# HG changeset patch # User johnheap # Date 1559592024 14400 # Node ID e7da2274c9f6f6b95cb2f28ae226741bc3cba892 # Parent 5e346d75ccf3e96be2f880317167a732236a83db Uploaded diff -r 5e346d75ccf3 -r e7da2274c9f6 Tryp_V.py --- a/Tryp_V.py Mon Jun 03 15:59:39 2019 -0400 +++ b/Tryp_V.py Mon Jun 03 16:00:24 2019 -0400 @@ -1,2064 +1,292 @@ +""" + * Copyright 2018 University of Liverpool + * Author: John Heap, Computational Biology Facility, UoL + * Based on original scripts of Sara Silva Pereira, Institute of Infection and Global Health, UoL + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + """ +import matplotlib as mpl +mpl.use('Agg') +import subprocess +import shutil +import re +import pandas as pd +import os +import sys +import matplotlib.pyplot as plt +from matplotlib.patches import Patch +import seaborn as sns + +def assembleWithVelvet(name, kmers, inslen, covcut, fastq1name,fastq2name): + #argString = "velveth " + name + "_k65 65 -shortPaired -fastq " + name + "_R1.fastq " + name + "_R2.fastq" + argString = "velveth " + name + "_k"+ kmers+" "+ kmers + " -shortPaired -fastq " + fastq1name+" "+fastq2name + print(argString) + returncode = subprocess.call(argString, shell=True) + if returncode != 0: + return "Error in velveth" + argString = "velvetg " + name + "_k"+kmers+" -exp_cov auto -ins_length "+inslen+" -clean yes -ins_length_sd 50 -min_pair_count 20" + #argString = "velvetg " + name + "_k"+kmers+" -exp_cov auto -ins_length "+inslen+" -cov_cutoff "+covcut+" -clean yes -ins_length_sd 50 -min_pair_count 20" + #argString = "velvetg " + name + "_k65 -exp_cov auto -ins_length 400 -cov_cutoff 5 -clean yes -ins_length_sd 50 -min_pair_count 20"+quietString + print(argString) + returncode = subprocess.call(argString, shell = True) + if returncode != 0: + return "Error in velvetg" + shutil.copyfile(name + "_k"+kmers+"//contigs.fa",name + ".fa") # my $namechange = "mv ".$input."_k65/contigs.fa ".$input.".fa"; + return "ok" - - - - - - - - - - - +def blastContigs(test_name,database): + print(test_name) + print(database) + #db_path = os.path.dirname(os.path.realpath(__file__))+database + db_path = database + argString = "blastn -db "+db_path+" -query "+test_name+".fa -outfmt 10 -out "+test_name+"_blast.txt" + print(argString) + returncode = subprocess.call(argString, shell = True) + if returncode != 0: + return "Error in blastall" + blast_df = pd.read_csv(""+test_name+"_blast.txt") + #print (blast_df) + #if ($temp[2] >= 98 & & $temp[3] > 100 & & $temp[10] < 0.001){ + #'qaccver saccver pident length mismatch gapopen qstart qend sstart send evalue bitscore' + blast_df.columns = ['qaccver', 'saccver', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue','bitscore'] + blastResult_df = blast_df[(blast_df['pident']>=98) & (blast_df['length'] > 100) & (blast_df['evalue']<0.001) ] + blastResult_df = blastResult_df[['qaccver', 'saccver', 'pident']] #query accession.version, subject accession.version, Percentage of identical matches + + return blastResult_df - - - - - - - - - - - VAPPER-Galaxy/Tryp_V.py at master · johnheap/VAPPER-Galaxy · GitHub - - - - - - - +def getCogsPresent(blastResult_df,strain,cogOrBinList): + blastResult_df.sort_values('pident',axis = 0, ascending=False, inplace=True) + nodeList = blastResult_df['qaccver'].tolist() + cogList = blastResult_df['saccver'].tolist() + cogSet = set(cogList) #get unique values + cogList = sorted(cogSet) #sort them - - - - - - - - - - - - - - - - - - - + #print (cogList) + #print (len(cogList)) - - - - - - - - - - - - - - - - - - + thereList = [] + dataList = [] + #dir_path = os.path.dirname(os.path.realpath(__file__)) + fname = cogOrBinList + cnt = 0 + with open (fname) as f: + for line in f: + dataList.append(line.rstrip('\n\r ')) + if line.rstrip('\n\r ') in cogList: + thereList.append('1') + cnt = cnt+1 + else: + thereList.append('0') - - - - - - - - - + #print (thereList) + #print (cnt) + data = {'Cog': dataList, strain: thereList} + presence_df = pd.DataFrame(data) + #print (presence_df) + return presence_df - - - - +def addToCurrentData(cog_df, name): + dir_path = os.path.dirname(os.path.realpath(__file__)) + j_fname = dir_path + r"/data/vivax/TvDatabase.csv" + tv_df = pd.read_csv(j_fname) - + cogList = cog_df[name].tolist() + #cogList.insert(0,'Test') + #print (len(tv_df)) + #print(len(cogList)) - - - - - + #print(cogList) + tv_df.loc[:,name]=cogList + return tv_df - - - - - - - -
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- + countryList = pd.unique(geo_df['Location']) + colourList = pd.unique(geo_df['colour']) + legend_elements = [Patch(facecolor='orangered', label='COG Present'), + Patch(facecolor='skyblue', label='COG Absent')] -
