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Diffstat (limited to 'ratspub.py')
| -rwxr-xr-x | ratspub.py | 136 |
1 files changed, 136 insertions, 0 deletions
diff --git a/ratspub.py b/ratspub.py new file mode 100755 index 0000000..c853fd2 --- /dev/null +++ b/ratspub.py @@ -0,0 +1,136 @@ +#!/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+"<br>\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]) + "' } },\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", + "amphetamine":"methamphetamine|amphetamine|METH", + "cocaine":"cocaine", + "opioid":"opioid|fentanyl|oxycodone|oxycontin|heroin|morphine", + "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", + "neurotransmission": "neurotransmission|glutamate|GABA|cholinergic|serotoninergic", + "signalling":"signalling|phosphorylation|glycosylation", +# "regulation":"increased|decreased|regulated|inhibited|stimulated", + "transcription":"transcription|methylation|histone|ribosome", + } +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 |