#!/bin/env python3 from flask import Flask, render_template, request, session, Response, redirect, url_for, flash from flask_sqlalchemy import SQLAlchemy import json import shutil from flask import jsonify from datetime import datetime import bcrypt import tempfile import random import string from ratspub import * import time import os import re import pytz from os import listdir import nltk from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer from collections import Counter import numpy as np from numpy import array import tensorflow import keras from keras.models import Model from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.layers import * from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers import Embedding from keras import metrics from keras import optimizers import pickle app=Flask(__name__) datadir="/export/ratspub/" app.config['SECRET_KEY'] = '#DtfrL98G5t1dC*4' app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///'+datadir+'userspub.sqlite' db = SQLAlchemy(app) nltk.data.path.append("./nlp/") # the sqlite database class users(db.Model): __tablename__='user' id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(80), nullable=False) email = db.Column(db.String(80), unique=True, nullable=False) password = db.Column(db.String(128), nullable=False) date_created = db.Column(db.DateTime, default=datetime.utcnow) def clean_doc(doc, vocab): doc = doc.lower() tokens = doc.split() re_punc = re.compile('[%s]' % re.escape(string.punctuation)) tokens = [re_punc.sub('' , w) for w in tokens] tokens = [word for word in tokens if len(word) > 1] stop_words = set(stopwords.words('english')) tokens = [w for w in tokens if not w in stop_words] porter = PorterStemmer() stemmed = [porter.stem(word) for word in tokens] return tokens # load tokenizer with open('./nlp/tokenizer.pickle', 'rb') as handle: tokenizer = pickle.load(handle) # load vocabulary with open('./nlp/vocabulary.txt', 'r') as vocab: vocab = vocab.read() # create the CNN model def create_model(vocab_size, max_length): model = Sequential() model.add(Embedding(vocab_size, 32, input_length=max_length)) model.add(Conv1D(filters=16, kernel_size=4, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) opt = keras.optimizers.Adamax(learning_rate=0.002, beta_1=0.9, beta_2=0.999) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[keras.metrics.AUC()]) return model @app.route("/") def root(): return render_template('index.html') @app.route("/login", methods=["POST", "GET"]) def login(): email = None if request.method == "POST": email = request.form['email'] password = request.form['password'] found_user = users.query.filter_by(email=email).first() if (found_user and (bcrypt.checkpw(password.encode('utf8'), found_user.password))): session['email'] = found_user.email session['name'] = found_user.name session['id'] = found_user.id else: flash("Invalid username or password!", "loginout") return render_template('signup.html') flash("Login Succesful!", "loginout") return render_template('index.html') @app.route("/signup", methods=["POST", "GET"]) def signup(): if request.method == "POST": name = request.form['name'] email = request.form['email'] password = request.form['password'] found_user = users.query.filter_by(email=email).first() if (found_user and (bcrypt.checkpw(password.encode('utf8'), found_user.password)==False)): flash("Already registered, but wrong password!", "loginout") return render_template('signup.html') session['email'] = email session['name'] = name password = bcrypt.hashpw(password.encode('utf8'), bcrypt.gensalt()) user = users(name=name, email=email, password = password) if found_user: session['email'] = found_user.email session['id'] = found_user.id found_user.name = name db.session.commit() else: db.session.add(user) db.session.commit() newuser = users.query.filter_by(email=session['email']).first() session['id'] = newuser.id flash("Login Succesful!", "loginout") return render_template('index.html') else: if 'email' in session: flash("Already Logged In!") return render_template('index.html') return render_template('signup.html') @app.route("/signin", methods=["POST", "GET"]) def signin(): email = None