-
Notifications
You must be signed in to change notification settings - Fork 4
/
multiGenomicContext.py
executable file
·535 lines (439 loc) · 18.7 KB
/
multiGenomicContext.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
#!/usr/bin/python
from __future__ import with_statement
# ==============================================================================
# multiGenomicContext
#
# Author: Sandro Valenzuela ([email protected])
#
# Please type "python multiGenomicContext.py -h" for usage help
#
# ==============================================================================
__author__ = 'Sandro Valenzuela ([email protected])'
import sys, os, re, subprocess, csv, glob
from operator import itemgetter
from collections import deque
from optparse import OptionParser
from Bio import SeqIO
def printPlotStep(outfilename,globalA,cleanProcess):
print outfilename
plotstep=open("plotstep.R", 'w')
plotstep.write("""
rm(list=ls())
library(ggplot2)
library(genoPlotR)
library(dplyr)
args<-commandArgs()
outfilename<-args[6]
globalA<-ifelse(toupper(args[7])=="TRUE",TRUE,FALSE)
temp = list.files(pattern="*.DNASEGcsv")
nfiles<-length(temp)
#parse names
gbknames<- lapply(as.list(temp),function(x){strsplit(x = x,split = "[.]DNASEGcsv")[[1]][1]})
gbknames<- lapply(gbknames,function(x){gsub(pattern = "[.]gbff",replacement = "",x = x)})
#read regions of interest
df<-lapply(temp, read.csv, header = F, stringsAsFactors = F)
df<-lapply(df,function(x){
x<-unique(x)
colnames(x)<-c("name", "start", "end" ,"strand" ,"col" ,"lty" ,"lwd" ,"pch" ,"cex", "gene_type","locus_tag","contig")
name<-strsplit(x = x$name,split = "_|-")
x$name<-unlist(lapply(name,function(y){
y<-unique(y) #to avoid duplicated words in the name
halfy<-round(length(y)/2,0)
if(halfy+1>=3){
y<-paste0(paste(y[1:halfy],collapse = " "),"\n",
paste(y[(length(y)-(halfy-1)):length(y)],collapse = " ")
)
}else{
y<-paste(y,collapse = " ")
}
y
}))
x
})
df<-lapply(df,function(x){
x<-x[order(x$contig),]
x<-lapply(unique(x$contig), function(c){ tmp<-subset(x,contig==c)
tmp[order(tmp$start),]
}) %>% bind_rows()
prevContig<-x[1,"contig"]
newx<-list(subset(x, contig == prevContig))
endpos<-max(subset(x, contig == prevContig)[,"end"]) + 30000
for(c in unique(x$contig)[-1]){
tmp<-subset(x, contig == c)
if(nrow(tmp)>=2){
prevEnd<-0
nextStart<-0
for(i in 1:(nrow(tmp)-1)){
distGene<- abs(nextStart - prevEnd)
posdif<-tmp[i,"end"] - tmp[i,"start"]
tmp[i,"start"] <- endpos + distGene
tmp[i,"end"] <- tmp[i,"start"] + posdif
endpos<-tmp[i,"end"]
prevEnd<-tmp[i,"end"]
nextStart<-tmp[i+1,"start"]
}
i<-i+1
distGene<- abs(nextStart - prevEnd)
posdif<-tmp[i,"end"] - tmp[i,"start"]
tmp[i,"start"] <- endpos + distGene
tmp[i,"end"] <- tmp[i,"start"] + posdif
}else{
posdif<-tmp[1,"end"] - tmp[1,"start"]
tmp[1,"start"] <- endpos
tmp[1,"end"] <- tmp[1,"start"] + posdif
}
endpos<-tmp[nrow(tmp),"end"] + 30000
newx[[c]]<-tmp
}
x<-bind_rows(newx)
x
})
annot<-lapply(df,function(x){
annotation(x1=x$start+10,x2=x$end-5,text=x$name,rot=replicate(nrow(x),35))
#annotation(x1=x$start,x2=x$end,text=x$locus_tag,rot=replicate(nrow(x),35))
})
xlims<-lapply(df,function(x){
x<-x[order(x$start),]
lims<-c(ifelse(min(x[1,"start"])-100<=0,0,min(x[,"start"])-100))
for(i in 1:(nrow(x)-1)){
if(x[i+1,"start"]-x[i,"end"] >= 3000){
lims<-c(lims,x[i,"end"]+100,
x[i+1,"start"]-100)
}
}
lims<-c(lims,max(x[,"end"])+50)
})
#set unique colors for genes
uniqnames<-unique(do.call(rbind.data.frame, df)["name"])
uniqnames<-sort(uniqnames[,1])
color = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
colors<-sample(color, length(uniqnames))