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- - + for i in range(0, len(colourList)): + print("country = %s, colour = %s" % (countryList[i], colourList[i])) + p = Patch(facecolor=str(colourList[i]), label=countryList[i]) + legend_elements.append(p) -
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- - - - + ax.legend(handles = legend_elements, bbox_to_anchor=(-0.3,1.2),loc = 'upper left') -
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    + #plt.setp(cg.ax_heatmap.yaxis.get_ticklabels(), rotation=0 ) # get y labels printed horizontally + # cg.dendrogram_col.linkage # linkage matrix for columns + # cg.dendrogram_row.linkage # linkage matrix for rows + # plt.savefig(r"results/" + name + "_heatmap.png") + #plt.savefig(htmlresource + "/heatmap.png") + #if pdf == 'PDF_Yes': + # plt.savefig(htmlresource + "/heatmap.pdf") + # shutil.copyfile("heatmap.pdf",heatmapfn) # + #plt.legend() + fname = html_path+"/"+name+"_clustermap.png" + cg.savefig(fname) + if pdfExport == 'PDF_Yes': + fname = html_path + "/" + name + "_clustermap.pdf" + cg.savefig(fname) + #plt.show() - +def createHTML(name,htmlfn,htmlPath): + #assumes imgs are heatmap.png, dheatmap.png, vapPCA.png and already in htmlresource + htmlString = r"T.vivax VAP

    Trypanosoma vivax Variant Antigen Profile

    " + htmlString += name -
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+ htmlString += r'

The Heat Map and Dendrogram

' + imgString = r"Cog Clustermap

" + htmlString += imgString + print(htmlString) -

- - /VAPPER-Galaxy - - -

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- - + vivaxPath = os.path.dirname(os.path.realpath(__file__))+r"/data/vivax" + assembleWithVelvet(args[dict['name']], args[dict['kmers']], args[dict['inslen']], args[dict['covcut']], + args[dict['forward']], args[dict['reverse']]) + blastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/COGs.fas") + orthPresence_df = getCogsPresent(blastResult_df, args[dict['name']], vivaxPath+r"/Database/COGlist.txt") -
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- + binBlastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/Bin_2.fas") + binPresence_df = getCogsPresent(binBlastResult_df, args[dict['name']], vivaxPath+r"/Database/binlist.txt") + cogPresence_df = orthPresence_df.append(binPresence_df, ignore_index=True) -
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- - - - + fname = args[dict['html_resource']]+r"/"+ args[dict['name']]+"_cogspresent.csv" + cogPresence_df.to_csv(fname) + current_df = addToCurrentData(cogPresence_df,args[dict['name']]) # load in Tvdatabase and add cogPresence column to it. + createClusterMap(current_df, args[dict['name']], args[dict['html_resource']], args[dict['pdfexport']]) + createHTML(args[dict['name']],args[dict['html_file']],args[dict['html_resource']]) - - Permalink - - - - - -
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"""
* Copyright 2018 University of Liverpool
* Author: John Heap, Computational Biology Facility, UoL
* Based on original scripts of Sara Silva Pereira, Institute of Infection and Global Health, UoL
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
"""
-
import matplotlib as mpl
mpl.use('Agg')
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import subprocess
import shutil
import re
import pandas as pd
import os
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import sys
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import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import seaborn as sns
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def assembleWithVelvet(name, kmers, inslen, covcut, fastq1name,fastq2name):
#argString = "velveth " + name + "_k65 65 -shortPaired -fastq " + name + "_R1.fastq " + name + "_R2.fastq"
argString = "velveth " + name + "_k"+ kmers+" "+ kmers + " -shortPaired -fastq " + fastq1name+" "+fastq2name
print(argString)
returncode = subprocess.call(argString, shell=True)
if returncode != 0:
