annotate calc_fitness.py @ 12:ca6b343dd70e draft default tip

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author kaymccoy
date Sun, 11 Dec 2016 17:08:14 -0500
parents d099ed535baf
children
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kaymccoy
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1 # A translation of calc_fitness.pl into python! For analysis of Tn-Seq.
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2 # This script requires BioPython, which in turn has a good number of dependencies (some optional but very helpful).
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3 # How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html
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4 # K. McCoy
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13
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kaymccoy
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14 ##### ARGUMENTS #####
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15
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16 def print_usage():
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17 print "\n" + "You are missing one or more required flags. A complete list of flags accepted by calc_fitness is as follows:" + "\n\n"
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18 print "\033[1m" + "Required" + "\033[0m" + "\n"
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19 print "-ref" + "\t\t" + "The name of the reference genome file, in GenBank format." + "\n"
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20 print "-t1" + "\t\t" + "The name of the bowtie mapfile from time 1." + "\n"
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21 print "-t2" + "\t\t" + "The name of the bowtie mapfile from time 2." + "\n"
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22 print "-out" + "\t\t" + "Name of a file to enter the .csv output." + "\n"
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23 print "\n"
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24 print "\033[1m" + "Optional" + "\033[0m" + "\n"
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25 print "-expansion" + "\t\t" + "Expansion factor (default: 250)" + "\n"
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26 print "-reads1" + "\t\t" + "The number of reads to be used to calculate the correction factor for time 0." + "\n\t\t" + "(default counted from bowtie output)" + "\n"
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27 print "-reads2" + "\t\t" + "The number of reads to be used to calculate the correction factor for time 6." + "\n\t\t" + "(default counted from bowtie output)" + "\n"
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28 print "-cutoff" + "\t\t" + "Discard any positions where the average of counted transcripts at time 0 and time 1 is below this number (default 0)" + "\n"
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29 print "-cutoff2" + "\t\t" + "Discard any positions within the normalization genes where the average of counted transcripts at time 0 and time 1 is below this number (default 10)" + "\n"
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30 print "-strand" + "\t\t" + "Use only the specified strand (+ or -) when counting transcripts (default: both)" + "\n"
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31 print "-normalize" + "\t" + "A file that contains a list of genes that should have a fitness of 1 - used for normalization and bottleneck calculations." + "\n"
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32 print "-b" + "\t" + "Calculate bottleneck value (the percentage of insertions randomly lost) from all genes (rather than only normalization genes)" + "\n"
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33 print "-maxweight" + "\t" + "The maximum weight a transposon gene can have in normalization calculations" + "\n"
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34 print "-multiply" + "\t" + "Multiply all fitness scores by a certain value (e.g., the fitness of a knockout). You should normalize the data." + "\n"
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35 print "-ef" + "\t\t" + "Exclude insertions that occur in the first N amount (%) of gene--becuase may not affect gene function." + "\n"
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36 print "-el" + "\t\t" + "Exclude insertions in the last N amount (%) of the gene--considering truncation may not affect gene function." + "\n"
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37 print "-wig" + "\t\t" + "Create a wiggle file for viewing in a genome browser. Provide a filename." + "\n"
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38 print "-uncol" + "\t\t" + "Use if reads were uncollapsed when mapped." + "\n"
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39 print "\n"
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40
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41 import argparse
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42 parser = argparse.ArgumentParser()
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43 parser.add_argument("-ref", action="store", dest="ref_genome")
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44 parser.add_argument("-t1", action="store", dest="mapfile1")
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45 parser.add_argument("-t2", action="store", dest="mapfile2")
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46 parser.add_argument("-out", action="store", dest="outfile")
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47 parser.add_argument("-out2", action="store", dest="outfile2")
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48 parser.add_argument("-expansion", action="store", dest="expansion_factor")
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49 parser.add_argument("-reads1", action="store", dest="reads1")
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50 parser.add_argument("-reads2", action="store", dest="reads2")
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51 parser.add_argument("-cutoff", action="store", dest="cutoff")
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52 parser.add_argument("-cutoff2", action="store", dest="cutoff2")
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53 parser.add_argument("-strand", action="store", dest="usestrand")
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54 parser.add_argument("-normalize", action="store", dest="normalize")
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55 parser.add_argument("-b", action="store", dest="bottleall")
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56 parser.add_argument("-maxweight", action="store", dest="max_weight")
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57 parser.add_argument("-multiply", action="store", dest="multiply")
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58 parser.add_argument("-ef", action="store", dest="exclude_first")
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59 parser.add_argument("-el", action="store", dest="exclude_last")
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60 parser.add_argument("-wig", action="store", dest="wig")
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61 parser.add_argument("-uncol", action="store", dest="uncol")
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62 arguments = parser.parse_args()
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63
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64 if (not arguments.ref_genome or not arguments.mapfile1 or not arguments.mapfile2 or not arguments.outfile):
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65 print_usage()
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66 quit()
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67
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68 # Sets the default value of the expansion factor to 250, which is a trivial placeholder number.
