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authorHao Chen2019-05-19 17:44:30 -0500
committerHao Chen2019-05-19 17:44:30 -0500
commit6e90fb4af7b6163b1687910f416be89901a69731 (patch)
tree992f5415feabfaedd53a00a426a098022f4632fe
parent57dc1ef7a63f8c05e6d4369dbcd8eb0e51f40a64 (diff)
downloadgenecup-6e90fb4af7b6163b1687910f416be89901a69731.tar.gz
recover file
-rwxr-xr-xratspub.py88
1 files changed, 88 insertions, 0 deletions
diff --git a/ratspub.py b/ratspub.py
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+++ b/ratspub.py
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+#!/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"
+