return "Error in velveth"
argString = "velvetg " + name + "_k"+kmers+" -exp_cov auto -ins_length "+inslen+" -clean yes -ins_length_sd 50 -min_pair_count 20"
#argString = "velvetg " + name + "_k"+kmers+" -exp_cov auto -ins_length "+inslen+" -cov_cutoff "+covcut+" -clean yes -ins_length_sd 50 -min_pair_count 20"
#argString = "velvetg " + name + "_k65 -exp_cov auto -ins_length 400 -cov_cutoff 5 -clean yes -ins_length_sd 50 -min_pair_count 20"+quietString
print(argString)
returncode = subprocess.call(argString, shell = True)
if returncode != 0:
return "Error in velvetg"
shutil.copyfile(name + "_k"+kmers+"//contigs.fa",name + ".fa") # my $namechange = "mv ".$input."_k65/contigs.fa ".$input.".fa";
return "ok"
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def blastContigs(test_name,database):
print(test_name)
print(database)
#db_path = os.path.dirname(os.path.realpath(__file__))+database
db_path = database
argString = "blastn -db "+db_path+" -query "+test_name+".fa -outfmt 10 -out "+test_name+"_blast.txt"
print(argString)
returncode = subprocess.call(argString, shell = True)
if returncode != 0:
return "Error in blastall"
blast_df = pd.read_csv(""+test_name+"_blast.txt")
#print (blast_df)
#if ($temp[2] >= 98 & & $temp[3] > 100 & & $temp[10] < 0.001){
#'qaccver saccver pident length mismatch gapopen qstart qend sstart send evalue bitscore'
blast_df.columns = ['qaccver', 'saccver', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue','bitscore']
blastResult_df = blast_df[(blast_df['pident']>=98) & (blast_df['length'] > 100) & (blast_df['evalue']<0.001) ]
blastResult_df = blastResult_df[['qaccver', 'saccver', 'pident']] #query accession.version, subject accession.version, Percentage of identical matches
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return blastResult_df
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def getCogsPresent(blastResult_df,strain,cogOrBinList):
blastResult_df.sort_values('pident',axis = 0, ascending=False, inplace=True)
nodeList = blastResult_df['qaccver'].tolist()
cogList = blastResult_df['saccver'].tolist()
cogSet = set(cogList) #get unique values
cogList = sorted(cogSet) #sort them
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#print (cogList)
#print (len(cogList))
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thereList = []
dataList = []
#dir_path = os.path.dirname(os.path.realpath(__file__))
fname = cogOrBinList
cnt = 0
with open (fname) as f:
for line in f:
dataList.append(line.rstrip('\n\r '))
if line.rstrip('\n\r ') in cogList:
thereList.append('1')
cnt = cnt+1
else:
thereList.append('0')
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#print (thereList)
#print (cnt)
data = {'Cog': dataList, strain: thereList}
presence_df = pd.DataFrame(data)
#print (presence_df)
return presence_df
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def addToCurrentData(cog_df, name):
dir_path = os.path.dirname(os.path.realpath(__file__))
j_fname = dir_path + r"/data/vivax/TvDatabase.csv"
tv_df = pd.read_csv(j_fname)
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cogList = cog_df[name].tolist()
#cogList.insert(0,'Test')
#print (len(tv_df))
#print(len(cogList))
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#print(cogList)
tv_df.loc[:,name]=cogList
return tv_df
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def createClusterMap(tv_df,name,html_path,pdfExport):
#Retrieve Data
dir_path = os.path.dirname(os.path.realpath(__file__))
j_fname = dir_path+r"/data/vivax/geoTv.csv"
geo_df = pd.read_csv(j_fname)
geo_df.loc[len(geo_df)] =[name,name,'k']
print(geo_df)
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myStrains = tv_df.columns.values.tolist() #beware first entry is COG
myStrains = myStrains[1:]
print(myStrains)
myPal = []
for s in myStrains:
col = geo_df[(geo_df['Strain'] == s)]['colour'].tolist()
myPal.append(col[0])
print(myPal)
mycogmap = ['skyblue', 'orangered'] # blue absent,red present
tv_df.set_index('COG', inplace=True)
tv_df = tv_df[tv_df.columns].astype(float)
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cg = sns.clustermap(tv_df, method='ward', col_colors=myPal, cmap=mycogmap, yticklabels = 1500, row_cluster=False, linewidths = 0)
#cg = sns.clustermap(tv_df, method='ward', row_cluster=False, linewidths = 0)
ax = cg.ax_heatmap
#xasix ticks and labels.
