17
|
1 """
|
|
2 * Copyright 2018 University of Liverpool
|
|
3 * Author: John Heap, Computational Biology Facility, UoL
|
|
4 * Based on original scripts of Sara Silva Pereira, Institute of Infection and Global Health, UoL
|
|
5 *
|
|
6 * Licensed under the Apache License, Version 2.0 (the "License");
|
|
7 * you may not use this file except in compliance with the License.
|
|
8 * You may obtain a copy of the License at
|
|
9 *
|
|
10 * http://www.apache.org/licenses/LICENSE-2.0
|
|
11 *
|
|
12 * Unless required by applicable law or agreed to in writing, software
|
|
13 * distributed under the License is distributed on an "AS IS" BASIS,
|
|
14 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
15 * See the License for the specific language governing permissions and
|
|
16 * limitations under the License.
|
|
17 *
|
|
18 """
|
7
|
19
|
|
20
|
17
|
21 import subprocess
|
|
22 import pandas as pd
|
|
23 import re
|
|
24 import os
|
|
25 import sys
|
|
26 import matplotlib as mpl
|
|
27 mpl.use('Agg')
|
|
28 import matplotlib.pyplot as plt
|
7
|
29
|
17
|
30 pList = ['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15']
|
|
31 quietString = "" #"">> Vap_log.txt 2>&1"
|
|
32 def transcriptMapping(inputname, strain, forwardFN,reverseFN):
|
|
33 #where is our Reference data -
|
|
34 dir_path = os.path.dirname(os.path.realpath(__file__))
|
|
35 refName = dir_path+"/data/Reference/Tc148" #default
|
|
36 if strain == "Tc148":
|
|
37 refName = dir_path+"/data/Reference/Tc148"
|
|
38 if strain == "IL3000":
|
|
39 refName = dir_path+"/data/Reference/IL3000"
|
|
40 #argString = "bowtie2 -x Refe4rence/IL3000 -1 data/"+forwardFN+" -2 data/"+reverseFN+" -S "+inputname+".sam" #>log.txt
|
|
41 #argString = "bowtie2 -x Reference/Tc148 -1 data/"+forwardFN+" -2 data/"+reverseFN+" -S "+inputname+".sam" #>log.txt
|
|
42 argString = "bowtie2 -x "+refName+" -1 "+forwardFN+" -2 "+reverseFN+" -S "+inputname+".sam"+quietString #>log.txt
|
|
43 #print(argString)
|
|
44 returncode = subprocess.call(argString, shell=True)
|
7
|
45
|
17
|
46 def processSamFiles(inputname):
|
|
47 #debug use a mapping sam file we have already found
|
|
48 #dir_path = os.path.dirname(os.path.realpath(__file__))
|
|
49 #bugName = dir_path+"/data/T_Test" #defasult
|
7
|
50
|
17
|
51 cur_path = os.getcwd()
|
|
52 samName = cur_path+"/"+inputname
|
7
|
53
|
17
|
54 #argString = "samtools view -bS "+bugName+" > "+inputname+".bam"
|
|
55 argString = "samtools view -bS "+inputname+".sam > "+samName+".bam"+quietString
|
|
56 #print(argString)
|
|
57 returncode = subprocess.call(argString, shell=True)
|
7
|
58
|
|
59
|
17
|
60 #argString = "samtools sort "+bugName+" -o "+inputname+".sorted"
|
|
61 argString = "samtools sort "+samName+".bam -o "+samName+".sorted"+quietString
|
|
62 #print("argstring = "+argString)
|
|
63 returncode = subprocess.call(argString, shell=True)
|
7
|
64
|
17
|
65 #argString = "samtools index "+bugName+".sorted "+inputname+".sorted.bai"
|
|
66 argString = "samtools index "+samName+".sorted "+samName+".sorted.bai"+quietString
|
|
67 #print("argstring = " + argString)
|
|
68 returncode = subprocess.call(argString, shell=True)
|
7
|
69
|
|
70
|
|
71
|
|
72
|
17
|
73 def transcriptAbundance(inputname, strain):
