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| -rwxr-xr-x | ratspub.py | 88 |
1 files changed, 88 insertions, 0 deletions
diff --git a/ratspub.py b/ratspub.py new file mode 100755 index 0000000..0cc5d8a --- /dev/null +++ b/ratspub.py @@ -0,0 +1,88 @@ +#!/bin/env python3 +from nltk.tokenize import sent_tokenize +import os +import re +from ratspub_keywords import * + +global function_d, brain_d, drug_d, addiction_d, brain_query_term, pubmed_path + + +## 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 "+ pubmed_path + " | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText|sed \"s/-/ /g\"").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'<strong>\1</strong>', sent, flags=re.I) + out+=pmid+"\t"+sent+"\n" + return(out) + +def gene_category(gene, cat_d, query, cat): + #e.g. BDNF, addiction_d, undic(addiction_d) "addiction" + q="\"(" + query.replace("|", " OR ") + ") AND " + gene + "\"" + sents=getSentences(q, gene) + 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_edges(data, filename): + 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: + edgeCnts[edgeID]+=1 + else: + edgeCnts[edgeID]=1 + 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) + +# 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) + +nodecolor={'function':"#A9CCE3", 'addiction': "#D7BDE2", 'drug': "#F9E79F", 'brain':"#A3E4D7"} +#https://htmlcolorcodes.com/ +n0=generate_nodes(function_d, 'function') +n1=generate_nodes(addiction_d, 'addiction') +n2=generate_nodes(drug_d, 'drug') +n3=generate_nodes(brain_d, 'brain') +default_nodes=n0+n1+n2+n3 + + +host= os.popen('hostname').read().strip() +if host=="x1": + pubmed_path="/run/media/hao/PubMed/Archive/" +elif host=="hchen3": + pubmed_path="/media/hao/2d554499-6c5b-462d-85f3-5c49b25f4ac8/PubMed/Archive" + |