if request.method == "POST": email = request.form['email'] password = request.form['password'] found_user = users.query.filter_by(email=email).first() if (found_user and (bcrypt.checkpw(password.encode('utf8'), found_user.password))): session['email'] = found_user.email session['name'] = found_user.name session['id'] = found_user.id flash("Login Succesful!", "loginout") return render_template('index.html') else: flash("Invalid username or password!", "loginout") return render_template('signup.html') return render_template('signin.html') # change password @app.route("/", methods=["POST", "GET"]) def profile(nm_passwd): try: if "_" in str(nm_passwd): user_name = str(nm_passwd).split("_")[0] user_passwd = str(nm_passwd).split("_")[1] user_passwd = "b\'"+user_passwd+"\'" found_user = users.query.filter_by(name=user_name).first() if request.method == "POST": password = request.form['password'] session['email'] = found_user.email session['name'] = found_user.name session['id'] = found_user.id password = bcrypt.hashpw(password.encode('utf8'), bcrypt.gensalt()) found_user.password = password db.session.commit() flash("Your password is changed!", "loginout") return render_template('index.html') # remove reserved characters from the hashed passwords reserved = (";", "/", "?", ":", "@", "=", "&", ".") def replace_reserved(fullstring): for replace_str in reserved: fullstring = fullstring.replace(replace_str,"") return fullstring replaced_passwd = replace_reserved(str(found_user.password)) if replaced_passwd == user_passwd: return render_template("/passwd_change.html", name=user_name) else: return "This url does not exist" else: return "This url does not exist" except (AttributeError): return "This url does not exist" @app.route("/logout") def logout(): if 'email' in session: global user1 if session['name'] != '': user1 = session['name'] else: user1 = session['email'] flash("You have been logged out, {user1}", "loginout") session.pop('email', None) session.clear() return render_template('index.html') @app.route("/about") def about(): return render_template('about.html') @app.route('/progress') def progress(): #get the type from checkbox search_type = request.args.getlist('type') if (search_type == []): search_type = ['GWAS', 'function', 'addiction', 'drug', 'brain', 'stress', 'psychiatric'] session['search_type'] = search_type # only 1-100 terms are allowed genes=request.args.get('query') genes=genes.replace(",", " ") genes=genes.replace(";", " ") genes=re.sub(r'\bLOC\d*?\b', "", genes, flags=re.I) genes=genes.split() if len(genes)>=100: message="Up to 100 terms can be searched at a time" return render_template('index.html', message=message) elif len(genes)==0: message="Please enter a search term " return render_template('index.html', message=message) tf_path=tempfile.gettempdir() session['path']=tf_path+"/tmp" + ''.join(random.choice(string.ascii_letters) for x in range(6)) # put the query in session cookie session['query']=genes return render_template('progress.html', url_in="search", url_out="cytoscape") @app.route("/search") def search(): genes=session['query'] genes_for_folder_name ="" if len(genes) == 1: marker = "" genes_for_folder_name =str(genes[0]) elif len(genes) == 2: marker = "" genes_for_folder_name =str(genes[0])+"_"+str(genes[1]) elif len(genes) == 3: marker = "" genes_for_folder_name =str(genes[0])+"_"+str(genes[1])+"_"+str(genes[2]) else: genes_for_folder_name =str(genes[0])+"_"+str(genes[1])+"_"+str(genes[2]) marker="_m" # generate a unique session ID depending on timestamp to track the results timestamp = datetime.utcnow().replace(microsecond=0) timestamp = timestamp.replace(tzinfo=pytz.utc) timestamp = timestamp.astimezone(pytz.timezone("America/Chicago")) session['timestamp'] = timestamp timeextension = str(timestamp) timeextension = timeextension.replace(':', '_') timeextension = timeextension.replace('-', '_') timeextension = timeextension.replace(' ', '_') timeextension = timeextension.replace('_06_00', '') user_login=0 #create a folder for the search if ('email' in session): user_login=1 