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69
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70 if (not arguments.expansion_factor):
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71 arguments.expansion_factor = 250
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72
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73 # 75 is similarly trivial
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74
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75 if (not arguments.max_weight):
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76 arguments.max_weight = 75
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77
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78 # Sets the default value of cutoff to 0; cutoff exists to discard positions with a low number of counted transcripts, because fitnesses calculated from them may not be very accurate, by the same reasoning that studies with low sample sizes are innacurate.
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79
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80 if (not arguments.cutoff):
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81 arguments.cutoff = 0
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82
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83 # Sets the default value of cutoff2 to 10; cutoff2 exists to discard positions within normalization genes with a low number of counted transcripts, because fitnesses calculated from them similarly may not be very accurate.
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84 # This only has an effect if it's larger than cutoff, since the normalization step references a list of insertions already affected by cutoff.
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85
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86 if (not arguments.cutoff2):
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87 arguments.cutoff2 = 10
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88
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89 if (not arguments.usestrand):
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90 arguments.usestrand = "both"
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91
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92
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93
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95
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96
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97 ##### PARSING THE REFERENCE GENOME #####
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98
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99 def get_time():
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100 import datetime
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101 return datetime.datetime.now().time()
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102 print "\n" + "Starting: " + str(get_time()) + "\n"
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103
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104 from Bio import SeqIO
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105 import os.path
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106 handle = open(arguments.ref_genome, "rU")
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107 for record in SeqIO.parse(handle, "genbank"):
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108 refname = record.id
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109 features = record.features
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110 handle.close()
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111
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112 # Makes a dictionary out of each feature that's a gene - with its gene name, start location, end location, and strand as keys to their values. Then makes a list out of all those dictionaries for ease of accessing later on.
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113
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114 feature_list = []
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115 for feature in features:
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116 if feature.type == "gene":
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117 gene = feature.qualifiers["locus_tag"]
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118 strand = feature.location.strand
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119 start = float(feature.location.start)
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120 end = float(feature.location.end)
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121
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122 # Exclude_first and exclude_last are used here to exclude whatever percentage of the genes you like from calculations; e.g. a value of 0.1 for exclude_last would exclude the last 10% of all genes!
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123 # This can be useful because insertions at the very start or end of genes often don't actually break its function.
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124
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125 if (arguments.exclude_first):
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126 start += (end - start) * float(arguments.exclude_first)
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127 if (arguments.exclude_last):
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128 end -= (end - start) * float(arguments.exclude_last)
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129 feature_dictionary = {"gene": gene, "start": start, "end": end, "strand": strand}
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130 feature_list.append(feature_dictionary)
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131
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132 print "Done generating feature lookup: " + str(get_time()) + "\n"
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133
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134
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135
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136
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137
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138
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139
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140
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141
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142
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143 ##### PARSING THE MAPFILES #####
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144
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145 with open(arguments.mapfile1) as file:
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146 r1 = file.readlines()
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147 with open(arguments.mapfile2) as file:
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148 r2 = file.readlines()
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149
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150 # When called, goes through each line of the mapfile to find the strand (+/Watson or -/Crick), count, and position of the read. It may be helpful to look at how the mapfiles are formatted to understand how this code finds them.
