diff options
| -rw-r--r-- | Readme.md | 2 | ||||
| -rw-r--r-- | addiction.onto | 7 | ||||
| -rw-r--r-- | addiction_gwas_ontology.md | 13 | ||||
| -rw-r--r-- | addiction_keywords.py | 10 | ||||
| -rwxr-xr-x | more_functions.py | 30 | ||||
| -rwxr-xr-x | server.py | 142 |
6 files changed, 125 insertions, 79 deletions
@@ -1,6 +1,6 @@ # GeneCup: Mining gene relationships from PubMed using custom ontology -URL: [http://genecup.org](http://genecup.org) +URL: [https://genecup.org](https://genecup.org) GeneCup automatically extracts information from PubMed and NHGRI-EBI GWAS catalog on the relationship of any gene with a custom list of keywords hierarchically organized into an ontology. The users create an ontology by identifying categories of concepts and a list of keywords for each concept. diff --git a/addiction.onto b/addiction.onto index ccfbf4a..c996e23 100644 --- a/addiction.onto +++ b/addiction.onto @@ -1,7 +1,8 @@ -{'addiction': {'addiction': {'addiction|addictive|compulsive|drug abuse|escalation|punishment'}, 'aversion': {'aversion|aversive|conditioned taste aversion|CTA'}, 'dependence': {'dependence'}, 'intoxication': {'binge|intoxication'}, 'relapse': {'craving|drug seeking|reinstatement|relapse|seeking'}, 'reward': {'conditioned place preference|CPP|drug reinforced|hedonic|ICSS|incentive|instrumental response|intracranial self stimulation|operant|reinforcement|reinforcing|reward|self administered|self administration'}, 'sensitization': {'behavioral sensitization|locomotor sensitization|drug sensitization|incentive sensitization'}, 'withdrawal': {'withdrawal'}}, - 'brain': {'accumbens': {'acbc|acbs|accumbal|accumbens|core|Nacc|NacSh|shell'}, 'amygdala': {'amy|amygdala|bla|cea|cna'}, 'cortex': {'cerebral|cingulate|cortex|cortico limbic|corticolimbic|corticostriatal|infralimbic|insular|mPFC|orbitofrontal|pfc|prefrontal|prelimbic|prl|vmpfc'}, 'habenula': {'habenula|lhb|mhb'}, 'hippocampus': {'ca1|ca3|dentate gyrus|dhpc|hip|hipp|hippocampal|hippocampus|subiculum|vhipp|vhpc'}, 'hypothalamus': {'hypothalamic|hypothalamus|LHA|paraventricular nucleus|PVN'}, 'striatum': {'basal ganglia|caudate|globus pallidus|GPI|putamen|STR|striatal|striatum'}, 'VTA': {'limbic|mesoaccumbal|mesoaccumbens|mesolimbic|midbrain|pvta|ventral tegmental|vta'}}, +{'brain': {'accumbens': {'acbc|acbs|accumbal|accumbens|core|Nacc|NacSh|shell'}, 'amygdala': {'amy|amygdala|bla|cea|cna'}, 'cortex': {'cerebral|cingulate|cortex|cortico limbic|corticolimbic|corticostriatal|infralimbic|insular|mPFC|orbitofrontal|pfc|prefrontal|prelimbic|prl|vmpfc'}, 'habenula': {'habenula|lhb|mhb'}, 'hippocampus': {'ca1|ca3|dentate gyrus|dhpc|hip|hipp|hippocampal|hippocampus|subiculum|vhipp|vhpc'}, 'hypothalamus': {'hypothalamic|hypothalamus|LHA|paraventricular nucleus|PVN'}, 'striatum': {'basal ganglia|caudate|globus pallidus|GPI|putamen|STR|striatal|striatum'}, 'VTA': {'limbic|mesoaccumbal|mesoaccumbens|mesolimbic|midbrain|pvta|ventral tegmental|vta'}}, + 'addiction': {'addiction': {'addiction|addictive|compulsive|drug abuse|escalation|punishment'}, 'aversion': {'aversion|aversive|conditioned taste aversion|CTA'}, 'dependence': {'dependence'}, 'intoxication': {'binge|intoxication'}, 'relapse': {'craving|drug seeking|reinstatement|relapse|seeking'}, 'reward': {'conditioned place preference|CPP|drug reinforced|hedonic|ICSS|incentive|instrumental response|intracranial self