df2color<-data.frame(as.matrix(uniqnames),as.matrix(colors))
df2color<-t(df2color)
colnames(df2color)<-df2color[1,]
df2color<- df2color[-1,]
df<-lapply(df,function(x){
x["fill"]<-df2color[x$name]
x["col"]<-"black"
#x["gene_type"]<-"side_blocks"
#tmp<-x["name"]
#x["name"]<-x["locus_tag"]
#x["locus_tag"]<-tmp
x
})
df<-lapply(df,function(x){dna_seg(x)})
if(nfiles>1){
names(df)<-gbknames
}
wformula=as.integer(log(max(sapply(annot,nrow)))*log(max(sapply(annot,nrow)))*2)+max(sapply(xlims, length))
hformula=as.integer(log(nfiles)*nfiles)+1
pdf(file=outfilename, width = wformula, height = hformula)
par(mar=c(0,3,2,3))
plot(c(0,1000), c(0,1000), type="n", axes=FALSE, xlab="", ylab="")
legend("center", legend = gsub("_"," ",c(as.matrix(uniqnames))),ncol = as.integer(length(uniqnames)/20)+1,xpd = NA,
cex = 0.8, bty="n",fill=c(as.matrix(colors)),border = c("white"),title = "Genes")
if(globalA){
#read mauve comparison
mauvebb<-read_mauve_backbone("tmpbb.mauve")
plot_gene_map(dna_segs = mauvebb$dna_segs,dna_seg_label_cex = 0.8,
comparisons = mauvebb$comparisons,
dna_seg_scale = T)
}else{
plot_gene_map(dna_segs = df,dna_seg_label_cex = 0.5,annotation_height = round(2+log(hformula),0),
annotations = annot, xlims = xlims,
scale = F, dna_seg_scale = T,plot_new=T)
}
dev.off()
""")
plotstep.close()
RBIN=which("Rscript")
subprocess.call([RBIN, "plotstep.R", str(outfilename), str(globalA)])
if not cleanProcess:
return None
filenames = glob.glob('*.DNASEGcsv')
for filename in filenames:
os.remove(filename)
os.remove("plotstep.R")
return None
def foundGenomicContext(gene,faafile,upstream,downstream,GCX,dna_segs): #function to search genomic context
#the faa files are with genes in order and formatted >gene|locustag|contig|position
gene_list = [] #to save up and downstream genes
faa_sequences = SeqIO.parse(open(faafile),'fasta')
for proteins in faa_sequences:
name = str(proteins.id)
gene_list.append(name)
#get the position of our gene
gene_position=gene_list.index(gene)
#calculate the total num of genes to print
downstream=downstream+upstream
#backup gene_position to change the color
ourgene_position=gene_position
#save the contigname of out gene
contigname=str(gene_list[gene_position]).split("|")[2]
#check if we are close to the begining of the list
if (gene_position-upstream)<0:
gene_position=0
upstream=upstream-gene_position
else:
gene_position=gene_position-upstream
while gene_position<len(gene_list) and downstream>=0:
#only prints genes in the same contig of our gene
if str(gene_list[gene_position]).split("|")[2] == contigname:
genid=str(gene_list[gene_position]).split("|")[0]
name=str(gene_list[gene_position]).split("|")[1]
contig=str(gene_list[gene_position]).split("|")[2]
pos1=str(gene_list[gene_position]).split("|")[3].split(":")[0]
pos1=str(pos1).replace(">","").replace("<","")
pos2=str(gene_list[gene_position]).split("|")[3].split(":")[1]
pos2=str(pos2).replace(">","").replace("<","")
strand=str(gene_list[gene_position]).split("|")[3]
strand=str(strand).split(":")[2].replace("+","1").replace("-","-1")
if gene_position == ourgene_position:
color="red"
#GCX.write("%s,%s,%s,%s,%s,%s,%s\n" % (faafile,str(genid+" (input)"),contig,name,pos1,pos2,strand))
GCX.write("%s,%s,%s,%s,%s,%s,%s\n" % (faafile,genid,contig,name,pos1,pos2,strand))
else:
color="gray"
GCX.write("%s,%s,%s,%s,%s,%s,%s\n" % (faafile,genid,contig,name,pos1,pos2,strand))
#print name,pos1,pos2,strand,color
if gene_position == ourgene_position:
#dna_segs.write("%s,%s,%s,%s,%s,1,1,8,1,arrows,%s,%s\n" % (str(name+" (input)"), pos1, pos2, strand, color, genid, contig))