|
|
74 dir_path = os.path.dirname(os.path.realpath(__file__))
|
|
75 refName = dir_path + "/data/Reference/ORFAnnotation.gtf" # defasult
|
|
76 if strain == "Tc148":
|
|
77 refName = dir_path + "/data/Reference/ORFAnnotation.gtf"
|
|
78 if strain == "IL3000":
|
|
79 refName = dir_path + "/data/Reference/IL3000.gtf"
|
|
80 #argString = "cufflinks -G Reference/IL3000.gtf -o "+inputname+".cuff -u -p 8 "+inputname+".sorted"
|
|
81 #argString = "cufflinks -G Reference/ORFAnnotation.gtf -o "+inputname+".cuff -u -p 8 "+inputname+".sorted"
|
|
82 argString = "cufflinks -q -G "+refName+" -o "+inputname+".cuff -u -p 8 "+inputname+".sorted"+quietString
|
|
83 returncode = subprocess.call(argString, shell = True)
|
7
|
84
|
|
85
|
17
|
86 def convertToFasta(inputName, strain): #equivalent to Sara's awk scripte
|
|
87 dir_path = os.path.dirname(os.path.realpath(__file__))
|
|
88 refName = dir_path + "/data/Reference/ORFAnnotation.gtf" # default
|
|
89 if strain == "Tc148":
|
|
90 refName = dir_path + "/data/Reference/148_prot.fasta"
|
|
91 if strain == "IL3000":
|
|
92 refName = dir_path + "/data/Reference/IL3000_prot.fasta"
|
|
93
|
|
94 cuff_df = pd.read_csv(inputName+".cuff/genes.fpkm_tracking", sep='\t')
|
|
95 cuff_df = cuff_df[(cuff_df['FPKM'] > 0)]
|
|
96 cuff_df.to_csv("cuffTest.csv")
|
|
97 gene_id_List = cuff_df['gene_id'].tolist()
|
|
98
|
|
99 #print(gene_id_List)
|
|
100 #print ("Found from 8880="+str(found))
|
7
|
101
|
17
|
102 # need to load in IL3000_prot.fasta
|
|
103 # for each line with >TcIL3000_1_1940
|
|
104 # search within cuff_df[gene_id] for match
|
|
105 # add it to the outfile. (need to save it as used by hmmer later
|
|
106 number = 0
|
|
107 all = 0
|
|
108 with open(inputName+"_6frame.fas", 'w') as outfile:
|
|
109 ref = open(refName,'r')
|
|
110 #ref = open(r"Reference/IL3000_prot.fasta",'r')
|
|
111 n = 0
|
|
112 line = ref.readline()
|
|
113 while line:
|
|
114 if line[0] == '>':
|
|
115 all = all+1
|
|
116 ln = line[1:] #remove >
|
|
117 ln = ln.rstrip() #remove /n /r etc
|
|
118 #print (ln)
|
|
119 if ln in gene_id_List:
|
|
120 number = number+1
|
|
121 outfile.write(line)
|
|
122 line = ref.readline()
|
|
123 if line:
|
|
124 while line[0] != '>':
|
|
125 outfile.write(line)
|
|
126 line=ref.readline()
|
|
127 if not line:
|
|
128 break;
|
|
129 else:
|
|
130 line = ref.readline()
|
|
131 else:
|
|
132 line =ref.readline()
|
|
133 ref.close()
|
|
134 print(str(len(gene_id_List))+":"+str(number)+" from "+str(all))
|
|
135 return cuff_df
|
7
|
136
|
17
|
137 def HMMerMotifSearch(name, strain, cuff_df):
|
|
138 motifs = ['1', '2a', '2b', '3', '4a', '4b', '4c', '5', '6', '7', '8a', '8b', '9a', '9b',
|
|
139 '9c', '10a', '10b', '11a', '11b', '12', '13a', '13b', '13c', '13d', '14', '15a', '15b', '15c']
|
|
140 dir_path = os.path.dirname(os.path.realpath(__file__))
|
|
141 phylopath = dir_path + "/data/Motifs/Phylotype"
|
|
142 lineCounts = []
|
|
143 compoundList = []
|
|
144 for m in motifs:
|
|
145 argString = "hmmsearch "+phylopath + m + ".hmm " + name + "_6frame.fas > Phy" + m + ".out"
|
|
146 print(argString)
|
|
147 subprocess.call(argString, shell=True)
|
|
148 hmmResult = open("Phy" + m + ".out", 'r')
|
|
149 regex = r"Tc148[0-9]{1,8}"
