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ratspub.py
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#!/bin/env python3
from nltk.tokenize import sent_tokenize
import os
import re
from ratspub_keywords import *
from gene_synonyms import *
import time
global function_d, brain_d, drug_d, addiction_d, brain_query_term, pubmed_path, genes
## turn dictionary (synonyms) to regular expression
def undic(dic):
return "|".join(dic.values())
def findWholeWord(w):
return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
def getabstracts(gene,query):
query="\"(" + query.replace("|", " OR ") + ") AND " + gene + "\""
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()
return(abstracts)
sentences_ls=[]
def getSentences(gene, sentences_ls):
#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()
out=str()
## keep the sentence only if it contains the gene
for sent in sentences_ls:
#if findWholeWord(gene)(sent):
#for sent in sente:
if gene.lower() in sent.lower():
#print(sent)
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)
out+=pmid+"\t"+sent+"\n"
#out+="\t"+sent+"\n"
#print(out)
return(out)
def gene_category(gene, cat_d, query, cat, abstracts):
#e.g. BDNF, addiction_d, undic(addiction_d) "addiction"
q="\"(" + query.replace("|", " OR ") + ") AND " + gene + "\""
sents=getSentences(gene, abstracts)
out=str()
for sent in sents.split("\n"):
for key in cat_d:
if findWholeWord(cat_d[key])(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
out+=gene+"\t"+ cat + "\t"+key+"\t"+sent+"\n"
return(out)
def generate_nodes(nodes_d, nodetype):
# include all search terms even if there are no edges, just to show negative result
json0 =str()
for node in nodes_d:
json0 += "{ data: { id: '" + node + "', nodecolor: '" + nodecolor[nodetype] + "', nodetype: '"+nodetype + "', url:'/shownode?nodetype=" + nodetype + "&node="+node+"' } },\n"
return(json0)
def generate_nodes_json(nodes_d, nodetype):
# include all search terms even if there are no edges, just to show negative result
nodes_json0 =str()
for node in nodes_d:
nodes_json0 += "{ \"id\": \"" + node + "\", \"nodecolor\": \"" + nodecolor[nodetype] + "\", \"nodetype\": \"" + nodetype + "\", \"url\":\"/shownode?nodetype=" + nodetype + "&node="+node+"\" },\n"
return(nodes_json0)
def generate_edges(data, filename):
pmid_list=[]
json0=str()
edgeCnts={}
for line in data.split("\n"):
if len(line.strip())!=0:
(source, cat, target, pmid, sent) = line.split("\t")
edgeID=filename+"|"+source+"|"+target
if (edgeID in edgeCnts) and (pmid+target not in pmid_list):
edgeCnts[edgeID]+=1
pmid_list.append(pmid+target)
elif (edgeID not in edgeCnts) and (pmid+target not in pmid_list):
edgeCnts[edgeID]=1
pmid_list.append(pmid+target)
for edgeID in edgeCnts:
(filename, source,target)=edgeID.split("|")
json0+="{ data: { id: '" + edgeID + "', source: '" + source + "', target: '" + target + "', sentCnt: " + str(edgeCnts[edgeID]) + ", url:'/sentences?edgeID=" + edgeID + "' } },\n"
return(json0)
def generate_edges_json(data, filename):
pmid_list=[]
edges_json0=str()
edgeCnts={}
for line in data.split("\n"):
if len(line.strip())!=0:
(source, cat, target, pmid, sent) = line.split("\t")
edgeID=filename+"|"+source+"|"+target
if (edgeID in edgeCnts) and (pmid+target not in pmid_list):
edgeCnts[edgeID]+=1
pmid_list.append(pmid+target)
elif (edgeID not in edgeCnts) and (pmid+target not in pmid_list):
