Mercurial > repos > drosofff > pirna_signatures

comparison piRNAsignature.py @ 2:8d4ca527888b draft

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author drosofff
date Mon, 23 Jun 2014 04:10:17 -0400
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1:d1b99ef50f79 2:8d4ca527888b
1 #!/usr/bin/python
2 # script for computing overlap signatures from a bowtie output
3 # Christophe Antoniewski <drosofff@gmail.com>
4 # Usage piRNAsignature.py <1:input> <2:format of input> <3:minsize query> <4:maxsize query> <5:minsize target> <6:maxsize target>
5 # <7:minscope> <8:maxscope> <9:output> <10:bowtie index> <11:procedure option> <12: graph (global or lattice)>
6 # <13: R code>
7
8 import sys, subprocess
9 from smRtools import *
10 from collections import defaultdict # test whether it is required
11
12 if sys.argv[11] == "--extract_index":
13 if sys.argv[2] == "tabular":
14 Genome = HandleSmRNAwindows (sys.argv[1],"tabular",sys.argv[10],"bowtieIndex")
15 elif sys.argv[2] == "sam":
16 Genome = HandleSmRNAwindows (sys.argv[1],"sam",sys.argv[10],"bowtieIndex")
17 else:
18 Genome = HandleSmRNAwindows (sys.argv[1],"bam",sys.argv[10],"bowtieIndex")
19 else:
20 if sys.argv[2] == "tabular":
21 Genome = HandleSmRNAwindows (sys.argv[1],"tabular",sys.argv[10],"fastaSource")
22 elif sys.argv[2] == "sam":
23 Genome = HandleSmRNAwindows (sys.argv[1],"sam",sys.argv[10],"fastaSource")
24 else:
25 Genome = HandleSmRNAwindows (sys.argv[1],"bam",sys.argv[10],"fastaSource")
26 # this decisional tree may be simplified if sam and bam inputs are treated the same way by pysam
27
28 # replace objDic by Genome.instanceDict or... objDic = Genome.instanceDict
29 objDic = Genome.instanceDict
30
31 minquery = int(sys.argv[3])
32 maxquery = int(sys.argv[4])
33 mintarget = int(sys.argv[5])
34 maxtarget = int(sys.argv[6])
35 minscope = int(sys.argv[7])
36 maxscope = int(sys.argv[8]) + 1
37 general_frequency_table = dict ([(i,0) for i in range(minscope,maxscope)])
38 general_percent_table = dict ([(i,0) for i in range(minscope,maxscope)])
39 OUT = open (sys.argv[9], "w")
40
41 if sys.argv[12] == "global":
42 ###### for normalized summing of local_percent_table(s)
43 readcount_dic = {}
44 Total_read_in_objDic = 0
45 for item in objDic:
46 readcount_dic[item] = objDic[item].readcount(minquery, maxquery)
47 Total_read_in_objDic += readcount_dic[item]
48 ######
49 for x in (objDic):
50 local_frequency_table = objDic[x].signature( minquery, maxquery, mintarget, maxtarget, range(minscope,maxscope) )
51 local_percent_table = objDic[x].hannon_signature( minquery, maxquery, mintarget, maxtarget, range(minscope,maxscope) )
52 try:
53 for overlap in local_frequency_table.keys():
54 general_frequency_table[overlap] = general_frequency_table.get(overlap, 0) + local_frequency_table[overlap]
55 except:
56 pass
57 try:
58 for overlap in local_percent_table.keys():
59 general_percent_table[overlap] = general_percent_table.get(overlap, 0) + (1./Total_read_in_objDic*readcount_dic[x]*local_percent_table[overlap])
60 except:
61 pass
62 print >> OUT, "overlap\tnum of pairs\tprobability"
63 for classe in sorted(general_frequency_table):
64 print >> OUT, "%i\t%i\t%f" % (classe, general_frequency_table[classe], general_percent_table[classe])
65
66 else:
67 print >> OUT, "overlap\tnum of pairs\tprobability\titem"
68 for x in (objDic):
69 local_frequency_table = objDic[x].signature( minquery, maxquery, mintarget, maxtarget, range(minscope,maxscope) )
70 local_percent_table = objDic[x].hannon_signature( minquery, maxquery, mintarget, maxtarget, range(minscope,maxscope) )
71 for classe in range(minscope,maxscope):
72 print >> OUT, "%i\t%i\t%f\t%s" % (classe, local_frequency_table[classe], local_percent_table[classe], x)
73
74 OUT.close()
75
76 ## Run the R script that is defined in the xml using the Rscript binary provided with R.
77 R_command="Rscript "+ sys.argv[13]
78 process = subprocess.Popen(R_command.split())