johnheap/VAPPER-Galaxy

+ + + + + + + + + + + +

+ + + + + + + + + + + + + +

+ + + + +

+ + Find file + + + Copy path + +

+ + + +

+ + Find file + + + Copy path + +

+ + + + +

+ Fetching contributors… +

+ +

+ Cannot retrieve contributors at this time +

+ + + + + + +

+ +

+ 426 lines (349 sloc) + + 17.3 KB +

+ +

+ Raw + Blame + History +

+ + +

+ + + +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

	"""
	* Galaxy Version

	* Copyright 2019 University of Liverpool
	* Author John Heap, Computational Biology Facility, UoL
	* Based on original scripts of Sara Silva 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 subprocess
	import pandas as pd
	import re
	import os
	import sys
	import shutil
	# import matplotlib as mpl
	# mpl.use('Agg')
	import matplotlib.pyplot as plt
	import numpy as np
	+
	+
	+
	+
	# copies the user provided Fasta file to data/reference/file/file.fasta
	def uploadUserReferenceFastq(refFastq):
	refBase = os.path.basename(refFastq)
	ref = os.path.splitext(refBase)[0] # 'mydata/test.fasta' -> 'test'
	dir_path = os.path.dirname(os.path.realpath(__file__)) # directory of this file
	refDir = dir_path + "/data/Reference/" + ref #propose putting file in '/data/reference/ref/
	if not os.path.isdir(refDir): # if directory data/Reference/ref doesn't exist
	os.mkdir(refDir)
	refPath = refDir+"/"
	shutil.copy(refFastq, refPath + refBase) #copy reference file into the directory
	argString = "bowtie2-build " + refPath + refBase+" "+refPath+ref
	print("Building the bowtie2 reference files.")
	subprocess.call(argString, shell=True)
	return
	+
	def transcriptMapping(inputname, refFastq, forwardFN, reverseFN):
	# where is our Reference data?
	refBase = os.path.basename(refFastq)
	ref = os.path.splitext(refBase)[0]
	dir_path = os.path.dirname(os.path.realpath(__file__))
	refDir = dir_path + "/data/Reference/" + ref + "/"
	refName = refDir + ref
	# now have reference file so we can proceed with the transcript mapping via bowtie2
	argString = "bowtie2 -x "+refName+" -1 "+forwardFN+" -2 "+reverseFN+" -S "+inputname+".sam"
	print(argString)
	subprocess.call(argString, shell=True) #outputs a name.sam file
	return
	+
	+
	+
	def processSamFiles(inputname):
	cur_path = os.getcwd()
	samName = cur_path+"/"+inputname
	argString = "samtools view -bS "+inputname+".sam > "+samName+".bam"
	print(argString)
	subprocess.call(argString, shell=True)
	+
	argString = "samtools sort "+samName+".bam -o "+samName+".sorted"
	print("argstring = "+argString)
	subprocess.call(argString, shell=True)
	+
	argString = "samtools index "+samName+".sorted "+samName+".sorted.bai"
	print("argstring = " + argString)
	subprocess.call(argString, shell=True)
	return #we have saved out the relevent name.bam, name.sorted and name.sorted.bai files
	+
	# we will not have the .gtf file so call cufflinks without -G option
	def transcriptAbundance(inputname):
	argString = "cufflinks -o "+inputname+".cuff -u -p 8 "+inputname+".sorted"
	subprocess.call(argString, shell = True)
	os.remove(inputname+".sorted") #remove name.sorted
	os.remove(inputname+".sorted.bai")
	os.remove(inputname+".bam")
	return
	+
	def transcriptsForBlast(name, refFastq):
	# quick and dirty just to see.
	refBase = os.path.basename(refFastq)