os.makedirs(datadir+"user/"+str(session['email']+"/"+timeextension+"_0_"+genes_for_folder_name+marker),exist_ok=True) session['user_folder'] = datadir+"user/"+str(session['email']) user_folder=session['user_folder'] session['path'] = datadir+"user/"+str(session['email'])+"/"+timeextension+"_0_"+genes_for_folder_name+marker+"/"+timeextension percent=round(100/(len(genes)*6),1) # 6 categories snt_file=session['path']+"_snt" cysdata=open(session['path']+"_cy","w+") sntdata=open(snt_file,"w+") zeroLinkNode=open(session['path']+"_0link","w+") search_type = session['search_type'] #consider the types got from checkbox temp_nodes = "" json_nodes = "{\"data\":[" if ("function" in search_type): temp_nodes += n0 json_nodes += nj0 if ("addiction" in search_type): temp_nodes += n1 json_nodes += nj1 if ("drug" in search_type): temp_nodes += n2 json_nodes += nj2 if ("brain" in search_type): temp_nodes += n3 json_nodes += nj3 if ("stress" in search_type): temp_nodes += n4 json_nodes += nj4 if ("psychiatric" in search_type): temp_nodes += n5 json_nodes += nj5 if ("GWAS" in search_type): temp_nodes += n6 json_nodes += nj6 json_nodes = json_nodes[:-2] json_nodes =json_nodes+"]}" def generate(genes, tf_name): sentences=str() edges=str() nodes = temp_nodes progress=0 searchCnt=0 nodesToHide=str() json_edges = str() for gene in genes: gene=gene.replace("-"," ") # report progress immediately progress+=percent yield "data:"+str(progress)+"\n\n" #addiction terms must present with at least one drug addiction=undic(addiction_d) +") AND ("+undic(drug_d) sent0=gene_category(gene, addiction_d, addiction, "addiction") e0=generate_edges(sent0, tf_name) ej0=generate_edges_json(sent0, tf_name) # drug drug=undic(drug_d) sent1=gene_category(gene, drug_d, drug, "drug") progress+=percent yield "data:"+str(progress)+"\n\n" e1=generate_edges(sent1, tf_name) ej1=generate_edges_json(sent1, tf_name) # function function=undic(function_d) sent2=gene_category(gene, function_d, function, "function") progress+=percent yield "data:"+str(progress)+"\n\n" e2=generate_edges(sent2, tf_name) ej2=generate_edges_json(sent2, tf_name) # brain has its own query terms that does not include the many short acronyms sent3=gene_category(gene, brain_d, brain_query_term, "brain") progress+=percent e3=generate_edges(sent3, tf_name) ej3=generate_edges_json(sent3, tf_name) # stress stress=undic(stress_d) sent4=gene_category(gene, stress_d, stress, "stress") progress+=percent yield "data:"+str(progress)+"\n\n" e4=generate_edges(sent4, tf_name) ej4=generate_edges_json(sent4, tf_name) # psychiatric psychiatric=undic(psychiatric_d) sent5=gene_category(gene, psychiatric_d, psychiatric, "psychiatric") progress+=percent yield "data:"+str(progress)+"\n\n" e5=generate_edges(sent5, tf_name) ej5=generate_edges_json(sent5, tf_name) # GWAS e6=searchArchived('GWAS', gene, 'cys') ej6=searchArchived('GWAS', gene , 'json') #consider the types got from checkbox geneEdges = "" if ("addiction" in search_type): geneEdges += e0 json_edges += ej0 if ("drug" in search_type): geneEdges += e1 json_edges += ej1 if ("function" in search_type): geneEdges += e2 json_edges += ej2 if ("brain" in search_type): geneEdges += e3 json_edges += ej3 if ("stress" in search_type): geneEdges += e4 json_edges += ej4 if ("psychiatric" in search_type): geneEdges += e5 json_edges += ej5 if ("GWAS" in search_type): geneEdges += e6 json_edges += ej6 ## there is a bug here. zero link notes are not excluded anymore if len(geneEdges) >1: edges+=geneEdges nodes+="{ data: { id: '" + gene + "', nodecolor:'#E74C3C', fontweight:700, url:'/startGeneGene?forTopGene="+gene+"'} },\n" else: nodesToHide+=gene + " " sentences+=sent0+sent1+sent2+sent3+sent4+sent5 sent0=None sent1=None sent2=None sent3=None sent4=None sent5=None #save data before the last yield searchCnt+=1 if (searchCnt==len(genes)): progress=100 sntdata.write(sentences) sntdata.close() cysdata.write(nodes+edges) cysdata.close() zeroLinkNode.write(nodesToHide) zeroLinkNode.close() yield "data:"+str(progress)+"\n\n" #edges in json format json_edges="{\"data\":["+json_edges json_edges = json_edges[:-2] json_edges =json_edges+"]}" #write edges to txt file in json format with open(datadir+"edges.json", 'w') as edgesjson: edgesjson.write(json_edges) #write