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#!/home/erik/bin/python3.6
#import packages to be used
from sklearn.svm import SVC
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
import cgi, cgitb
#----------------------------------------------\
# Parse the web-form information to variables \
# \_______________________________________________________
# |
cgitb.enable()
form=cgi.FieldStorage()
alignment = form.getvalue('fasta')
if alignment.startswith(">"): #naive check for FASTA format
list=alignment.split(">")
book={}
for a in list:
tempList=a.splitlines()
nameLine=tempList.pop(0)
name=nameLine.split(" ")[0]
seq="".join(tempList)
book[name]=seq
seqList=[]
lenList=[]
nameList=[]
for i in book:
nameList.append(i)
seqList.append(book[i])
lenList.append(str(len(book[i])))
if len(seqList)=0: #check for empty sequence list
seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
nameList=['demo']
lenList=[str(len(alignment[0]))]
else:
seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
nameList=['demo']
lenList=[str(len(alignment[0]))]
#--------------------------------------------------------------------------------------------------------+
#----------------------------------------------\
# predict genus of input sequences \
# \_______________________________________________________
# |
#list of amino acids as vocabulary for the CountVectorizer
AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y']
#load the classifier and scaler
clf=joblib.load("./cgi-bin/SVM_linear_aa_clf.pkl")
StSc=joblib.load("./cgi-bin/UniqRepsGemys_6089_StSCALER.pkl")
cv=CountVectorizer(analyzer='char',ngram_range=(1,1),vocabulary=AAs)
#initialize text data vectorizer
dataVect=cv.transform(seqList)
#Scale the data to the training set
X=StSc.transform(dataVect.astype("float64"))
#make predictions for the original dataset
predictions=clf.predict(X)
#----------------------------------------------\
# Build HTML table of results \
# \_______________________________________________________
# |
results=""""""
for k in len(seqList):
results+="""<tr><td>{0}</td><td>{1}</td><td>{2}</td></tr>""".format(nameList[k],lenList[k],predictions[k])
if "demo" in nameList:
results+="""<p>There seems to have been an error.<br>If you are expecting more than one prediction or
do not see the name you entered please try the submission form again, making sure that the input is in FASTA format."""
#----------------------------------------------\
# Build output page \
# \_______________________________________________________
# |
#build output page parts
#Header and CSS Style bits
header="""
<!DOCTYPE html>
<html>
<head>
<style>
* {box-sizing: border-box}
body {font-family: "Lato", sans-serif;}
/* Style the tab */
div.tab {
float: left;
border: 1px solid #ccc;
background-color: #f1f1f1;
width: 20%;
height: 250px;
}
/* Style the buttons inside the tab */
div.tab button {
display: block;
background-color: inherit;
color: black;
padding: 22px 16px;
width: 100%;
border: none;
outline: none;
text-align: left;
cursor: pointer;
transition: 0.3s;
font-size: 17px;
}
/* Change background color of buttons on hover */
div.tab button:hover {
background-color: #ddd;
}
/* Create an active/current "tab button" class */
div.tab button.active {
background-color: #1acefc;
}
/* Style the tab content */
.tabcontent {
float: left;
padding: 0px 12px;
border: 1px solid #ccc;
width: 80%;
min-height: 250px;
}
table {
border-collapse: collapse;
width: 80%;
}
th, td {
text-align: left;
padding: 8px;
}
tr:nth-child(even){background-color: #f2f2f2}
th {
background-color: #ff0000;
color: white;
}
</style>
</head>
"""
#Page contents, first part
body1="""
<body>
<p>Welcome to CRESSdna.org</p>
<div class="tab">
