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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.keys())+"|"+"|".join(dic.values())
def findWholeWord(w):
return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
def getSentences(query):
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 (" + drugs.replace("|", " OR ", ) + ") AND " + gene + "\""
sents=getSentences(q)
out=str()
for sent in sents.split("\n"):
for drug0 in drugs_d:
if findWholeWord(drugs_d[drug0])(sent) :
sent=re.sub(r'\b(%s)\b' % drugs_d[drug0], r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"drug\t" + drug0+"\t"+sent+"\n"
for add0 in addiction.split("|"):
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)
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_biological(gene):
q="\"(" + biological.replace("|", " OR ") + ") AND " + gene + "\""
sents=getSentences(q)
out=str()
for sent in sents.split("\n"):
for bio0 in biological_d:
if findWholeWord(biological_d[bio0])(sent) :
sent=re.sub(r'\b(%s)\b' % biological_d[bio0], r'<b>\1</b>', sent, flags=re.I)
out+=gene+"\t"+"function\t"+bio0+"\t"+sent+"\n"
return(out)
addiction="reward|reinforcement|sensitization|intake|addiction|drug abuse|relapse|self-administered|self-administration|reinstatement|binge|intoxication|withdrawal|conditioned place preference|aversion|aversive|CPP"
drugs_d = {"alcohol":"alcohol|alcoholism",
"nicotine":"smoking|nicotine|tobacco",
"amphetamine":"methamphetamine|amphetamine",
"cocaine":"cocaine",
"opioid":"opioid|fentanyl|oxycodone|oxycontin|heroin|morphine",
"cannabinoid":"marijuana|cannabinoid|Tetrahydrocannabinol|thc"
}
drugs=undic(drugs_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"
biological_d={"plasticity":"LTP|LTD|plasticity|synaptic|epsp|epsc",
"neurotransmission": "neurotransmission|glutamate|GABA|cholinergic|serotoninergic",
"signalling":"signalling|phosphorylation|glycosylation",
# "regulation":"increased|decreased|regulated|inhibited|stimulated",
"transcription":"transcription|methylation|histone|ribosome",
}
biological=undic(biological_d)
report=str()
out0=gene_addiction(gene)
report+=out0
out1=gene_anatomical(gene)
report+=out1
out2=gene_biological(gene)
report+=out2
with codecs.open(gene+"_addiction_sentences.tab", "w", encoding='utf8') as writer:
writer.write(report)
writer.close()
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