edges to txt file in json format also in user folder if (user_login == 1): with open(user_folder+"/"+timeextension+"_0_"+genes_for_folder_name+marker+"/edges.json", "w") as temp_file_edges: temp_file_edges.write(json_edges) #write nodes to txt file in json format with open(datadir+"nodes.json", 'w') as nodesjson: #if (userlogin) == 1: nodesjson.write(json_nodes) #write nodes to txt file in json format also in user folder if ('email' in session): with open(datadir+"user/"+str(session['email'])+"/"+timeextension+"_0_"+genes_for_folder_name+marker+"/nodes.json", "w") as temp_file_nodes: temp_file_nodes.write(json_nodes) return Response(generate(genes, snt_file), mimetype='text/event-stream') @app.route("/tableview") def tableview(): with open(datadir+"nodes.json") as jsonfile: jnodes = json.load(jsonfile) jedges ='' file_edges = open(datadir+'edges.json', 'r') for line in file_edges.readlines(): if ':' not in line: nodata_temp = 1 else: nodata_temp = 0 with open(datadir+"edges.json") as edgesjsonfile: jedges = json.load(edgesjsonfile) break genename=session['query'] if len(genename)>3: genename = genename[0:3] added = ",..." else: added = "" gene_name = str(genename)[1:] gene_name=gene_name[:-1] gene_name=gene_name.replace("'","") gene_name = gene_name+added num_gene = gene_name.count(',')+1 message3=" Actions:
  • Click on the abstract count to read sentences linking the keyword and the gene.
  • Click on a gene to search its relations with top 200 addiction genes.
  • Click on a keyword to see the terms included in the search.
  • View the results in a graph." return render_template('tableview.html', nodata_temp=nodata_temp, num_gene=num_gene,session_path = session['path'], jedges=jedges, jnodes=jnodes,gene_name=gene_name, message3=message3) @app.route("/tableview0") def tableview0(): with open(datadir+"nodes.json") as jsonfile: jnodes = json.load(jsonfile) jedges ='' file_edges = open(datadir+'edges.json', 'r') for line in file_edges.readlines(): if ':' not in line: nodata_temp = 1 else: nodata_temp = 0 with open(datadir+"edges.json") as edgesjsonfile: jedges = json.load(edgesjsonfile) break genename=session['query'] if len(genename)>3: genename = genename[0:3] added = ",..." else: added = "" gene_name = str(genename)[1:] gene_name=gene_name[:-1] gene_name=gene_name.replace("'","") gene_name = gene_name+added num_gene = gene_name.count(',')+1 message4=" Notes:
  • These are the keywords that have zero abstract counts.
  • View all the results in a graph." return render_template('tableview0.html',nodata_temp=nodata_temp, num_gene=num_gene,session_path = session['path'], jedges=jedges, jnodes=jnodes,gene_name=gene_name, message4=message4) @app.route("/userarchive") def userarchive(): if os.path.exists(datadir+"user/"+str(session['email'])) == False: flash("Search history doesn't exist!") return render_template('index.html') if ('email' in session): session['user_folder'] = datadir+"user/"+str(session['email']) session_id=session['id'] def sorted_alphanumeric(data): convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] return sorted(data, key=alphanum_key) dirlist = sorted_alphanumeric(os.listdir(session['user_folder'])) folder_list = [] directory_list = [] gene_list=[] for filename in dirlist: folder_list.append(filename) gene_name = filename.split('_0_')[1] if gene_name[-2:] == '_m': gene_name = gene_name[:-2] gene_name = gene_name + ", ..." gene_name = gene_name.replace('_', ', ') gene_list.append(gene_name) gene_name="" filename=filename[0:4]+"-"+filename[5:7]+"-"+filename[8:13]+":"+filename[14:16]+":"+filename[17:19] directory_list.append(filename) len_dir = len(directory_list) message3=" Actions:
  • Click on the Date/Time to view archived results.
