg", "all"] and genes is None:
k = KEGG()
list_of_pathways = k.find("pathway", query)
temp_path_list = list_of_pathways.split("\n")
for thing in temp_path_list:
temp_split = thing.split("\t")
if len(temp_split) != 1:
path_id = temp_split[0].strip().split(":")[1]
path_name = temp_split[1].strip()
if "map" in path_id:
temp_path = gnomics.objects.pathway.Pathway(identifier=path_id, identifier_type="KEGG MAP PATHWAY ID", source="KEGG", name=path_name)
path_array.append(temp_path)
elif "ko" in path_id:
temp_path = gnomics.objects.pathway.Pathway(identifier=path_id, identifier_type="KEGG KO PATHWAY ID", source="KEGG", name=path_name)
path_array.append(temp_path)
elif "ec" in path_id:
temp_path = gnomics.objects.pathway.Pathway(identifier=path_id, identifier_type="KEGG EC PATHWAY ID", source="KEGG", name=path_name)
path_array.append(temp_path)
elif "rn" in path_id:
temp_path = gnomics.objects.pathway.Pathway(identifier=path_id, identifier_type="KEGG RN PATHWAY ID", source="KEGG", name=path_name)
path_array.append(temp_path)
else:
print(k.get(path_id))
return path_array
#from Bio import Entrez
from bioservices.kegg import KEGG
import sys
k = KEGG()
#Entrez.email = "*****@*****.**"
#file = open(sys.argv[1], "r")
file = open("../data/ids5.txt", "r")
result = ""
k.organism = "lpl"
k.get()
#for line in file.readlines():
#
file.close()
print(result)
# for line in file.readlines():
# handle = Entrez.esearch(db="pubmed", term=line)
# record = Entrez.read(handle)
# ids = record["IdList"]
# print(ids)
def search(query,
source="wikipathways",
result_format="xml",
species=None,
genes=None,
user=None):
path_array = []
if source.lower() in ["wikipathways", "all"] and species is None:
url = "http://webservice.wikipathways.org/"
ext = "/findPathwaysByText?query=" + str(query)
r = requests.get(url + ext,
headers={"Content-Type": "application/json"})
if not r.ok:
r.raise_for_status()
sys.exit()
tree = ET.ElementTree(ET.fromstring(r.text))
root = tree.getroot()
for child in root:
temp_path_dict = {}
for subchild in child:
if subchild.tag == "{http://www.wikipathways.org/webservice}id":
temp_path_dict["identifier"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}score":
temp_path_dict["score"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}url":
temp_path_dict["url"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}name":
temp_path_dict["name"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}species":
temp_path_dict["species"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}revision":
temp_path_dict["revision"] = subchild.text
temp_path = gnomics.objects.pathway.Pathway(
identifier=temp_path_dict["identifier"],
identifier_type="WikiPathways ID",
name=temp_path_dict["name"],
taxon=temp_path_dict["species"],
source="WikiPathways")
if temp_path_dict["identifier"] not in path_array:
path_array.append(temp_path)
elif source.lower() in ["wikipathways", "all"] and species is not None:
url = "http://webservice.wikipathways.org/"
ext = "/findPathwaysByText?query=" + str(query) + "&species=" + str(
species)
r = requests.get(url + ext,
headers={"Content-Type": "application/json"})
if not r.ok:
r.raise_for_status()
sys.exit()
tree = ET.ElementTree(ET.fromstring(r.text))
root = tree.getroot()
path_array = []
for child in root:
temp_path_dict = {}
for subchild in child:
if subchild.tag == "{http://www.wikipathways.org/webservice}id":
temp_path_dict["identifier"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}score":
temp_path_dict["score"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}url":
temp_path_dict["url"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}name":
temp_path_dict["name"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}species":
temp_path_dict["species"] = subchild.text
elif subchild.tag == "{http://www.wikipathways.org/webservice}revision":
temp_path_dict["revision"] = subchild.text
temp_path = gnomics.objects.pathway.Pathway(
identifier=temp_path_dict["identifier"],
identifier_type="WikiPathways ID",
name=temp_path_dict["name"],
taxon=temp_path_dict["species"],
source="WikiPathways")
if temp_path_dict["identifier"] not in path_array:
path_array.append(temp_path)
if source.lower() in ["kegg", "all"] and genes is not None:
k = KEGG()
elif source.lower() in ["kegg", "all"] and genes is None:
k = KEGG()
list_of_pathways = k.find("pathway", query)
temp_path_list = list_of_pathways.split("\n")
for thing in temp_path_list:
temp_split = thing.split("\t")
if len(temp_split) != 1:
path_id = temp_split[0].strip().split(":")[1]
path_name = temp_split[1].strip()
if "map" in path_id:
temp_path = gnomics.objects.pathway.Pathway(
identifier=path_id,
identifier_type="KEGG MAP PATHWAY ID",
source="KEGG",
name=path_name)
path_array.append(temp_path)
elif "ko" in path_id:
temp_path = gnomics.objects.pathway.Pathway(
identifier=path_id,
identifier_type="KEGG KO PATHWAY ID",
source="KEGG",
name=path_name)
path_array.append(temp_path)
elif "ec" in path_id:
temp_path = gnomics.objects.pathway.Pathway(
identifier=path_id,
identifier_type="KEGG EC PATHWAY ID",
source="KEGG",
name=path_name)
path_array.append(temp_path)
elif "rn" in path_id:
temp_path = gnomics.objects.pathway.Pathway(
identifier=path_id,
identifier_type="KEGG RN PATHWAY ID",
source="KEGG",
name=path_name)
path_array.append(temp_path)
else:
print(k.get(path_id))
return path_array
def pathwayVisualization(KEGG_id, path_to_csv, redirect=True, compound=False):
"""
The pathwayVisualization function returns a graph visualization based on user input
Args:
KEGG_id (str): string specifying KEGG pathway ID to visualize
path_to_csv (str): string specifying data to overlay on graph
redirect (bool): True to split nodes into their components. Defaults to True
compound (bool): True to display compounds (such as Ca2+). Defaults to False
Returns:
A graph visualization using the visjs_network function from visjs_2_jupyter
"""
s = KEGG()
res = s.get(KEGG_id, "kgml")
if res == 404 or res == 400:
print KEGG_id + ' is not a valid KEGG ID'
return
result = s.parse_kgml_pathway(KEGG_id)
ETroot = parsingXML(KEGG_id, s)
G=nx.DiGraph()
max_id, compound_array = addNodes(G, result)
setCoord(G, ETroot)
if redirect is False:
getNodeSymbols(G, s, compound)
else:
parent_list, parent_dict = splitNodes(G, s, max_id)
complex_array, component_array, node_dict, comp_dict = undefNodes(G, ETroot)
if redirect is False:
addEdges(G, result, component_array, node_dict)
else:
addAndRedirectEdges(G, result, complex_array, component_array, parent_list, parent_dict, node_dict, comp_dict)
#add reactions to graph
addReaction(G, ETroot)
edge_to_name = dict()
for edge in G.edges():
if G.edge[edge[0]][edge[1]]['name'] == 'phosphorylation':
edge_to_name[edge] = G.edge[edge[0]][edge[1]]['value']
elif G.edge[edge[0]][edge[1]]['name'] == 'dephosphorylation':
edge_to_name[edge] = G.edge[edge[0]][edge[1]]['value']
elif 'dephosphorylation' in G.edge[edge[0]][edge[1]]['name']:
edge_to_name[edge] = G.edge[edge[0]][edge[1]]['name'].replace('dephosphorylation', '-p')
elif 'phosphorylation' in G.edge[edge[0]][edge[1]]['name']:
edge_to_name[edge] = G.edge[edge[0]][edge[1]]['name'].replace('phosphorylation', '+p')
else:
edge_to_name[edge] = G.edge[edge[0]][edge[1]]['name']
edge_to_name[edge] = edge_to_name[edge].replace('activation, ', '')
edge_to_name[edge] = edge_to_name[edge].replace('inhibition, ', '')
edge_to_name[edge] = edge_to_name[edge].replace('activation', '')
edge_to_name[edge] = edge_to_name[edge].replace('inhibition', '')
#edges are transparent
edge_to_color = dict()
for edge in G.edges():
if 'activation' in G.edge[edge[0]][edge[1]]['name']:
edge_to_color[edge] = 'rgba(26, 148, 49, 0.3)' #green
elif 'inhibition' in G.edge[edge[0]][edge[1]]['name']:
edge_to_color[edge] = 'rgba(255, 0, 0, 0.3)' #red
else:
edge_to_color[edge] = 'rgba(0, 0, 255, 0.3)' #blue
#for graph with split nodes
if redirect is True:
#remove undefined nodes from graph
G.remove_nodes_from(complex_array)
#remove nodes with more than one gene
G.remove_nodes_from(parent_list)
if compound is False:
#remove compound nodes
G.remove_nodes_from(compound_array)
node_to_symbol = dict()
for node in G.node:
if G.node[node]['type'] == 'map':
node_to_symbol[node] = G.node[node]['gene_names']
else:
if 'symbol' in G.node[node]:
node_to_symbol[node] = G.node[node]['symbol']
elif 'gene_names'in G.node[node]:
node_to_symbol[node] = G.node[node]['gene_names']
else:
node_to_symbol[node] = G.node[node]['name']
# getting name of nodes
node_to_gene = dict()
for node in G.node:
node_to_gene[node] = G.node[node]['gene_names']
# getting x coord of nodes
node_to_x = dict()
for node in G.node:
node_to_x[node] = G.node[node]['x']
# getting y coord of nodes
node_to_y = dict()
for node in G.node:
node_to_y[node] = G.node[node]['y']
id_to_log2fold = log2FoldChange(G, path_to_csv)
# Create color scale with negative as green and positive as red
my_scale = spectra.scale([ "green", "#CCC", "red" ]).domain([ -4, 0, 4 ])
# color nodes based on log2fold data
node_to_color = dict()
for node in G.nodes():
if node in id_to_log2fold:
node_to_color[node] = my_scale(id_to_log2fold[node][0]).hexcode
else:
node_to_color[node] = '#f1f1f1'
# getting nodes in graph
nodes = G.nodes()
numnodes = len(nodes)
node_map = dict(zip(nodes,range(numnodes))) # map to indices for source/target in edges
# getting edges in graph
edges = G.edges()
numedges = len(edges)
# dictionaries that hold per node and per edge attributes
nodes_dict = [{"id":node_to_gene[n],"degree":G.degree(n),"color":node_to_color[n], "node_shape":"box",
"node_size":10,'border_width':1, "id_num":node_to_symbol[n], "x":node_to_x[n], "y":node_to_y[n]} for n in nodes]
edges_dict = [{"source":node_map[edges[i][0]], "target":node_map[edges[i][1]],
"color":edge_to_color[edges[i]], "id":edge_to_name[edges[i]], "edge_label":'',
"hidden":'false', "physics":'true'} for i in range(numedges)]
# html file label for first graph (must manually increment later)
time = 1700
# create graph here
#return G
return visJS_module.visjs_network(nodes_dict, edges_dict, time_stamp = time, node_label_field = "id_num",
edge_width = 3, border_color = "black", edge_arrow_to = True, edge_font_size = 15,
edge_font_align= "top", physics_enabled = False, graph_width = 1000, graph_height = 1000)