	ref = os.path.splitext(refBase)[0] # 'mydata/test.fasta' -> 'test'
	dir_path = os.path.dirname(os.path.realpath(__file__)) # directory of this file
	refPath = dir_path + "/data/Reference/" + ref + "/" + refBase # eg refPath = data/Reference/Trinity/Trinity.fasta
	# used for dirty # refPath = 'Trinity.fasta' # dirty one
	track_df = pd.read_csv(name+'.cuff/genes.fpkm_tracking', sep='\t')
	names = track_df['locus']
	# print(len(names))
	# print(names[:5])
	+
	nlist = []
	for n in range(0,len(names)):
	i = names[n].find(':')
	nlist.append(names[n][:i])
	nameset = set(nlist) #get unique.
	with open(refPath, 'r') as myRef:
	refData = myRef.read()
	refData= refData+'\n>'
	+
	with open(name + '_for_blast.fa', 'w') as outfile:
	for trans_id in nameset:
	namepos = refData.find(trans_id)
	endpos = refData.find('>', namepos)
	outfile.write('>'+refData[namepos:endpos])
	+
	pass
	+
	def blastContigs(test_name,html_resource, database):
	db_path = database
	argString = "blastx -db "+db_path+" -query "+test_name+"_for_blast.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")
	blast_df.columns = ['qaccver', 'saccver', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue','bitscore']
	blastResult_df = blast_df[(blast_df['pident']>=70) & (blast_df['length'] > 100) & (blast_df['evalue'] <=0.001) ]
	blastResult_df = blastResult_df[['qaccver', 'saccver', 'pident', 'evalue', 'bitscore']] #query accession.version, subject accession.version, Percentage of identical matches
	# need to allocate the transcripts (if allocated more than once to the phylotype with least error.
	transcripts = blastResult_df['qaccver']
	b_df = pd.DataFrame(columns=['qaccver', 'saccver', 'pident', 'evalue', 'bitscore'])
	transSet = set(transcripts)
	for t in transSet:
	temp_df = blastResult_df[(blastResult_df['qaccver'] == t)]
	# get one with smallest error value
	#print(t + ":")
	#print(temp_df)
	temp_df = temp_df.sort_values(by=['evalue'])
	b_df = b_df.append(temp_df.iloc[[0]])
	+
	b_df.sort_values(by=['qaccver'])
	b_df.to_csv(test_name + '_transcript.csv')
	return b_df
	+
	+
	def createMultiHTML(tdict,composite_df):
	labelList = composite_df.columns.tolist()
	htmlString = r"<html><title>T.vivax VAP (Transcriptomic Pathway(</title><body><div style='text-align:center'><h2><i>Trypanosoma vivax</i> Variant Antigen Profile</h2><h3>"
	htmlString += r"Sample name: "+tdict['name']
	htmlString += r"<br>Transcriptomic Analysis</h3></p>"
	htmlString += "<p style = 'margin-left:20%; margin-right:20%'>Legend: " \
	"Variant Antigen Profile of a <i>Trypanosoma vivax</i> transcriptomes. " \
	"Weighted Frequency reflects Phylotype abundance and is expressed as " \
	"phylotype frequencies adjusted for the combined transcript abundance. " \
	"Data was produced with VAPPER-Variant Antigen Profiler " \
	"(Silva Pereira et al., 2019).</p> "
	htmlString += r"<style> table, th, tr, td {border: 1px solid black; border-collapse: collapse;}</style>"
	+
	header = r"<table style='width:50%;margin-left:25%;text-align:center'><tr><th>Phylotype</th>"
	wLists = []
	+
	for j in range(1,len(labelList)):
	wLists.append(composite_df[labelList[j]])
	header += r"<th>" + str(labelList[j]) + "</th>"
	+
	htmlString += "</tr>\n" + header