  • The Date/Time are based on US Central time zone. " return render_template('userarchive.html', len_dir=len_dir, gene_list = gene_list, folder_list=folder_list, directory_list=directory_list, session_id=session_id, message3=message3) # delete this search @app.route('/remove', methods=['GET', 'POST']) def remove(): remove_folder = request.args.get('remove_folder') shutil.rmtree(datadir+"user/"+str(session['email']+"/"+remove_folder), ignore_errors=True) return redirect(url_for('userarchive')) @app.route('/date', methods=['GET', 'POST']) def date(): select_date = request.args.get('selected_date') #open the cache folder for the user tf_path=datadir+"user" if ('email' in session): time_extension = str(select_date) time_extension = time_extension.split('_0_')[0] gene_name1 = str(select_date).split('_0_')[1] time_extension = time_extension.replace(':', '_') time_extension = time_extension.replace('-', '_') session['path'] = tf_path+"/"+str(session['email'])+"/"+select_date+"/"+time_extension session['user_folder'] = tf_path+"/"+str(session['email']) else: tf_path=tempfile.gettempdir() session['path']=tf_path+"/tmp" + ''.join(random.choice(string.ascii_letters) for x in range(6)) with open(tf_path+"/"+str(session['email'])+"/"+select_date+"/edges.json", "r") as archive_file: with open(datadir+"edges.json", "w") as temp_file: for line in archive_file: temp_file.write(line) with open(tf_path+"/"+str(session['email'])+"/"+select_date+"/nodes.json", "r") as archive_file: with open(datadir+"nodes.json", "w") as temp_file: for line in archive_file: temp_file.write(line) with open(datadir+"nodes.json", "r") as jsonfile: jnodes = json.load(jsonfile) jedges ='' file_edges = open(datadir+'edges.json', 'r') for line in file_edges.readlines(): if ':' not in line: nodata_temp = 1 else: nodata_temp = 0 with open(datadir+"edges.json") as edgesjsonfile: jedges = json.load(edgesjsonfile) break gene_list=[] if nodata_temp == 0: for p in jedges['data']: if p['source'] not in gene_list: gene_list.append(p['source']) if len(gene_list)>3: gene_list = gene_list[0:3] added = ",..." else: added = "" gene_name = str(gene_list)[1:] gene_name=gene_name[:-1] gene_name=gene_name.replace("'","") gene_name = gene_name+added num_gene = gene_name.count(',')+1 else: gene_name1 = gene_name1.replace("_", ", ") gene_name = gene_name1 num_gene = gene_name1.count(',')+1 for i in range(0,num_gene): gene_list.append(gene_name1.split(',')[i]) session['query'] = gene_list message3=" Actions:
  • Click on the keywords to see the indicated number of abstracts
  • Click on a gene to search its relations with top 200 addiction genes
  • Click on a keyword to see the terms included in the search
  • Hover your pointer over a node to hide other links
  • Nodes can be moved around for better visibility, reload the page will restore the original layout
  • View the results in a graph." return render_template('tableview.html', title='',nodata_temp=nodata_temp, date=select_date, num_gene=num_gene,session_path = session['path'], jedges=jedges, jnodes=jnodes,gene_name=gene_name, message3=message3) @app.route('/cytoscape') def cytoscape(): message2=" Notes:
  • Click on a line to see the indicated number of abstracts
  • Click on a gene to search its relations with top 200 addiction genes
  • Click on a keyword to see the terms included in the search
  • Hover your pointer over a node to hide other links
  • Nodes can be moved around for better visibility, reload the page will restore the original layout
  • View the results in a table. " with open(session['path']+"_cy","r") as f: elements=f.read() with open(session['path']+"_0link","r") as z: zeroLink=z.read() if (len(zeroLink)>0): message2+="No result was found for these genes: " + zeroLink + "" return render_template('cytoscape.html', elements=elements, message2=message2) @app.route("/sentences") def sentences(): def predict_sent(sent_for_pred): max_length = 64 tokens = clean_doc(sent_for_pred, vocab) tokens = [w for w in tokens if w in vocab] # convert to line line = ' '.join(tokens) line = [line] tokenized_sent = tokenizer.texts_to_sequences(line) tokenized_sent = pad_sequences(tokenized_sent, maxlen=max_length, padding='post') predict_sent = model.predict(tokenized_sent, verbose=0) percent_sent = predict_sent[0,0] if round(percent_sent) == 0: return 'neg' else: return 'pos' pmid_list=[] edge=request.args.get('edgeID') (tf_name, gene0, cat0)=edge.split("|") if(cat0=='stress'): model = create_model(23154, 64) model.load_weights("./nlp/weights.ckpt") out3="" out_pos = "" out_neg = "" num_abstract = 0 stress_cellular = "