<button class="tablinks" onclick="openTab(event, 'Home')" id="defaultOpen">Home</button>
<button class="tablinks" onclick="openTab(event, 'Taxonomy')">Taxonomy</button>
<button class="tablinks" onclick="openTab(event, 'Contact')">Contact</button>
<button class="tablinks" onclick="openTab(event, 'Results')">Results</button>
</div>
<div id="Home" class="tabcontent">
<h3>Home</h3>
<p>Part of the <a href='http://www.nsf.gov/pubs/2010/nsf10513/nsf10513.htm'>National Science Foundation's Assembling the Tree of Life</a>.</p>
<img src='nsf1.jpg' alt='Sponsored with a Grant from the National Science Foundation'>
</div>
<div id="Taxonomy" class="tabcontent">
<h3>Taxonomy</h3>
<p>Please enter only one word as the name(no space) and only one Rep sequence</p>
<form action="./cgi-bin/classifier.py" method="post"><br>
<input type="text" name="seqname" value="seqID"><br>
<textarea rows="4" cols="50" name="fasta" input type="submit">
Enter ONE Rep protein sequence here...</textarea>
<br>
<input type="reset">
<input type="submit">
</form>
<p>
<ul>
<li>This classifier requires Rep protein sequence to be:</li>
<ul>
<li>Complete</li>
<li>Unaligned</li>
<li>in FASTA format</li>
</ul>
<p>And has been trained on the following Genera:</p>
<li>Circoviridae</li>
<ul>
<li>Circovirus</li>
<li>Cyclovirus</li>
</ul>
<li>Nanoviridae</li>
<ul>
<li>Babuvirus</li>
<li>Nanovirus</li>
</ul>
<li>Genomoviridae</li>
<ul>
<li>Gemycircularvirus</li>
<li>Gemygorvirus</li>
<li>Gemykibivirus</li>
<li>Gemykolovirus</li>
<li>Gemykrogvirus</li>
<li>Gemyvongvirus</li>
</ul>
<li>Geminiviridae</li>
<ul>
<li>Becurtovirus</li>
<li>Begomovirus</li>
<li>Capulavirus</li>
<li>Curtovirus</li>
<li>Eragrovirus</li>
<li>Grablovirus</li>
<li>Mastrevirus</li>
<li>Turncurtovirus</li>
</ul>
<li>Smacovirus</li>
</ul> </p>
</div>
<div id="Contact" class="tabcontent">
<h3>Contact</h3>
<p>Questions or comments? Send us an email:</p>
<p>email At domain Dot something</p>
</div>
<div id="Results" class="tabcontent">
<h3>Results</h3>
<p>Results from Taxonomy prediction</p>
<table>
<tr>
<th>Sequence Name</th>
<th>Length</th>
<th>Prediction</th>
</tr>
"""
#Page contents, second part (results fit between body1 and body2)
body2="""
</table>
<p>This classifier will return the best fit of the submitted sequence to the training data.<br>
Currently included in the training data:<br>
<li>Circoviridae</li>
<ul>
<li>Circovirus</li>
<li>Cyclovirus</li>
</ul>
<li>Nanoviridae</li>
<ul>
<li>Babuvirus</li>
<li>Nanovirus</li>
</ul>
<li>Genomoviridae</li>
<ul>
<li>Gemycircularvirus</li>
<li>Gemygorvirus</li>
<li>Gemykibivirus</li>
<li>Gemykolovirus</li>
<li>Gemykrogvirus</li>
<li>Gemyvongvirus</li>
</ul>
<li>Geminiviridae</li>
<ul>
<li>Becurtovirus</li>
<li>Begomovirus</li>
<li>Capulavirus</li>
<li>Curtovirus</li>
<li>Eragrovirus</li>
<li>Grablovirus</li>
<li>Mastrevirus</li>
<li>Turncurtovirus</li>
</ul>
<li>Smacovirus</li>
<br><br>
</p>
</div>
<script>
function openTab(evt, tabTitle) {
var i, tabcontent, tablinks;
tabcontent = document.getElementsByClassName("tabcontent");
for (i = 0; i < tabcontent.length; i++) {
tabcontent[i].style.display = "none";
}
tablinks = document.getElementsByClassName("tablinks");
for (i = 0; i < tablinks.length; i++) {
tablinks[i].className = tablinks[i].className.replace(" active", "");
}
document.getElementById(tabTitle).style.display = "block";
evt.currentTarget.className += " active";
}
// Get the element with id="defaultOpen" and click on it
document.getElementById("defaultOpen").click();
</script>
</body>
"""
#close the Page
footer="""
</html>
"""
#build the output page
page=header+body1+results+body2+footer
#send the output as html
output = page.format()
print (output)
quit()
|
