find top genes

4 files changed, 117 insertions, 1 deletions

diff --git a/server.py b/server.py
index d9e3551..0a97413 100755
--- a/server.py
+++ b/server.py
@@ -127,7 +127,7 @@ def sentences():
 @app.route("/showTopGene")
 def showTopGene():
     topGene=request.args.get('topGene')
-    topGeneSentFile="gene_addiction_sentences_cnt.tab"
+    topGeneSentFile="gene_addiction_sentences.tab"
     with open(topGeneSentFile, "r") as sents:
         catCnt={}
         for sent in sents:
diff --git a/topGene_step1_cnt_abstracts.py b/topGene_step1_cnt_abstracts.py
index 780c314..a9dd23f 100755
--- a/topGene_step1_cnt_abstracts.py
+++ b/topGene_step1_cnt_abstracts.py
@@ -39,6 +39,7 @@ if len(sys.argv)==2:
     input_f=sys.argv[1]
 else:
     input_f="./ncbi_gene_symb_syno_name_txid9606.txt"
+    input_f="./ncbi_gene_symb_syno_name_txid9606_p2.txt"
 
 addiction=undic(addiction_d)
 drug=undic(drug_d)
@@ -72,6 +73,8 @@ with open (input_f, "r") as f:
         # tab is added if there are abstracts counts
         if "\t" in line:
             (gene, count)=line.split("\t")
+            if int(count)<100:
+               rerun=1
         else:
             gene=line.strip()
         # remove synonyms with only two letters
@@ -93,3 +96,7 @@ with open (input_f, "r") as f:
         if (int(count)>0):
             out.write(gene+"\t"+count)
 
+sorted_f=out_f.replace(".txt","_sorted.txt")
+os.system("sort -k2 -t$'\t' -rn " + out_f + " > " + sorted_f )
+
+
diff --git a/topGene_step2_cnt_sentences.py b/topGene_step2_cnt_sentences.py
new file mode 100755
index 0000000..fe97cdd
--- /dev/null
+++ b/topGene_step2_cnt_sentences.py
@@ -0,0 +1,59 @@
+#!/bin/env python3 
+import os
+import re
+import time
+from nltk.tokenize import sent_tokenize
+from ratspub_keywords import *
+
+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, genes):
+    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|sed \"s/-/ /g\"").read()
+    gene_syno=genes.split("|")
+    symb=gene_syno[0]
+    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:
+            for gene in gene_syno:
+                if findWholeWord(gene)(sent):
+                    sent=re.sub(r'\b(%s)\b' % gene, r'<strong>\1</strong>', sent, flags=re.I)
+                    for drug0 in drug_d:
+                        if findWholeWord(drug_d[drug0])(sent) :
+                            sent=sent.replace("<b>","").replace("</b>","")
+                            sent=re.sub(r'\b(%s)\b' % drug_d[drug0], r'<b>\1</b>', sent, flags=re.I)
+                            out+=symb+"\t"+"drug\t" + drug0+"\t"+pmid+"\t"+sent+"\n"
+                    for add0 in addiction_d:
+                        if findWholeWord(addiction_d[add0])(sent) :
+                            sent=sent.replace("<b>","").replace("</b>","")
+                            sent=re.sub(r'\b(%s)\b' % addiction_d[add0], r'<b>\1</b>', sent, flags=re.I)
+                            out+=symb+"\t"+"addiction\t"+add0+"\t"+pmid+"\t"+sent+"\n"
+    return(out)
+
+addiction=undic(addiction_d)
+drug=undic(drug_d)
+
+
+out=open("gene_addiction_sentences.tab", "w+")
+cnt=0
+with open ("./ncbi_gene_symb_syno_name_txid9606_absCnt_sorted_absCnt_sorted_absCnt_sorted.txt", "r") as f:
+    for line in f:
+        (genes, abstractCount)=line.strip().split("\t")
+        if int(abstractCount)>20:
+            symb=genes.split("|")[0]
+            print(symb+"-->"+genes)
+            q="\'(\"" + addiction.replace("|", "\"[tiab] OR \"")  + "\") AND (\"" + drug.replace("|", "\"[tiab] OR \"", ) + "\") AND (\"" + genes.replace("|", "\"[tiab] OR \"", ) + "\")\'"
+            sentences=getSentences(q,genes)
+            out.write(sentences)
+out.close()
+
+os.system("cut -f 1,4 gene_addiction_sentences.tab |uniq  |cut -f 1 |uniq -c |sort -rn > topGeneAbstractCount.tab")
+
diff --git a/topGene_step3_generate_html.py b/topGene_step3_generate_html.py
new file mode 100755
index 0000000..dfcd6fe
--- /dev/null
+++ b/topGene_step3_generate_html.py
@@ -0,0 +1,50 @@
+import re
+
+## generate the html page for the top genes
+
+## put gene names and alias in a dictionary
+geneNames={}
+with open ("./ncbi_gene_symb_syno_name_txid9606_absCnt_sorted_absCnt_sorted.txt","r") as f:
+    for line in f:
+        (genes, count)=line.strip().split("\t")
+        gene=genes.split("|")
+        geneNames[gene[0]]=genes.strip()
+
+out=str()
+html=str()
+with open("./topGeneAbstractCount.tab" ,"r") as gc:
+    cnt=0
+    for  line in gc:
+        cnt+=1
+        line=re.sub(r'^\s+','',line)
+        print (line)
+        pmid_cnt, symb=line.strip().split()
+        out+= symb+"\t"+geneNames[symb]+"\n"
+        html+="<li><a href=\"/showTopGene?topGene="+symb+"\">"+symb+"</a><br>\n"
+        if cnt==500:
+            break
+
+with open("topGene_symb_alias.txt", "w+")  as tg:
+    tg.write(out)
+    tg.close()
+
+
+htmlout='''
+{% extends "layout.html" %}
+{% block content %}
+
+<h3> Top addiction related genes </h3>
+
+<br>
+These genes are ranked by the number of PubMed abstracts that contain the name of the gene and one or more addiction related keyword.
+<hr>
+
+<ol>''' + html  + '''
+</ol>
+{% endblock %}
+'''
+
+with open("./templates/topAddictionGene.html", "w+")  as html_f:
+    html_f.write(htmlout)
+    html_f.close()
+