edgeCnts[edgeID]=1
pmid_list.append(pmid+target)
for edgeID in edgeCnts:
(filename, source,target)=edgeID.split("|")
edges_json0+="{ \"id\": \"" + edgeID + "\", \"source\": \"" + source + "\", \"target\": \"" + target + "\", \"sentCnt\": \"" + str(edgeCnts[edgeID]) + "\", \"url\":\"/sentences?edgeID=" + edgeID + "\" },\n"
return(edges_json0)
def searchArchived(sets, query, filetype):
if sets=='topGene':
dataFile="topGene_addiction_sentences.tab"
nodes= "{ data: { id: '" + query + "', nodecolor: '" + "#2471A3" + "', fontweight:700, url:'/progress?query="+query+"' } },\n"
elif sets=='GWAS':
dataFile="gwas_addiction.tab"
nodes=str()
with open(dataFile, "r") as sents:
pmid_list=[]
cat1_list=[]
catCnt={}
for sent in sents:
(symb, cat0, cat1, pmid, sent)=sent.split("\t")
if (symb.upper() == query.upper()) :
if (cat1 in catCnt.keys()) and (pmid+cat1 not in pmid_list):
pmid_list.append(pmid+cat1)
catCnt[cat1]+=1
elif (cat1 not in catCnt.keys()):
catCnt[cat1]=1
pmid_list.append(pmid+cat1)
nodes= "{ data: { id: '" + query + "', nodecolor: '" + "#2471A3" + "', fontweight:700, url:'/progress?query="+query+"' } },\n"
edges=str()
gwas_json=str()
for key in catCnt.keys():
if sets=='GWAS':
nc=nodecolor["GWAS"]
nodes += "{ data: { id: '" + key + "', nodecolor: '" + nc + "', url:'https://www.ebi.ac.uk/gwas/search?query="+key.replace("_GWAS","")+"' } },\n"
elif key in drug_d.keys():
nc=nodecolor["drug"]
nodes += "{ data: { id: '" + key + "', nodecolor: '" + nc + "', url:'/shownode?node="+key+"' } },\n"
else:
nc=nodecolor["addiction"]
nodes += "{ data: { id: '" + key + "', nodecolor: '" + nc + "', url:'/shownode?node="+key+"' } },\n"
edgeID=dataFile+"|"+query+"|"+key
edges+="{ data: { id: '" + edgeID+ "', source: '" + query + "', target: '" + key + "', sentCnt: " + str(catCnt[key]) + ", url:'/sentences?edgeID=" + edgeID + "' } },\n"
gwas_json+="{ \"id\": \"" + edgeID + "\", \"source\": \"" + query + "\", \"target\": \"" + key + "\", \"sentCnt\": \"" + str(catCnt[key]) + "\", \"url\":\"/sentences?edgeID=" + edgeID + "\" },\n"
if(filetype == 'cys'):
return(nodes+edges)
else:
return(gwas_json)
# brain region has too many short acronyms to just use the undic function, so search PubMed using the following
#brain_query_term="cortex|accumbens|striatum|amygadala|hippocampus|tegmental|mesolimbic|infralimbic|prelimbic|habenula"
#function=undic(function_d)
#addiction=undic(addiction_d)
#drug=undic(drug_d)
gene_s=undic(genes)
nodecolor={'function':"#A9CCE3", 'addiction': "#F5B7B1", 'drug': "#D7BDE2", 'brain':"#D4EFDF", 'GWAS':"#D5DBDB", 'stress':"#FAD7A0", 'psychiatric':"#F9E79F", 'cell':"#ABEBC6"}
#https://htmlcolorcodes.com/color-chart flat design third row
n0=generate_nodes(function_d, 'function')
n1=generate_nodes(addiction_d, 'addiction')
n2=generate_nodes(drug_d, 'drug')
n3=generate_nodes(brain_d, 'brain')
n4=generate_nodes(stress_d, 'stress')
n5=generate_nodes(psychiatric_d, 'psychiatric')
n6=generate_nodes(cell_d, 'cell')
n7=''
nj0=generate_nodes_json(function_d, 'function')
nj1=generate_nodes_json(addiction_d, 'addiction')
nj2=generate_nodes_json(drug_d, 'drug')
nj3=generate_nodes_json(brain_d, 'brain')
nj4=generate_nodes_json(stress_d, 'stress')
nj5=generate_nodes_json(psychiatric_d, 'psychiatric')
nj6=generate_nodes_json(cell_d, 'cell')
nj7=''
pubmed_path=os.environ["EDIRECT_PUBMED_MASTER"]