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- | - | """ | -
| - | * 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(width*sampnum/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() | -
| - | - | -
| - | - | -