    "+"Sentence(s) describing celluar stress (classified using a deep learning model):
      " stress_systemic = "Sentence(s) describing systemic stress (classified using a deep learning model):
      " with open(tf_name, "r") as df: all_sents=df.read() for sent in all_sents.split("\n"): if len(sent.strip())!=0: (gene,nouse,cat, pmid, text)=sent.split("\t") if (gene.upper() == gene0.upper() and cat.upper() == cat0.upper()) : out3+= "
    1. "+ text + " PMID:"+pmid+"
      " num_abstract += 1 if(pmid+cat0 not in pmid_list): pmid_list.append(pmid+cat0) if(cat0=='stress'): out_pred = "
    2. "+ text + " PMID:"+pmid+"
      " out4 = predict_sent(text) if(out4 == 'pos'): out_pos += out_pred else: out_neg += out_pred out1="

      "+gene0 + " and " + cat0 + "

      \n" if len(pmid_list)>1: out2 = str(num_abstract) + ' sentences in ' + str(len(pmid_list)) + ' studies' + "

      " else: out2 = str(num_abstract) + ' sentence(s) in ' + str(len(pmid_list)) + ' study' "

      " if(out_neg == "" and out_pos == ""): out= out1+ out2 +out3 elif(out_pos != "" and out_neg!=""): out = out1 + out2 + stress_systemic+out_pos + stress_cellular + out_neg elif(out_pos != "" and out_neg ==""): out= out1+ out2 + stress_systemic + out_pos elif(out_neg != "" and out_pos == ""): out = out1 +out2+stress_cellular+out_neg return render_template('sentences.html', sentences="
        "+out+"

      ") ## show the cytoscape graph for one gene from the top gene list @app.route("/showTopGene") def showTopGene(): query=request.args.get('topGene') nodesEdges=searchArchived('topGene',query, 'cys') message2="