	tabString = ""
	phyList = composite_df['Phylotype']
	+
	+
	+
	for i in range(0, len(composite_df)):
	tabString += "<tr><td>" + str(phyList[i]) + "</td>"
	for j in range(0,len(labelList)-1):
	#print(j)
	f = format(wLists[j][i], '.4f')
	tabString += "<td>" + str(f) + "</td>"
	tabString += "</tr>\n"
	+
	htmlString += tabString + "</table><br><br><br><br><br>"
	htmlString += r"<h3>Weighted Relative Frequencies of Detected Phylotypes.</h3>"
	imgString = r"<img src = '"+ tdict['name']+"_phylotypes.png' alt='Bar chart of phylotype variation' style='max-width:100%'><br><br>"
	htmlString += imgString
	+
	with open(tdict['html_file'], "w") as htmlfile:
	htmlfile.write(htmlString)
	+
	+
	def createHTML(tdict,sum_df):
	#assumes imgs are heatmap.png, dheatmap.png, vapPCA.png and already in htmlresource
	htmlString = r"<html><title>T.vivax VAP (Transcriptomic Pathway(</title><body><div style='text-align:center'><h2><i>Trypanosoma vivax</i> Variant Antigen Profile</h2><h3>"
	htmlString += r"Sample name: "+tdict['name']
	htmlString += r"<br>Transcriptomic Analysis</h3></p>"
	htmlString += "<p style = 'margin-left:20%; margin-right:20%'>Legend: " \
	"Variant Antigen Profile of a <i>Trypanosoma vivax</i> transcriptome. " \
	"Weighted Frequency reflects Phylotype abundance and is expressed as " \
	"phylotype frequencies adjusted for the combined transcript abundance. " \
	"Data was produced with VAPPER-Variant Antigen Profiler " \
	"(Silva Pereira et al., 2019).</p> "
	htmlString += r"<style> table, th, tr, td {border: 1px solid black; border-collapse: collapse;}</style>"
	+
	htmlString += r"<table style='width:50%;table-layout: auto; margin-left:25%;text-align:center'><tr><th>Phylotype</th>" \
	r"<th>Combined FPKM</th><th>Weighted Frequency</th></tr>"
	tabString = ""
	# flush out table with correct values
	phySeries = sum_df['Phylotype']
	# sacSeries = sum_df['saccver']
	fSeries = sum_df['FPKM']
	total = fSeries.sum()
	# print("Total="+str(total))
	for i in range(0, len(sum_df)):
	# print(phySeries[i])
	f = format(fSeries[i], '.2f')
	w = format(fSeries[i]/total, '.2f')
	+
	#w = format(weightList[i], '.4f')
	+
	tabString += "<tr><td>" + str(phySeries[i]) + "</td><td>" + str(f) + "</td><td>"+str(w)+"</tr>"
	htmlString += tabString + "</table><br><br><br><br><br>"
	htmlString += r"<h3>Weighted Relative Frequencies of Detected Phylotypes.</h3>"
	imgString = r"<img src = '"+ tdict['name']+"_phylotypes.png' alt='Bar chart of phylotype variation' style='max-width:100%'><br><br>"
	htmlString += imgString
	+
	with open(tdict['html_file'], "w") as htmlfile:
	htmlfile.write(htmlString)
	+
	+
	+
	def getPhyloNumber(sac):
	i = sac.find('_')
	return int(sac[1:i])
	+
	def combineFPMK(tdict):
	fpkm_df = pd.read_csv(tdict['name']+'.cuff/genes.fpkm_tracking', sep='\t')
	+
	#fpkm_df = pd.read_csv('genes.fpkm_tracking',sep='\t')
	#print(fpkm_df.head())
	fpkm_df['locus'] = fpkm_df['locus'].apply(lambda names: names[:names.find(':')])
	#print(fpkm_df.head())
	reducedBlast_df = pd.read_csv(tdict['name']+'_transcript.csv')
	# reducedBlast_df = pd.read_csv('TrinityVT_transcript.csv')
	saccverSet = set(reducedBlast_df['saccver'])
	saccverList = list(saccverSet)
	saccverList.sort()
	# print(saccverList[:5])
