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#!/bin/env python3
from nltk.tokenize import sent_tokenize
import os
import re
import codecs
import sys
#gene=sys.argv[1]
## 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 getSentences(query, gene):
abstracts = os.popen("esearch -db pubmed -query " + query + " | efetch -format uid |fetch-pubmed -path /run/media/hao/PubMed/Archive/ | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText").read()
out=str()
for row in abstracts.split("\n"):
tiab=row.split("\t")
pmid = tiab.pop(0)
tiab= " ".join(tiab)
sentences = sent_tokenize(tiab)
## keep the sentence only if it contains the gene
for sent in sentences:
if findWholeWord(gene)(sent):
sent=re.sub(r'\b(%s)\b' % gene, r'<b>\1</b>', sent, flags=re.I)
out+=pmid+"\t"+sent+"\n"
return(out)
def gene_addiction(gene):
# search gene name & drug name in the context of addiction terms (i.e., exclude etoh affects cancer, or methods to extract cocaine)
q="\"(" + addiction.replace("|", " OR ") + ") AND (" + drug.replace("|", " OR ", ) + ") AND " + gene + "\""
sents=getSentences(q, gene)
out=str()
for sent in sents.split("\n"):
for drug0 in drug_d:
if findWholeWord(drug_d[drug0])(sent) :
sent=re.sub(r'\b(%s)\b' % drug_d[drug0], r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"drug\t" + drug0+"\t"+sent+"\n"
for add0 in addiction_d:
if findWholeWord(add0)(sent) :
sent=re.sub(r'\b(%s)\b' % add0, r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"addiction\t"+add0+"\t"+sent+"\n"
return(out)
def gene_anatomical(gene):
q="\"(" + brain.replace("|", " OR ") + ") AND " + gene + "\""
sents=getSentences(q,gene)
out=str()
for sent in sents.split("\n"):
for brain0 in brain_d:
if findWholeWord(brain_d[brain0])(sent) :
sent=re.sub(r'\b(%s)\b' % brain_d[brain0], r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"brain\t"+brain0+"\t"+sent+"\n"
return(out)
def gene_functional(gene):
q="\"(" + function.replace("|", " OR ") + ") AND " + gene + "\""
sents=getSentences(q,gene)
out=str()
for sent in sents.split("\n"):
for bio0 in function_d:
if findWholeWord(function_d[bio0])(sent) :
sent=re.sub(r'\b(%s)\b' % function_d[bio0], r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"function\t"+bio0+"\t"+sent+"\n"
return(out)
def generate_nodes(nodes_d, nodecolor):
# include all search terms even if there are no edges, just to show negative result
json0 =str() #"{ data: { id: '" + gene + "'} },\n"
for node in nodes_d:
json0 += "{ data: { id: '" + node + "', nodecolor: '" + nodecolor + "' } },\n"
return(json0)
def generate_edges(data):
json0=str()
edgeCnts={}
for line in data.split("\n"):
if len(line.strip())!=0:
(source, cat, target, pmid, sent) = line.split("\t")
edgeID=source+"|"+target
if edgeID in edgeCnts:
edgeCnts[edgeID]+=1
else:
edgeCnts[edgeID]=1
for edgeID in edgeCnts:
(source,target)=edgeID.split("|")
json0+="{ data: { id: \'" + edgeID + "\', source: \'" + source + "\', target: '" + target + "\', sentCnt: '" + str(edgeCnts[edgeID]) + "', url:'/sentences?edgeID=" + edgeID + "' } },\n"
return(json0)
addiction_d = {"reward":"reward|reinforcement|conditioned place preference|CPP|self-administration|self-administered",
"aversion":"aversion|aversive|CTA|withdrawal",
"relapse":"relapse|reinstatement|craving|drug seeking",
"sensitization":"sensitization",
"addiction":"addiction|drug abuse",
"intoxication":"intoxication|binge"
}
addiction=undic(addiction_d)
drug_d = {"alcohol":"alcohol|alcoholism",
"nicotine":"smoking|nicotine|tobacco",
"cocaine":"cocaine",
"opioid":"opioid|fentanyl|oxycodone|oxycontin|heroin|morphine",
"amphetamine":"methamphetamine|amphetamine|METH",
"cannabinoid":"marijuana|cannabinoid|tetrahydrocannabinol|thc|thc-9"
}
drug=undic(drug_d)
brain_d ={"cortex":"cortex|pfc|vmpfc|il|pl|prelimbic|infralimbic",
"striatum":"striatum|STR",
"accumbens":"shell|core|NAcc|acbs|acbc",
"hippocampus":"hippocampus|hipp|hip|ca1|ca3|dentate|gyrus",
"amygadala":"amygadala|cea|bla|amy",
"vta":"ventral tegmental|vta|pvta"
}
# brain region has too many short acronyms to just use the undic function, so search PubMed using the following
brain="cortex|accumbens|striatum|amygadala|hippocampus|tegmental|mesolimbic|infralimbic|prelimbic"
function_d={"plasticity":"LTP|LTD|plasticity|synaptic|epsp|epsc",
"signalling":"signalling|phosphorylation|glycosylation",
# "regulation":"increased|decreased|regulated|inhibited|stimulated",
"transcription":"transcription|methylation|histone|ribosome",
"neurotransmission": "neurotransmission|glutamate|GABA|cholinergic|serotoninergic",
}
function=undic(function_d)
#https://htmlcolorcodes.com/
n0=generate_nodes(function_d, "#D7BDE2")
n1=generate_nodes(addiction_d,"#A9CCE3")
n2=generate_nodes(drug_d, "#A3E4D7")
n3=generate_nodes(brain_d, "#F9E79F")
default_nodes=n0+n1+n2+n3
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