|
|
150 if strain == "Tc148":
|
|
151 regex = r"Tc148[0-9]{1,8}"
|
|
152 if strain == "IL3000":
|
|
153 regex = r"TcIL3000_[0-9]{1,4}_[0-9]{1,5}"
|
|
154 n = 0
|
|
155 outList = []
|
|
156 for line in hmmResult:
|
|
157 m = re.search(regex, line)
|
|
158 if m:
|
|
159 outList.append(""+m.group())
|
|
160 n += 1
|
|
161 if re.search(r"inclusion", line):
|
|
162 print("inclusion threshold reached")
|
|
163 break
|
|
164 compoundList.append(outList)
|
|
165 lineCounts.append(n)
|
|
166 hmmResult.close()
|
|
167 #print(lineCounts)
|
7
|
168
|
17
|
169 #print(cuff_df)
|
|
170 concatGroups = [1, 2, 1, 3, 1, 1, 1, 2, 3, 2, 2, 1, 4, 1, 3]
|
|
171 countList = []
|
|
172 weightList = []
|
|
173 countIndex = 0
|
|
174 totalCount = 0
|
|
175 totalWeigth = 0
|
|
176 for c in concatGroups:
|
|
177 a = []
|
|
178 weight = []
|
|
179 for n in range(0, c):
|
|
180 a = a + compoundList.pop(0)
|
|
181 t = set(a)
|
|
182 countList.append(len(t))
|
|
183 wa = 0
|
|
184 for w in t:
|
|
185 wt = cuff_df.loc[cuff_df['gene_id'] == w, 'FPKM'].iloc[0]
|
|
186 #print(w)
|
|
187 #print(wt)
|
|
188 wa = wa+wt
|
|
189 weightList.append(wa)
|
|
190 totalWeigth+=wa
|
|
191 totalCount += len(t)
|
|
192 countList.append(totalCount)
|
|
193 weightList.append(totalWeigth)
|
|
194 #print(countList)
|
|
195 #print("--------")
|
|
196 #print(weightList)
|
|
197 #print("--------")
|
|
198 return countList,weightList
|
7
|
199
|
17
|
200 def relativeFrequencyTable(countList, name, htmlresource):
|
|
201 relFreqList = []
|
|
202 c = float(countList[15])
|
|
203 for i in range(0, 15):
|
|
204 relFreqList.append(countList[i] / c)
|
7
|
205
|
17
|
206 data = {'Phylotype': pList, 'Relative Frequency': relFreqList}
|
|
207 relFreq_df = pd.DataFrame(data)
|
|
208 j_fname = htmlresource+ "/" + name + "_t_relative_frequency.csv"
|
|
209 relFreq_df.to_csv(j_fname)
|
|
210 return relFreqList # 0-14 = p1-p15 counts [15] = total counts
|
7
|
211
|
17
|
212
|
|
213 def weightedFrequencyTable(countList, name, htmlresource):
|
|
214 relFreqList = []
|
|
215 c = float(countList[15])
|
|
216 for i in range(0, 15):
|
|
217 relFreqList.append(countList[i] / c)
|
|
218
|
|
219 data = {'Phylotype': pList, 'Weighted Frequency': relFreqList}
|
|
220 relFreq_df = pd.DataFrame(data)
|
|
221 j_fname = htmlresource+ "/" + name + "_t_weighted_frequency.csv"
|
|
222 relFreq_df.to_csv(j_fname)
|
|
223 return relFreqList # 0-14 = p1-p15 counts [15] = total counts
|
7
|
224
|
|
225
|
|
226
|
17
|
227 def createStackedBar(name,freqList,strain,pdf,html_resource):
|
|
228 palette = ["#0000ff", "#6495ed", "#00ffff", "#caff70",
|
|
229 "#228b22", "#528b8b", "#00ff00", "#a52a2a",
|
|
230 "#ff0000", "#ffff00", "#ffa500", "#ff1493",
|
|
231 "#9400d3", "#bebebe", "#000000", "#ff00ff"]
|
7
|
232
|
17
|
233 VAP_148 = [0.072, 0.032, 0.032, 0.004, 0.007,
|
|
234 0.005, 0.202, 0.004, 0.006, 0.014,
|
|
235 0.130, 0.133, 0.054, 0.039, 0.265]
|
7
|
236
|
17
|
237 VAP_IL3000 = [0.073, 0.040, 0.049, 0.018, 0.060,
|
|
238 0.055, 0.054, 0.025, 0.012, 0.060,
|
|
239 0.142, 0.100, 0.061, 0.078, 0.172]
|
|
240 cmap = plt.cm.get_cmap('tab20')
|
|
241 palette = [cmap(i) for i in range(cmap.N)]
|
7
|
242
|
17
|
243 if strain == "Tc148":
|
|
244 VAPtable = VAP_148