	new_df = pd.DataFrame(columns=['qaccver','saccver','FPKM'])
	for sv in saccverList:
	#print(sv)
	temp_df = reducedBlast_df[reducedBlast_df['saccver'] == sv]
	qList = list(temp_df['qaccver'])
	for q in qList:
	f_df = fpkm_df[(fpkm_df['locus'] == q)]
	if len(f_df) > 1:
	print('WARNING MULTIPLE FPKM')
	new_fpkm=list(f_df['FPKM'])
	f = (new_fpkm[0])
	# print(f)
	new_df = new_df.append({'qaccver': q, 'saccver': sv, 'FPKM': f}, ignore_index=True)
	FPKMsum_df = new_df.groupby('saccver')['FPKM'].sum().reset_index()
	+
	FPKMsum_df['Phylotype'] = FPKMsum_df.apply(lambda row: getPhyloNumber(row['saccver']), axis=1)
	FPKMsum_df = FPKMsum_df.sort_values(by=['Phylotype'])
	FPKMsum_df = FPKMsum_df.reset_index(drop=True)
	+
	# print(FPKMsum_df)
	FPKMsum_df.to_csv('FPKM_sum.csv')
	FPKMsum2_df = FPKMsum_df.groupby('Phylotype')['FPKM'].sum().reset_index()
	FPKMsum2_df = FPKMsum2_df.sort_values(by=['Phylotype'])
	+
	# print(FPKMsum2_df)
	FPKMsum2_df.to_csv('FPKM_sum2.csv') # in case more than one entry for a particular phylotype
	return FPKMsum_df, FPKMsum2_df
	+
	+
	+
	def normalisef(f,max):
	return f/max
	+
	def getComposite_sum2(nameList,sum2_dfs):
	# lets get a composite sum2_df from all of the sum2_dfs
	phyList = []
	+
	for i in range(0, len(sum2_dfs)):
	total = sum2_dfs[i]['FPKM'].sum()
	sum2_dfs[i]['w'] = sum2_dfs[i].apply(lambda row: normalisef(row['FPKM'], total), axis=1)
	pSeries = sum2_dfs[i]['Phylotype']
	for p in pSeries:
	phyList.append(p) # get all the phylotypes in this one
	phyList = list(set(phyList))
	phyList.sort()
	composite_sum2_df = pd.DataFrame(phyList, columns=['Phylotype'])
	for i in range(0, len(sum2_dfs)):
	wList = []
	pindf = list(sum2_dfs[i]['Phylotype'])
	# print(pindf)
	for p in phyList:
	if p in pindf:
	df = sum2_dfs[i]
	w = df.loc[df['Phylotype'] == p, 'w'].iloc[0]
	else:
	w = 0
	wList.append(w)
	composite_sum2_df[nameList[i]] = wList
	#print(composite_sum2_df)
	#composite_sum2_df.to_csv('composite.csv')
	return composite_sum2_df
	+
	+
	def doMultiBarChart(tdict, composite_df): #array of multiple sum2_dfs
	labelList = composite_df.columns.tolist()
	sampnum = len(labelList)-1
	# need to arrange bars
	# number of phylotype = len(composite_df)
	#number of bars = (len(labelist)-1) +1 for space
	# ytick needs to ne
	+
	cmap = plt.cm.get_cmap('tab10')
	palette = [cmap(i) for i in range(cmap.N)]
	title = "Legend: Variant Antigen Profile of a $\itTrypanosoma$ $\itvivax$ transcriptomes. " \
	"Phylotype abundance is expressed as phylotype frequencies adjusted " \
	"for combined transcript abundance. " \
	"Data was produced with VAPPER-Variant Antigen Profiler (Silva Pereira et al., 2019)."
	width = 0.6
	ind = np.arange(widthsampnum/2, len(composite_df)width(sampnum+1), width(sampnum+1))
	#print(ind)
	ysize = len(composite_df)*0.4
	+
	fig, ax = plt.subplots(figsize=(10,ysize))
	+
	+
	for s in range(1, len(labelList)):
	ax.barh(ind, composite_df[labelList[s]], width, color=palette[s], label=labelList[s])
	ind = ind + width
	+
	ax.set(yticks=np.arange(width(sampnum+2)/2, len(composite_df)width(sampnum+1), width(sampnum+1)), yticklabels=composite_df['Phylotype']) # , ylim=[(len(labelList)-1) * width - 1, len(composite_df)])
	ax.legend()
	+
	+
	ax.set_ylabel('Phylotype')