stimulation|operant|reinforcement|reinforcing|reward|self administered|self administration'}, 'sensitization': {'behavioral sensitization|locomotor sensitization|drug sensitization|incentive sensitization'}, 'withdrawal': {'withdrawal'}}, 'drug': {'alcohol': {'acamprosate|alcohol|alcoholics|alcoholism|antabuse|campral|disulfiram|ethanol|naltrexone|revia|vivitrol'}, 'amphetamine': {'AMPH|amphetamine|METH|methamphetamine'}, 'benzodiazepine': {'adinazolam|alprazolam|benzodiazepine|benzos|brotizolam|chlordiazepoxide|climazolam|clobazam|clonazepam|clorazepate|diazepam|estazolam|flunitrazepam|flurazepam|halazepam|librium|loprazolam|lorazepam|lormetazepam|midazolam|nimetazepam|nitrazepam|normison|oxazepam|prazepam|temazepam|triazolam|valium|xanax'}, 'cannabinoid': {'acylethanolamines|cannabichromene|cannabidiol|cannabigerol|cannabinoids|cannabinol|cannabis|cannabivarin|cesamet|drobinal|dronabinol|endocannabinoids|epidiolex|JWH 018|JWH 122|JWH 250|marijuana|marinol|nabilone|Oleoylethanolamide|palmitoylethanolamide|phytocannabinoid|rimonabant|SR141716|SR144528|syndros|tetrahydrocannabinol|tetrahydrocannabivarin|thc|thc 9'}, 'cocaine': {'cocaine'}, 'nicotine': {'nicotine|smokers|smoking|tobacco'}, 'opioid': {'buprenorphine|codeine|fentanyl|heroin|hycodan|hydrocodone|hydromorphone|kadian|kratom|methadone|morphine|naloxone|opioids|oxycodone|oxycontin|percocet|suboxone|tramadol|ultram|vicodin'}, 'psychedelics': {'ayahuasca|ecstasy|ibogaine|ketamine|LSD|lysergic acid diethylamide|MDMA|mescaline|methylenedioxymethamphetamine|N methoxybenzyl|NBOMes|peyote|psilocybin|psychedelic|psychedelics'}}, 'function': {'neuroplasticity': {'boutons|epsc|epsp|IPSC|IPSP|long term depression|long term potentiation|LTD|LTP|mIPSC|neurite|neurogenesis|neuroplasticity|plasticity|synaptic'}, 'neurotransmission': {'5 ht|acetylcholine|cholinergic|DAergic|dopamine|dopaminergic|GABA|GABAergic|glutamate|glutamatergic|muscarinic|neuropeptides|neurotransmission|nicotinic|serotonergic|serotonin'}, 'signalling': {'glycosylation|phosphorylation|signaling|signalling|kinase|binding|signal transduction|second messengers|cGMP|cAMP'}, 'transcription': {'histone|hypermethylation|hypomethylation|methylation|ribosome|transcription'}}, 'psychiatric': {'anxiety': {'anxiety|anxious'}, 'autism': {'autism|autistic'}, 'bipolar': {'bipolar disorder'}, 'compulsive': {'compulsive|obsessive'}, 'depression': {'depression|depressive|major depressive disorder|MDD'}, 'impulsivity': {'5 CSRTT|5 choice task|delay discounting|delay exposure|delay intolerance|delayed reward|delay task|five choice serial reaction time task|impulsive|impulsivity|premature responding'}, 'schizophrenia': {'schizophrenia'}}, 'cell': {'neuron': {'adrenergic neurons|cholinergic neurons|dopaminergic neurons|gabaergic neurons|glutamatergic neurons|GnRH neurons|interneurons|monoaminergic neurons|medium spiny neurons|motor neurons|neuronal cells|nitrergic neurons|noradrenergic neurons|projection neurons|pyramidal neurons|sensory neurons|serotonergic neurons|somatostatin neurons|neurons|excitatory neurons|inhibitory neurons|corticospinal neurons|dopamine neurons|D1 neurons|D2 neurons|afferent neurons|efferent neurons|serotonin neurons|cortical neurons|hippocampal neurons|DA neurons|CNS neurons|cortex neurons|mesencephalic neurons|orexin neurons|catecholaminergic neurons|striatal neurons|bipolar neurons|ganglion cells|RGC|horizontal cells|amacrine cells'}, 'astrocyte': {'astrocytic|astrocytes|astroglia|astroglial'}, 'microglia': {'microglia|microglial'}, 'endothelium': {'endothelium|endothelial cells'}, 'oligodendrocyte': {'oligodendrocytes'}}, - 'stress': {'PTSD': {'PTSD|post traumatic stress|post traumatic stress symptoms|post traumatic stress disorder'}, 'stress': {'distress|psychological trauma|stress'}}}