dna_segs.write("%s,%s,%s,%s,%s,1,1,8,1,arrows,%s,%s\n" % (name, pos1, pos2, strand, color, genid, contig))
else:
dna_segs.write("%s,%s,%s,%s,%s,1,1,8,1,arrows,%s,%s\n" % (name, pos1, pos2, strand, color, genid, contig))
gene_position=gene_position+1
downstream=downstream-1
return None
def which(program):#function to check if some program exists
def is_exe(fpath):
return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
fpath, fname = os.path.split(program)
if fpath:
if is_exe(program):
return program
else:
for path in os.environ["PATH"].split(os.pathsep):
path = path.strip('"')
exe_file = os.path.join(path, program)
if is_exe(exe_file):
return exe_file
return None
def Makefaa(gbk):
wd=os.getcwd()
gbkname=gbk.replace("/"," ").split()[len(gbk.replace("/"," ").split())-1]
protdict={}
contiggene={}
location={}
location2={}
location3={}
faa= open(str(wd+"/"+gbkname+ ".faa"), 'w')
recs = [rec for rec in SeqIO.parse(gbk, "genbank")]
for rec in recs:
contigname=rec[0:].id
feats = [feat for feat in rec.features if feat.type == "CDS"]
for feat in feats:
if "product" in feat.qualifiers:
cdsname=str(feat.qualifiers["locus_tag"]).replace("'","").replace("[","").replace("]","")
if "gene" in feat.qualifiers:
product=str(feat.qualifiers["gene"]).replace("'","").replace("[","").replace("]","")
else:
product=str(feat.qualifiers["product"]).replace("'","").replace("[","").replace("]","").replace(" ","_").replace(",",".")
locationStart = list()
locationEnd = list()
locationStrand = list()
i=0
if "join" in str(feat.location):
for gensection in str(feat.location).replace("[","").replace("]","").replace("{","").replace("}","").replace("join","").replace(" ","").replace(">","").replace("<","").split(","):
locationStart.append(gensection.split(":")[0])
locationEnd.append(gensection.split(":")[1].split("(")[0])
locationStrand.append(gensection.split(":")[1].split("(")[1].replace(")",""))
i=i+1
else:
featlocation=str(feat.location).replace(">","").replace("<","").replace("[","").replace("]","").replace("(",":").replace(")","")
locationStart.append(featlocation.split(":")[0])
locationEnd.append(featlocation.split(":")[1])
locationStrand.append(featlocation.split(":")[2])
i=i+1
if "translation" in feat.qualifiers:
translation=str(feat.qualifiers["translation"]).replace("'","").replace("[","").replace("]","")
band=0
for seq in protdict.items():
if seq[1] == translation:
band=1
if band==0:
for i in range(0,i):
#print str(cdsname+" || "+product+" || "+contigname+" || "+locationStart[i]+" || "+locationEnd[i]+" || "+locationStrand[i])
faa.write(">%s|%s|%s|%s\n" % (cdsname, product, contigname, str(int(locationStart[i])+1)+":"+locationEnd[i]+":"+locationStrand[i]))
faa.write("%s\n" % (translation))
faa.close()
return str(gbkname + ".faa")
def main():
parser = OptionParser(usage = "Usage: python multiGenomicContext.py -f protein.fasta -g mygbff.gbff [-w if your protein fasta have 2>= proteins]")
#parser.add_option("-g","--global", dest="globalA",help="default:False plot gbk-gbk aligment instead only a region. use only with -l parameter", default=False, action='store_true')
parser.add_option("-f","--proteinFasta",dest="proteinFasta",help="default:none. your protein in fasta format to search on the gbk/gbff")
parser.add_option("-g","--gbklist",dest="gbkList",help="Comma separated gbk, for example: mygbk1.gbk,mygbk2.gbk,mygbk3.gbk")
parser.add_option("-u","--upstreamGenes",dest="Upstream",help="default:5 number of genes to search upstream on the gbks",default=4)