ax.xaxis.tick_top() #set ticks at top
newlabs = []
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labs = ax.xaxis.get_ticklabels()
for i in range(0, len(labs)):
print(labs[i])
# labs[i].set_text(" "+labs[i].get_text()) #make enough room so label sits atop of col_color bars
newlabs.append(" " + labs[i].get_text())
ax.xaxis.set_ticklabels(newlabs)
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#labs = ax.xaxis.get_ticklabels()
#for i in range(0, len(labs)):
# print(labs[i])
# labs[i].set_text(" "+labs[i].get_text()) #make enough room so label sits atop of col_color bars
# print(labs[i])
#ax.xaxis.set_ticklabels(labs)
plt.setp(cg.ax_heatmap.xaxis.get_ticklabels(), rotation=90) # get x labels printed vertically
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cg.cax.set_visible(False)
ax = cg.ax_heatmap
ax.set_yticklabels("")
ax.set_ylabel("")
ax = cg.ax_heatmap
# ax.set_title("Variant antigen profiles of T. vivax genomes.\nDendrogram reflects the VSG repertoire relationships of each strain inferred by the presence and absence of non-universal T. vivax VSG orthologs.", va = "top", wrap = "True")
b = len(tv_df)
print(b)
title = "Figure Legend: The Variant Antigen Profiles of $\itTrypanosoma$ $\itvivax$ " \
"showing the \ncombination of present and absent diagnostic cluster of VSG orthologs " \
"across the sample cohort. \nDendrogram reflects the relationships amongst the VSG" \
" repertoires of each strain. " \
"Strains were isolated \nfrom multiple African countries as shown in the key.\nData was produced with the " \
"'Variant Antigen Profiler' (Silva Pereira et al., 2019)."
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ax.text(-1.5, len(tv_df) + 8,
title,
ha="left", va="top", wrap="True")
col = cg.ax_col_dendrogram.get_position()
cg.ax_col_dendrogram.set_position([col.x0, col.y0*1.08, col.width, col.height*1.1])
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countryList = pd.unique(geo_df['Location'])
colourList = pd.unique(geo_df['colour'])
legend_elements = [Patch(facecolor='orangered', label='COG Present'),
Patch(facecolor='skyblue', label='COG Absent')]
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for i in range(0, len(colourList)):
print("country = %s, colour = %s" % (countryList[i], colourList[i]))
p = Patch(facecolor=str(colourList[i]), label=countryList[i])
legend_elements.append(p)
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ax.legend(handles = legend_elements, bbox_to_anchor=(-0.3,1.2),loc = 'upper left')
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#plt.setp(cg.ax_heatmap.yaxis.get_ticklabels(), rotation=0 ) # get y labels printed horizontally
# cg.dendrogram_col.linkage # linkage matrix for columns
# cg.dendrogram_row.linkage # linkage matrix for rows
# plt.savefig(r"results/" + name + "_heatmap.png")
#plt.savefig(htmlresource + "/heatmap.png")
#if pdf == 'PDF_Yes':
# plt.savefig(htmlresource + "/heatmap.pdf")
# shutil.copyfile("heatmap.pdf",heatmapfn) #
#plt.legend()
fname = html_path+"/"+name+"_clustermap.png"
cg.savefig(fname)
if pdfExport == 'PDF_Yes':
fname = html_path + "/" + name + "_clustermap.pdf"
cg.savefig(fname)
#plt.show()
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def createHTML(name,htmlfn,htmlPath):
#assumes imgs are heatmap.png, dheatmap.png, vapPCA.png and already in htmlresource
htmlString = r"<html><title>T.vivax VAP</title><body><div style='text-align:center'><h2><i>Trypanosoma vivax</i> Variant Antigen Profile</h2><h3>"
htmlString += name
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htmlString += r'<p> <h3>The Heat Map and Dendrogram</h3></p>'
imgString = r"<img src = '"+name+"_clustermap.png' alt='Cog Clustermap' style='max-width:100%'><br><br>"
htmlString += imgString
print(htmlString)