    3. "+query + " is one of the top addiction genes.
    4. An archived search is shown. Click on the blue circle to update the results and include keywords for brain region and gene function. The update may take a long time to finish. " return render_template("cytoscape.html", elements=nodesEdges, message="Top addiction genes", message2=message2) @app.route("/shownode") def shownode(): node=request.args.get('node') allnodes={**brain_d, **drug_d, **function_d, **addiction_d, **stress_d, **psychiatric_d} out="

      "+node.upper()+"

    5. "+ allnodes[node].replace("|", "
    6. ") return render_template('sentences.html', sentences=out+"

      ") @app.route("/startGeneGene") def startGeneGene(): session['forTopGene']=request.args.get('forTopGene') return render_template('progress.html', url_in="searchGeneGene", url_out="showGeneTopGene") @app.route("/searchGeneGene") def gene_gene(): tmp_ggPMID=session['path']+"_ggPMID" gg_file=session['path']+"_ggSent" #gene_gene result_file=session['path']+"_ggResult" def generate(query): progress=1 yield "data:"+str(progress)+"\n\n" os.system("esearch -db pubmed -query \"" + query + "\" | efetch -format uid |sort >" + tmp_ggPMID) abstracts=os.popen("comm -1 -2 topGene_uniq.pmid " + tmp_ggPMID + " |fetch-pubmed -path "+pubmed_path+ " | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText|sed \"s/-/ /g\"").read() os.system("rm "+tmp_ggPMID) #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() progress=10 yield "data:"+str(progress)+"\n\n" topGenes=dict() out=str() hitGenes=dict() with open("topGene_symb_alias.txt", "r") as top_f: for line in top_f: (symb, alias)=line.strip().split("\t") topGenes[symb]=alias.replace("; ","|") allAbstracts= abstracts.split("\n") abstractCnt=len(allAbstracts) rowCnt=0 for row in allAbstracts: rowCnt+=1 if rowCnt/10==int(rowCnt/10): progress=10+round(rowCnt/abstractCnt,2)*80 yield "data:"+str(progress)+"\n\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(query)(sent): sent=re.sub(r'\b(%s)\b' % query, r'\1', sent, flags=re.I) for symb in topGenes: allNames=symb+"|"+topGenes[symb] if findWholeWord(allNames)(sent) : sent=sent.replace("","").replace("","") sent=re.sub(r'\b(%s)\b' % allNames, r'\1', sent, flags=re.I) out+=query+"\t"+"gene\t" + symb+"\t"+pmid+"\t"+sent+"\n" if symb in hitGenes.keys(): hitGenes[symb]+=1 else: hitGenes[symb]=1 progress=95 yield "data:"+str(progress)+"\n\n" with open(gg_file, "w+") as gg: gg.write(out) gg.close() results="

      "+query+" vs top addiction genes

      Click on the number of sentences will show those sentences. Click on the top addiction genes will show an archived search for that gene.
      " topGeneHits={} for key in hitGenes.keys(): url=gg_file+"|"+query+"|"+key if hitGenes[key]==1: sentword="sentence" else: sentword="sentences" topGeneHits[ "
    7. " + "Show " + str(hitGenes[key]) + " " + sentword +" about "+query+" and "+key+"" ]=hitGenes[key] topSorted = [(k, topGeneHits[k]) for k in sorted(topGeneHits, key=topGeneHits.get, reverse=True)] for k,v in topSorted: results+=k saveResult=open(result_file, "w+") saveResult.write(results) saveResult.close() progress=100 yield "data:"+str(progress)+"\n\n" ## start the run query=session['forTopGene'] return Response(generate(query), mimetype='text/event-stream') @app.route('/showGeneTopGene') def showGeneTopGene (): with open(session['path']+"_ggResult", "r") as result_f: results=result_f.read() return render_template('sentences.html', sentences=results+"


      ") ## generate a page that lists all the top 150 addiction genes with links to cytoscape graph. @app.route("/allTopGenes") def top150genes(): return render_template("topAddictionGene.html") if __name__ == '__main__': db.create_all() app.run(debug=True, port=4200)