parser.add_option("-d","--downstreamGenes",dest="Downstream",help="default:5 number of genes to search downstream on the gbks",default=4)
parser.add_option("-e","--evalue",dest="evalue",help="default:1e-5 e-value for blastp search",default=1e-5)
parser.add_option("-i","--identity",dest="Identity",help="default:85 range 1-100 % of identity on the blastp alignment to consider the gene exists on the genome",default=85)
parser.add_option("-a","--alignmentLength",dest="alignL",help="default:75 range 1-100 % of aligment length to consider the gene exists on the genome",default=85)
parser.add_option("-b","--blastpBIN", dest="blastpBIN",help="default:/usr/bin/blastp blastp binary path", default="/usr/bin/blastp")
parser.add_option("-m","--progressiveMauveBIN", dest="progressiveMauveBIN",help="default:/usr/bin/progressiveMauve mauve binary path", default="/usr/bin/progressiveMauve")
parser.add_option("-c","--cleanProcessOff", dest="cleanProcess",help="default: True plot this kind of files is complex, so if you turn this flag False, you will have the R file to manipulate the plots", default=True, action='store_false')
parser.add_option("-w","--wholeGenomicInput", dest="wholeGenomicInput",help="default: False by default the script plot one chart/csv per input sequence, with this parameter all proteins input are in the same chart (per gbk)", default=False, action='store_true')
(options,args) = parser.parse_args()
#globalA = options.globalA
Inputprotein = options.proteinFasta
gbkList= options.gbkList
Upstream = int(options.Upstream)
Downstream = int(options.Downstream)
Evalue=str(options.evalue)
Identity=int(options.Identity)
alignL=int(options.alignL)
blastpBIN=options.blastpBIN
mauveBIN=options.progressiveMauveBIN
cleanProcess=options.cleanProcess
wholeGenomicInput=options.wholeGenomicInput
globalA=False
#check variables
if not Inputprotein:
if globalA is not True:
print "No input provided (-f), use -h for help"
sys.exit()
else:
if not os.path.isfile(Inputprotein):
print str("* "+Inputprotein+"doesn't exist, check the file directory")
if globalA:
print "-g/--global is only vaild with -l/--gbklist option"
sys.exit()
if gbkList is None:
print "No gbk list provided (-l), use -h for help"
sys.exit()
else:
gbkList=str(gbkList).split(",")
#searching for blastp
if which(blastpBIN) is None:
print "No blastp found, install it before continue or use --blastpBIN for custom binary path"
sys.exit()
#searching for mauve
if globalA and which(mauveBIN) is None:
print "No mauveAligner found, install it before continue or use progressiveMauveBIN for custom progressiveMauve binary path"
sys.exit()
if Upstream+Downstream >= 433 and globalA is not True:
#max number of colors for R script
print "too much genes for plot, use the option --global for entire sequences"
sys.exit()
RBIN=which("Rscript")
if RBIN == None:
print "No Rscript binary found, install it before continue"
sys.exit()
Inputprotein=os.path.abspath(Inputprotein)
inputProteins = SeqIO.parse(open(Inputprotein),'fasta')
#################################################################################
#get proteins from gbks
print "Making .faa from gbk files"
for i in range(0,len(gbkList)):
if os.path.isfile(gbkList[i]):
gbkList[i]=os.path.abspath(gbkList[i])
else:
print str("* "+gbkList[i]+"doesn't exist")
sys.exit()
gbkfaafiles = [] #create list to save .faa
for gbk in gbkList:
gbk=gbk.rstrip() #delete \n character
name=Makefaa(gbk) #makefaa return the name of .faa (and create the file)
gbkfaafiles.append(name)
#################################################################################
#genome-genome aligment
