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