\ No newline at end of file + 'stress': {'PTSD': {'PTSD|post traumatic stress|post traumatic stress symptoms|post traumatic stress disorder'}, 'stress': {'distress|psychological trauma|stress'}}, + 'GWAS': {'psychiatric': {'psychiatric|schizophrenia|autism|depression|anxiety|bipolar|mental'},'nicotine': {'nicotine|smoking|chronic obstructive|tobacco'}, 'addiction': {'addiction|cocaine|opioid|morphine|amphetamine|methadone|heroin|drug dependence'}, 'alcohol': {'alcohol'}}} diff --git a/addiction_gwas_ontology.md b/addiction_gwas_ontology.md new file mode 100644 index 0000000..b02b7e6 --- /dev/null +++ b/addiction_gwas_ontology.md @@ -0,0 +1,13 @@ + + +GWAS + psychiatric + schizophrenia; autism; depression; anxiety; bipolar; mental; + nicotine + smoking; chronic obstructive; tobacco + addiction + cocaine; opioid; morphine; amphetamine; methadone; heroin; drug dependence; + alcohol + + + diff --git a/addiction_keywords.py b/addiction_keywords.py index 323fd16..0e813fc 100644 --- a/addiction_keywords.py +++ b/addiction_keywords.py @@ -47,3 +47,13 @@ cell_d={'neuron':'adrenergic neurons*|cholinergic neurons*|dopaminergic neurons* stress_d={'PTSD':'PTSD|post traumatic stress|post traumatic stress symptoms*|post traumatic stress disorder', 'stress':'distress|psychological trauma|stress'} +GWAS_d={'psychiatric':'psychiatric|schizophrenia|autism|depression|anxiety|bipolar|mental', +'nicotine':'nicotine|smoking|chronic obstructive|tobacco', +'addiction':'addiction|cocaine|opioid|morphine|amphetamine|methadone|heroin|drug dependence', +'alcohol':'alcohol'} + + + + + + diff --git a/more_functions.py b/more_functions.py index 234330b..cb070d9 100755 --- a/more_functions.py +++ b/more_functions.py @@ -29,18 +29,25 @@ def getabstracts(gene,query): query2 = query+"s*" query3 = query2.replace("s|", "s* OR ") query4 = query3.replace("|", "s* OR ") - query="\"(" + query4 + ") AND " + gene + "\"" + + #query4=query + #query="\"(" + query4 + ") AND ((" + gene + "[tiab]) or (" + gene + "[meSH]))\"" + query="\"(" + query4 + ") AND (" + gene + " [tiab])\"" + #query = "neurons* AND (penk [tiab])" abstracts = os.popen("esearch -db pubmed -query " + query \ + " | efetch -format uid |fetch-pubmed -path "+ pubmed_path \ + " | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText|sed \"s/-/ /g\"").read() + #print(abstracts) return(abstracts) sentences_ls=[] def getSentences(gene, sentences_ls): out=str() # Keep the sentence only if it contains the gene + #print(sentences_ls) for sent in sentences_ls: - if gene.lower() in sent.lower(): + #if gene.lower() in sent.lower(): + if re.search(r'\b'+gene.lower()+r'\b',sent.lower()): pmid = sent.split(' ')[0] sent = sent.split(' ',1)[1] sent=re.sub(r'\b(%s)\b' % gene, r'<strong>\1</strong>', sent, flags=re.I) @@ -50,18 +57,27 @@ def getSentences(gene, sentences_ls): def gene_category(gene, cat_d, cat, abstracts,addiction_flag,dictn): # e.g. BDNF, addiction_d, undic(addiction_d) "addiction" sents=getSentences(gene, abstracts) + #print(abstracts) out=str() if (addiction_flag==1): for sent in sents.split("\n"): for key in cat_d: - if findWholeWord(cat_d[key])(sent) : + if key =='s': + key_ad = key+"*" + else: + key_ad = key+"s*" + if findWholeWord(key_ad)(sent) : sent=sent.replace("<b>","").replace("</b>","") # remove other highlights - sent=re.sub(r'\b(%s)\b' % cat_d[key], r'<b>\1</b>', sent, flags=re.I) # highlight keyword + sent=re.sub(r'\b(%s)\b' % key_ad, r'<b>\1</b>', sent, flags=re.I) # highlight keyword out+=gene+"\t"+ cat + "\t"+key+"\t"+sent+"\n" else: - for sent in sents.split("\n"): - for key_1 in dictn[cat_d].keys(): - for key_2 in dictn[cat_d][key_1]: + for key_1 in dictn[cat_d].keys(): + for key_2 in dictn[cat_d][key_1]: + if key_2[-1] =='s': + key_2 = key_2+"*" + else: + key_2 = key_2+"s*" + for sent in sents.split("\n"): if findWholeWord(key_2)(sent) : sent=sent.replace("<b>","").replace("</b>","") # remove other highlights sent=re.sub(r'\b(%s)\b' % key_2, r'<b>\1</b>', sent, flags=re.I) # highlight keyword @@ -140,10 +140,13 @@ def login(): session['name'] = found_user.name session['id'] = found_user.id flash("Login Succesful!") + ontoarchive() + onto_len_dir = session['onto_len_dir'] + onto_list = session['onto_list'] else: flash("Invalid username or password!", "inval") return render_template('signup.html') - + print(onto_list) return render_template('index.html',onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto) @@ -169,13 +172,15 @@ def signup(): session['name'] = name password = bcrypt.hashpw(password.encode('utf8'), bcrypt.gensalt()) user = users(name=name, email=email, password = password) - if found_user: session['email'] = found_user.email session['hashed_email'] = hashlib.md5(session['email'] .encode('utf-8')).hexdigest() session['id'] = found_user.id found_user.name = name db.session.commit() + ontoarchive() + onto_len_dir = session['onto_len_dir'] + onto_list = session['onto_list'] else: db.session.add(user) db.session.commit() @@ -204,13 +209,16 @@ def signin(): if (found_user and (bcrypt.checkpw(password.encode('utf8'), found_user.password))): session['email'] = found_user.email - session['hashed_email'] = hashlib.md5(session['email'] .encode('utf-8')).hexdigest() + session['hashed_email'] = hashlib.md5(session['email'].encode('utf-8')).hexdigest() session['name'] = found_user.name session['id'] = found_user.id flash("Login Succesful!") - onto_len_dir = 0 - onto_list = '' + #onto_len_dir = 0 + #onto_list = '' onto_cont=open("addiction.onto","r").read() + ontoarchive() + onto_len_dir = session['onto_len_dir'] + onto_list = session['onto_list'] dict_onto=ast.literal_eval(onto_cont) return render_template('index.html', onto_len_dir=onto_len_dir, onto_list=onto_list, ontol = 'addiction', dict_onto = dict_onto) else: @@ -741,7 +749,6 @@ def progress(): for gen in genes: genes_session += str(gen) + "_" - genes_session = genes_session[:-1] session['query']=genes return render_template('progress.html', url_in="search", url_out="cytoscape/?rnd="+rnd+"&genequery="+genes_session) @@ -750,12 +757,12 @@ def progress(): @app.route("/search") def search(): genes=session['query'] - percent_ratio=len(genes) + percent_ratio=len(genes)+1 if(len(genes)==1): percent_ratio=2 timeextension=session['timeextension'] - percent=round(100/percent_ratio*8,1) # 7 categories + 1 at the beginning + percent=round(100/percent_ratio,1)-1 # 7 categories + 1 at the