	ax.invert_yaxis() # labels read top-to-bottom
	ax.set_xlabel('Weighted Phylotype Frequency')
	+
	# plt.text(-0.3, -0.15, title, va="top", wrap="True")
	#plt.tight_layout()
	+
	plt.subplots_adjust(bottom=0.1, top=0.92, left=0.15, right=0.9)
	ax.set_title(title, x=0, wrap='True',ha='left',)
	+
	plt.savefig(tdict['html_resource'] + tdict['name']+"_phylotypes.png")
	if tdict['pdf'] == 'PDF_Yes':
	plt.savefig(tdict['html_resource'] + tdict['name']+"phylotypes.pdf")
	plt.show()
	pass
	+
	+
	+
	def doBarChart(tdict, sum2_df):
	cmap = plt.cm.get_cmap('tab20')
	palette = [cmap(i) for i in range(cmap.N)]
	title = "Legend: Variant Antigen Profile of a $\itTrypanosoma$ $\itvivax$ transcriptome. " \
	"Phylotype abundance is expressed as phylotype frequencies adjusted " \
	"for combined transcript abundance. " \
	"Data was produced with VAPPER-Variant Antigen Profiler (Silva Pereira et al., 2019)."
	# get a list of phylotype, create equivalent of saccver, get a list of
	maxFPKM = sum2_df['FPKM'].max()
	total = sum2_df['FPKM'].sum()
	+
	sum2_df['Normalised'] = sum2_df.apply(lambda row: normalisef(row['FPKM'], maxFPKM),axis=1)
	sum2_df['Weighted'] = sum2_df.apply(lambda row: normalisef(row['FPKM'], total),axis=1)
	pList = sum2_df['Phylotype']
	phList = []
	for p in pList:
	phList.append(str(p))
	+
	fList = sum2_df['Weighted']
	ysize = len(phList)*0.3
	fig, ax = plt.subplots(figsize=(10,ysize))
	+
	ax.barh(phList, fList, color=palette)
	ax.set_ylabel('Phylotype')
	ax.invert_yaxis() # labels read top-to-bottom
	ax.set_xlabel('Weighted Phylotype Frequency')
	+
	# plt.text(-0.3, -0.15, title, va="top", wrap="True")
	#plt.tight_layout()
	plt.subplots_adjust(bottom=0.1, top=0.9, left=0.15, right=0.9)
	ax.set_title(title, x=0, wrap='True',ha='left',)
	+
	plt.savefig(tdict['html_resource'] + tdict['name']+"_phylotypes.png")
	if tdict['pdf'] == 'PDF_Yes':
	plt.savefig(tdict['html_resource'] + tdict['name']+"phylotypes.pdf")
	# plt.show()
	pass
	+
	# argdict = {'name':2, 'pdfexport': 3, 'refFastq': 4, 'forward': 5, 'reverse': 6, 'html_file': 7, 'html_resource': 8}
	+
	def transcriptomicProcess(args,argdict):
	tdict = {}
	tdict['name'] = args[argdict['name']]
	tdict['refFastq'] = args[argdict['refFastq']]
	tdict['forward'] = args[argdict['forward']]
	tdict['reverse'] = args[argdict['reverse']]
	tdict['vivax_trans_database'] = 'data/vivax/Database/Phylotype_typeseqs.fas'
	tdict['pdf'] = args[argdict['pdfexport']]
	tdict['html_file'] = args[argdict['html_file']]
	tdict['html_resource'] = args[argdict['html_resource']]

	uploadUserReferenceFastq(tdict['refFastq'])
	transcriptMapping(tdict['name'], tdict['refFastq'], tdict['forward'], tdict['reverse']) #uses bowtie
	processSamFiles(tdict['name']) #uses samtools
	transcriptAbundance(tdict['name']) #uses cufflinks -> ?.cuff/.
	transcriptsForBlast(tdict['name'], tdict['refFastq']) #creates name+4blast.fa
	blastContigs(tdict['name'], tdict['html_resource'], 'data/vivax/Database/Phylotype_typeseqs.fas')
	sum_df, sum2_df = combineFPMK(tdict)
	doBarChart(tdict, sum2_df)
	createHTML(tdict, sum_df)
	+
	+
	if __name__ == "__main__":
	exit()
	+
	+

+ + + +

+ +

+ + + +

+ + +

+ + + +

+ +

+ + johnheap/VAPPER-Galaxy + + +

Join GitHub today

+ VAPPER-Galaxy/Tryp_V_T.py +