|
|
245 VAPname='Tc148\nGenome VAP'
|
|
246 if strain == "IL3000":
|
|
247 VAPtable = VAP_IL3000
|
|
248 VAPname= 'IL3000\nGenome VAP'
|
|
249 width = 0.35 # the width of the bars: can also be len(x) sequence
|
|
250 plots = []
|
|
251 fpos = 0
|
|
252 vpos = 0
|
|
253 for p in range(0, 15):
|
|
254 tp = plt.bar(0, freqList[p], width, color= palette[p], bottom = fpos)
|
|
255 fpos +=freqList[p]
|
|
256
|
|
257 tp = plt.bar(1, VAPtable[p], width, color= palette[p], bottom = vpos)
|
|
258 vpos +=VAPtable[p]
|
7
|
259
|
17
|
260 plots.append(tp)
|
|
261 plt.xticks([0,1],[name,VAPname])
|
|
262 plt.legend(plots[::-1],['p15','p14','p13','p12','p11','p10','p9','p8','p7','p6','p5','p4','p3','p2','p1'])
|
|
263 title = "Figure Legend: The transcriptomic Variant Antigen Profile of $\itTrypanosoma$ $\itcongolense$ estimated as phylotype " \
|
|
264 "proportion adjusted for transcript abundance and the reference genomic Variant Antigen Profile. " \
|
|
265 "\nData was produced with the 'Variant Antigen Profiler' (Silva Pereira et al., 2019)."
|
|
266 #plt.title(title, wrap="True")
|
|
267 #plt.text(-0.2, -0.05, title, va="top", transform=ax.transAxes, wrap="True")
|
|
268 plt.text(-0.3, -0.15, title, va="top", wrap="True")
|
|
269 plt.tight_layout(pad=1.5)
|
|
270 plt.subplots_adjust(bottom = 0.3,top=0.99,left=0.125,right=0.9,hspace=0.2,wspace=0.2)
|
7
|
271
|
17
|
272 plt.savefig(html_resource + "/stackedbar.png")
|
|
273 if pdf == 'PDF_Yes':
|
|
274 plt.savefig(html_resource + "/stackedbar.pdf")
|
|
275 #plt.show()
|
7
|
276
|
|
277
|
17
|
278 def createHTML(name,htmlfn,htmlresource,freqList,weightList):
|
|
279 #assumes imgs are heatmap.png, dheatmap.png, vapPCA.png and already in htmlresource
|
|
280 htmlString = r"<html><title>T.congolense VAP</title><body><div style='text-align:center'><h2><i>Trypanosoma congolense</i> Variant Antigen Profile</h2><h3>"
|
|
281 htmlString += name
|
|
282 htmlString += r"<br>Transcriptomic Analysis</h3></p>"
|
|
283 htmlString += "<p style = 'margin-left:20%; margin-right:20%'>Table Legend: Variant Antigen Profiles of a transcriptome of <i>Trypanosoma congolense</i> estimated as phylotype proportion. " \
|
|
284 "Weighted frequency refers to the phylotype proportion based transcript abundance. " \
|
|
285 "Data was produced with the 'Variant Antigen Profiler' (Silva Pereira et al., 2019).</p> "
|
|
286 htmlString += r"<style> table, th, tr, td {border: 1px solid black; border-collapse: collapse;}</style>"
|
7
|
287
|
17
|
288 htmlString += r"<table style='width:50%;margin-left:25%;text-align:center'><tr><th>Phylotype</th><th>Relative Frequency</th><th>Weighted Frequency</th></tr>"
|
|
289 tabString = ""
|
|
290 # flush out table with correct values
|
|
291 for i in range(0, 15):
|
|
292 f = format(freqList[i], '.4f')
|
|
293 w = format(weightList[i], '.4f')
|
|
294 tabString += "<tr><td>phy" + str(i + 1) + "</td><td>" + f + "</td><td>" + w + "</td></tr>"
|
|
295 htmlString += tabString + "</table><br><br><br><br><br>"
|
|
296 htmlString += r"<p> <h3>Stacked Bar chart of Phylotype Frequency</h3> The 'weighted' relative frequency of each phylotype alongside the VAP of selected strain.</p>"
|
|