if globalA:
command=str(mauveBIN+" --output=tmp.mauve --backbone-output=tmpbb.mauve --seed-family --muscle-args='-refine' "+str(" ".join(gbkList)))
subprocess.call(command, shell=True)
printPlotStep(str(name+".pdf"), globalA,cleanProcess)
else:
if wholeGenomicInput:
#walking through the fastas and genes
for faa in gbkfaafiles:
GCX=open(str(faa+".csv"),'w')
GCX.write("source,genId,contig,name,start,end,strand\n")
outname=str(faa).replace(".faa","")
dna_segs=open(str(outname+".DNASEGcsv"),"w")
print "working on "+faa
for fasta in SeqIO.parse(open(Inputprotein),'fasta'):
name, qsequence = str(fasta.id), str(fasta.seq)
#making an individual fasta with the protein
tmp=open('tmp.faa','w')
tmp.write(">%s\n%s\n" % (name,qsequence))
tmp.close()
command=str(blastpBIN+" -query tmp.faa -subject "+str(faa)+" -out tmp.out -evalue "+Evalue+" -outfmt 10")
subprocess.call(command, shell=True)
os.remove("tmp.faa")
#now we check if the results pass the filter to consider the gene "exists" in the genome
if os.path.getsize("tmp.out")>0:
tmp=open("tmp.out","r")
for uniquerow in csv.reader(tmp,delimiter=','):
#uniquerow[0] is our query protein
#uniquerow[1] is the name of protein that match with our query (header of the fasta to be specific)
#uniquerow[2] is identity
#uniquerow[3] is alignment coverage (length)
if uniquerow[2]>=Identity and (float(uniquerow[3])/len(qsequence))>=(alignL/100.0):
#if we are here, so, the protein exist in the gbk, the next step is find the genes up and down stream of the gbk
#call the function
#print(uniquerow)
foundGenomicContext(uniquerow[1],faa,0,0,GCX,dna_segs)
tmp.close()
os.remove("tmp.out")
else:
print "No match found in gbk",str(">"+name),"for",faa
if os.path.isfile("tmp.out"):
os.remove("tmp.out")
GCX.close()
dna_segs.close()
#call plot step
printPlotStep(str("Gcontext"+".pdf"), globalA, cleanProcess)
else:
#walking through the fastas and genes
for fasta in inputProteins:
name, sequence = str(fasta.id), str(fasta.seq)
print "Find",name,"in faa files"
#making an individual fasta with the protein
tmp=open('tmp.faa','w')
tmp.write(">%s\n%s\n" % (name,sequence))
tmp.close()
GCX=open(str(name+".csv"),'w')
GCX.write("source,genId,contig,name,start,end,strand\n")
for faa in gbkfaafiles:
command=str(blastpBIN+" -query tmp.faa -subject "+str(faa)+" -out tmp.out -evalue "+Evalue+" -outfmt 10 -max_target_seqs 1 -max_hsps 1")
subprocess.call(command, shell=True)
#now we check if the results pass the filter to consider the gene "exists" in the genome
if os.path.getsize("tmp.out")>0:
tmp=open("tmp.out","r")
uniquerow=next(csv.reader(tmp))
tmp.close()
os.remove("tmp.out")
#uniquerow[1] is the name of protein that match with our query (header of the fasta to be specific)
#uniquerow[2] is identity
#uniquerow[3] is alignment coverage (length)
if uniquerow[2]>=Identity and (float(uniquerow[3])/len(sequence))>=(alignL/100.0):
#if we are here, so, the protein exist in the gbk, the next step is find the genes up and down stream of the gbk
#call the function
outname=str(faa).replace(".faa","")
dna_segs=open(str(outname+".DNASEGcsv"),"w")
foundGenomicContext(uniquerow[1],faa,Upstream,Downstream,GCX,dna_segs)
dna_segs.close()
else:
print "No match found on gbk",str(">"+name),"for",faa
if os.path.isfile("tmp.out"):
os.remove("tmp.out")
os.remove("tmp.faa")
GCX.close()
#call plot step
printPlotStep(str(name+".pdf"), globalA, cleanProcess)
print "Clean files"
for faa in gbkfaafiles:
os.remove(faa)
if os.path.exists("*.mauve"):
os.remove("*.mauve")
print "Done"
if __name__ == '__main__':
main()
sys.exit()