beginning if ('email' in session): sessionpath = session['path_user'] + timeextension @@ -777,7 +784,6 @@ def search(): nodecolor={} nodecolor['GWAS'] = "hsl(0, 0%, 70%)" nodes_list = [] - nodes_list_for_gwas = [] if 'namecat' in session: namecat_flag=1 @@ -786,8 +792,6 @@ def search(): dict_onto=ast.literal_eval(onto_cont) for ky in dict_onto.keys(): - for nd_g in dict_onto[ky]: - nodes_list_for_gwas.append(nd_g) nodecolor[ky] = "hsl("+str((n_num+1)*int(360/len(dict_onto.keys())))+", 70%, 80%)" d["nj{0}".format(n_num)]=generate_nodes_json(dict_onto[ky],str(ky),nodecolor[ky]) n_num+=1 @@ -802,8 +806,6 @@ def search(): else: namecat_flag=0 for ky in dictionary.keys(): - for nd_g in dictionary[ky]: - nodes_list_for_gwas.append(nd_g) nodecolor[ky] = "hsl("+str((n_num+1)*int(360/len(dictionary.keys())))+", 70%, 80%)" d["nj{0}".format(n_num)]=generate_nodes_json(dictionary[ky],str(ky),nodecolor[ky]) n_num+=1 @@ -826,9 +828,8 @@ def search(): progress=0 searchCnt=0 nodesToHide=str() - json_edges = str() - progress+=percent - genes_or = ' or '.join(genes) + json_edges = str() + #genes_or = ' [tiab] or '.join(genes) all_d='' if namecat_flag==1: @@ -837,7 +838,6 @@ def search(): for ky in dict_onto.keys(): if (ky in search_type): - ls_plural = list(dict_onto[ky].values()) all_d_ls=undic(list(dict_onto[ky].values())) all_d = all_d+'|'+all_d_ls else: @@ -846,23 +846,24 @@ def search(): all_d_ls=undic(list(dictionary[ky].values())) all_d = all_d+'|'+all_d_ls all_d=all_d[1:] - abstracts_raw = getabstracts(genes_or,all_d) + if ("GWAS" in search_type): + datf = pd.read_csv('./utility/gwas_used.csv',sep='\t') progress+=percent - sentences_ls=[] - - for row in abstracts_raw.split("\n"): - tiab=row.split("\t") - pmid = tiab.pop(0) - tiab= " ".join(tiab) - sentences_tok = sent_tokenize(tiab) - for sent_tok in sentences_tok: - sent_tok = pmid + ' ' + sent_tok - sentences_ls.append(sent_tok) + yield "data:"+str(progress)+"\n\n" for gene in genes: + abstracts_raw = getabstracts(gene,all_d) + sentences_ls=[] + + for row in abstracts_raw.split("\n"): + tiab=row.split("\t") + pmid = tiab.pop(0) + tiab= " ".join(tiab) + sentences_tok = sent_tokenize(tiab) + for sent_tok in sentences_tok: + sent_tok = pmid + ' ' + sent_tok + sentences_ls.append(sent_tok) gene=gene.replace("-"," ") - # report progress immediately - progress+=percent - yield "data:"+str(progress)+"\n\n" + geneEdges = "" if namecat_flag==1: @@ -872,53 +873,58 @@ def search(): dict_onto = dictionary for ky in dict_onto.keys(): - if (ky=='addiction') and ('addiction' in dict_onto.keys())\ - and ('drug' in dict_onto.keys()) and ('addiction' in dict_onto['addiction'].keys())\ - and ('aversion' in dict_onto['addiction'].keys()) and ('intoxication' in dict_onto['addiction'].keys()): - #addiction terms must present with at least one drug - addiction_flag=1 - #addiction=undic0(addiction_d) +") AND ("+undic0(drug_d) - sent=gene_category(gene, addiction_d, "addiction", sentences_ls,addiction_flag,dict_onto) - if ('addiction' in search_type): - geneEdges += generate_edges(sent, tf_name) - json_edges += generate_edges_json(sent, tf_name) - else: - addiction_flag=0 - if namecat_flag==1: - onto_cont = open(ses_namecat+".onto","r").read() - dict_onto=ast.literal_eval(onto_cont) - #ky_d=undic(list(dict_onto[ky].values())) - sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto) - + if (ky in search_type): + if (ky=='addiction') and ('addiction' in dict_onto.keys())\ + and ('drug' in dict_onto.keys()) and ('addiction' in dict_onto['addiction'].keys())\ + and ('aversion' in dict_onto['addiction'].keys()) and ('intoxication' in dict_onto['addiction'].keys()): + #addiction terms must present with at least one drug + addiction_flag=1 + #addiction=undic0(addiction_d) +") AND ("+undic0(drug_d) + sent=gene_category(gene, addiction_d, "addiction", sentences_ls,addiction_flag,dict_onto) + if ('addiction' in search_type): + geneEdges += generate_edges(sent, tf_name) + json_edges += generate_edges_json(sent, tf_name) else: - ky_d=undic(list(dict_onto[ky].values())) - sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto) - progress+=percent - yield "data:"+str(progress)+"\n\n" - if (ky in search_type): + addiction_flag=0 + if namecat_flag==1: + onto_cont = open(ses_namecat+".onto","r").read() + dict_onto=ast.literal_eval(onto_cont) + #ky_d=undic(list(dict_onto[ky].values())) + sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto) + + else: + #ky_d=undic(list(dict_onto[ky].values())) + sent=gene_category(gene,ky,str(ky), sentences_ls, addiction_flag,dict_onto) + yield "data:"+str(progress)+"\n\n" + geneEdges += generate_edges(sent, tf_name) json_edges += generate_edges_json(sent, tf_name) - sentences+=sent + sentences+=sent if ("GWAS" in search_type): gwas_sent=[] - for nd in nodes_list_for_gwas: - gwas_text='' - datf = pd.read_csv('./utility/gwas_used.csv',sep='\t') - datf_sub = datf[datf['DISEASE/TRAIT'].str.contains(nd,regex=False, case=False, na=False) - & (datf['REPORTED GENE(S)'].str.contains(gene,regex=False, case=False, na=False) - | (datf['MAPPED_GENE'].str.contains(gene,regex=False, case=False, na=False)))] - - if not datf_sub.empty: - for index, row in datf_sub.iterrows(): - gwas_text = "SNP:<b>"+str(row['SNPS'])+"</b>, P value: <b>"+str(row['P-VALUE'])\ - +"</b>, Disease/trait:<b> "+str(row['DISEASE/TRAIT'])+"</b>, Mapped trait:<b> "\ - +str(row['MAPPED_TRAIT'])+"</b><br>" - gwas_sent.append(gene+"\t"+"GWAS"+"\t"+nd+"_GWAS\t"+str(row['PUBMEDID'])+"\t"+gwas_text) + datf_sub1 = datf[datf['REPORTED GENE(S)'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE) + | (datf['MAPPED_GENE'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE))] + for nd2 in dict_onto['GWAS'].keys(): + for nd1 in dict_onto['GWAS'][nd2]: + for nd in nd1.split('|'): + gwas_text='' + datf_sub = datf_sub1[datf_sub1['DISEASE/TRAIT'].str.contains('(?:\s|^)'+nd+'(?:\s|$)', flags=re.IGNORECASE)] + #& (datf['REPORTED GENE(S)'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE) + #| (datf['MAPPED_GENE'].str.contains('(?:\s|^)'+gene+'(?:\s|$)', flags=re.IGNORECASE)))] + if not datf_sub.empty: + for index, row in datf_sub.iterrows(): + gwas_text = "SNP:<b>"+str(row['SNPS'])+"</b>, P value: <b>"+str(row['P-VALUE'])\ + +"</b>, Disease/trait:<b> "+str(row['DISEASE/TRAIT'])+"</b>, Mapped trait:<b> "\ + +str(row['MAPPED_TRAIT'])+"</b><br>" + gwas_sent.append(gene+"\t"+"GWAS"+"\t"+nd+"_GWAS\t"+str(row['PUBMEDID'])+"\t"+gwas_text) cys, gwas_json, sn_file = searchArchived('GWAS', gene , 'json',gwas_sent, path_user) with open(path_user+"gwas_results.tab", "w") as gwas_edges: gwas_edges.write(sn_file) geneEdges += cys json_edges += gwas_json + # report progress immediately + progress+=percent + yield "data:"+str(progress)+"\n\n" if len(geneEdges) >0: edges+=geneEdges |