297 imgString = r"<img src = 'stackedbar.png' alt='Stacked bar chart of phylotype variation' style='max-width:100%'><br><br>"
|
|
298 htmlString += imgString
|
|
299
|
|
300 # htmlString += r"<p><h3>The Deviation Heat Map and Dendogram</h3>The phylotype variation expressed as the deviation from your sample mean compared to the model dataset</p>"
|
|
301 # imgString = r"<img src = 'dheatmap.png' alt='Deviation Heatmap' style='max-width:100%'><br><br>"
|
|
302 # htmlString += imgString
|
|
303
|
|
304 # htmlString += r"<p><h3>The Variation PCA plot</h3>PCA analysis corresponding to absolute variation. Colour coded according to location</p>"
|
|
305 # imgString = r"<img src = 'vapPCA.png' alt='PCA Analysis' style='max-width:100%'><br><br>"
|
|
306 # htmlString += imgString + r"</div></body></html>"
|
|
307
|
|
308 with open(htmlfn, "w") as htmlfile:
|
|
309 htmlfile.write(htmlString)
|
7
|
310
|
17
|
311 #argdict = {'name':2, 'pdfexport': 3, 'strain': 4, 'forward': 5, 'reverse': 6, 'html_file': 7, 'html_resource': 8}
|
|
312 def transcriptomicProcess(args,dict):
|
|
313 transcriptMapping(args[dict['name']], args[dict['strain']], args[dict['forward']], args[dict['reverse']]) #uses bowtie
|
|
314 processSamFiles(args[dict['name']]) #uses samtools
|
|
315 transcriptAbundance(args[dict['name']],args[dict['strain']]) #uses cufflinks -> ?.cuff/*.*
|
|
316 cuff_df = convertToFasta(args[dict['name']],args[dict['strain']])
|
|
317 countList, weightList = HMMerMotifSearch(args[dict['name']],args[dict['strain']], cuff_df)
|
|
318 relFreqList = relativeFrequencyTable(countList,args[dict['name']],args[dict['html_resource']])
|
|
319 relWeightList = weightedFrequencyTable(weightList,args[dict['name']],args[dict['html_resource']])
|
|
320 createStackedBar(args[dict['name']],relWeightList, args[dict['strain']],args[dict['pdfexport']],args[dict['html_resource']])
|
|
321 createHTML(args[dict['name']],args[dict['html_file']],args[dict['html_resource']], relFreqList, relWeightList)
|
7
|
322
|
17
|
323 if __name__ == "__main__":
|
|
324 #print("Commencing Transcript Mapping")
|
|
325 #transcriptMapping("T_Test", "Transcripts.1","Transcripts.2")
|
|
326 #print("Processimg Sam Files")
|
|
327 #processSamFiles("T_Test")
|
|
328 #print("Assessing Transcript Abundance")
|
|
329 #transcriptAbundance("T_Test")
|
|
330 #print ("Converting to Fasta Subset")
|
|
331 #cuff_df = convertToFasta("T_Test")
|
|
332 #print("Commencing HMMer search")
|
|
333 #countList, weightList = HMMerMotifSearch("T_Test",cuff_df)
|
|
334 #relativeFrequencyTable(countList,'T_Test')
|
|
335 #weightedFrequencyTable(weightList,'T_Test')
|
|
336 relFreqList = [0.111842105,0.059210526,0.026315789,0.013157895,
|
|
337 0.006578947,0.013157895,0.032894737,0.019736842,
|
|
338 0.039473684,0.046052632,0.217105263,0.065789474,
|
|
339 0.151315789,0.059210526,0.138157895]
|
7
|
340
|
17
|
341 relWeightList = [0.07532571,0.05900545,0.009601452,0.042357532,0.01236219,0.001675663,0.04109726,
|
|
342 0.097464248,0.057491666,0.05826875,0.279457473,0.070004772,0.065329007,0.085361298,0.045197529]
|
7
|
343
|
17
|
344 createStackedBar('T_Test',relWeightList, 'Tc148','PDF_Yes','results')
|
|
345 createHTML("t_test","results/t_test.html","results",relFreqList,relWeightList)
|