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with open(htmlfn, "w") as htmlfile:
htmlfile.write(htmlString)
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def vivax_assemble(args,dict):
#argdict = {'name': 2, 'pdfexport': 3, 'kmers': 4, 'inslen': 5, 'covcut': 6, 'forward': 7, 'reverse': 8, 'html_file': 9,'html_resource': 10}
#assembleWithVelvet("V2_Test", '65', '400', '5', "data/TviBrRp.1.clean", "data/TviBrRp.2.clean")
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vivaxPath = os.path.dirname(os.path.realpath(__file__))+r"/data/vivax"
assembleWithVelvet(args[dict['name']], args[dict['kmers']], args[dict['inslen']], args[dict['covcut']],
args[dict['forward']], args[dict['reverse']])
blastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/COGs.fas")
orthPresence_df = getCogsPresent(blastResult_df, args[dict['name']], vivaxPath+r"/Database/COGlist.txt")
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binBlastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/Bin_2.fas")
binPresence_df = getCogsPresent(binBlastResult_df, args[dict['name']], vivaxPath+r"/Database/binlist.txt")
cogPresence_df = orthPresence_df.append(binPresence_df, ignore_index=True)
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fname = args[dict['html_resource']] +args[dict['name']]+"_cogspresent.csv"
cogPresence_df.to_csv(fname)
current_df = addToCurrentData(cogPresence_df,args[dict['name']]) # load in Tvdatabase and add cogPresence column to it.
createClusterMap(current_df, args[dict['name']],args[dict['html_resource']],args[dict['pdfexport']])
createHTML(args[dict['name']],args[dict['html_file']],args[dict['html_resource']])
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def test_cluster(args,dict):
print ("name: %s",args[dict['name']])
cogPresence_df = pd.read_csv("test_cogspresent.csv")
print(cogPresence_df)
current_df = addToCurrentData(cogPresence_df,args[dict['name']]) # load in Tvdatabase and add cogPresence column to it.
createClusterMap(current_df, args[dict['name']], args[dict['html_resource']], args[dict['pdfexport']])
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def vivax_contigs(args,dict):
# argdict = {'name': 2, 'pdfexport': 3, 'contigs': 4, 'html_file': 5, 'html_resource': 6}
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#test_cluster(args,dict)
#return;
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#subprocess.call('echo $PATH',shell = True)
#sys.exit(1)
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vivaxPath = os.path.dirname(os.path.realpath(__file__))+r"/data/vivax"
shutil.copyfile(args[dict['contigs']], args[dict['name']]+".fa")
blastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/COGs.fas")
orthPresence_df = getCogsPresent(blastResult_df, args[dict['name']], vivaxPath+r"/Database/COGlist.txt")
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binBlastResult_df = blastContigs(args[dict['name']], vivaxPath+r"/Database/Bin_2.fas")
binPresence_df = getCogsPresent(binBlastResult_df, args[dict['name']], vivaxPath+r"/Database/binlist.txt")
cogPresence_df = orthPresence_df.append(binPresence_df, ignore_index=True)
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fname = args[dict['html_resource']]+r"/"+ args[dict['name']]+"_cogspresent.csv"
cogPresence_df.to_csv(fname)
current_df = addToCurrentData(cogPresence_df,args[dict['name']]) # load in Tvdatabase and add cogPresence column to it.
createClusterMap(current_df, args[dict['name']], args[dict['html_resource']], args[dict['pdfexport']])
createHTML(args[dict['name']],args[dict['html_file']],args[dict['html_resource']])
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if __name__ == "__main__":
sys.exit()
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