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151
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152 def read_mapfile(reads):
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153 plus_total = 0
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154 minus_total = 0
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155 plus_counts = {"total": 0, "sites": 0}
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156 minus_counts = {"total": 0, "sites": 0}
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157 for read in reads:
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158 if (arguments.uncol):
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159 strand = read.split()[2]
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160 count = 1
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161 position = float(read.split()[4])
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162 if arguments.usestrand != "both" and strand != arguments.usestrand:
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163 continue
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164 if (strand == "+"):
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165 sequence_length = len(read.split()[5])
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166 position += (sequence_length - 2)
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167 plus_counts["total"] += count
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168 plus_counts["sites"] += 1
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169 if position in plus_counts:
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170 plus_counts[position] += count
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171 else:
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172 plus_counts[position] = count
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173 else:
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174 minus_counts["total"] += count
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175 minus_counts["sites"] += 1
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176 if position in minus_counts:
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177 minus_counts[position] += count
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178 else:
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179 minus_counts[position] = count
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180 else:
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181 if "-" in read.split()[0]:
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182 strand = read.split()[1]
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183 count = float(read.split()[0].split("-")[1])
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184 position = float(read.split()[3])
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185 else:
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186 continue
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187
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188 # If for some reason you want to skip all reads from one of the strands - for example, if you wanted to compare the two strands - that's done here.
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189
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190 if arguments.usestrand != "both" and strand != arguments.usestrand:
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191 continue
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192
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193 # Makes dictionaries for the + & - strands, with each insert position as a key and the number of insertions there as its corresponding value.
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194
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195 if (strand == "+"):
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196 sequence_length = len(read.split()[4])
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197
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198 # The -2 in "(sequence_length -2)" comes from a fake "TA" in the read; see how the libraries are constructed for further on this
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kaymccoy
parents:
diff changeset
199
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kaymccoy
parents:
diff changeset
200 position += (sequence_length - 2)
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kaymccoy
parents:
diff changeset
201 plus_counts["total"] += count
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kaymccoy
parents:
diff changeset
202 plus_counts["sites"] += 1
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kaymccoy
parents:
diff changeset
203 if position in plus_counts:
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kaymccoy
parents:
diff changeset
204 plus_counts[position] += count
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kaymccoy
parents:
diff changeset
205 else:
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kaymccoy
parents:
diff changeset
206 plus_counts[position] = count
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kaymccoy
parents:
diff changeset
207 else:
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kaymccoy
parents:
diff changeset
208 minus_counts["total"] += count
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kaymccoy
parents:
diff changeset
209 minus_counts["sites"] += 1
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kaymccoy
parents:
diff changeset
210 if position in minus_counts:
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kaymccoy
parents:
diff changeset
211 minus_counts[position] += count
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kaymccoy
parents:
diff changeset
212 else:
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kaymccoy
parents:
diff changeset
213 minus_counts[position] = count
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kaymccoy
parents:
diff changeset
214 return (plus_counts, minus_counts)
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kaymccoy
parents:
diff changeset
215
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kaymccoy
parents:
diff changeset
216 # Calls read_mapfile(reads) to parse arguments.reads1 and arguments.reads2 (your reads from t1 and t2).
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kaymccoy
parents:
diff changeset
217
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kaymccoy
parents:
diff changeset
218
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kaymccoy
parents:
diff changeset
219
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kaymccoy
parents:
diff changeset
220
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kaymccoy
parents:
diff changeset
221
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kaymccoy
parents:
diff changeset
222 (plus_ref_1, minus_ref_1) = read_mapfile(r1)
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kaymccoy
parents:
diff changeset
223 print "Read first file: " + str(get_time()) + "\n"
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kaymccoy
parents:
diff changeset
224 (plus_ref_2, minus_ref_2) = read_mapfile(r2)
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kaymccoy
parents:
diff changeset
225 print "Read second file: " + str(get_time()) + "\n"
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kaymccoy
parents:
diff changeset
226
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kaymccoy
parents:
diff changeset
227 # The lines below are just printed for reference. The number of sites is the length of a given dictionary of sites - 1 because its last key, "total", isn't actually a site.
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kaymccoy
parents:
diff changeset
228
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kaymccoy
parents:
diff changeset
229 print "Reads:" + "\n"
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kaymccoy
parents:
diff changeset
230 print "1: + " + str(plus_ref_1["total"]) + " - " + str(minus_ref_1["total"]) + "\n"
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kaymccoy
parents:
diff changeset
231 print "2: + " + str(plus_ref_2["total"]) + " - " + str(minus_ref_2["total"]) + "\n"
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kaymccoy
parents:
diff changeset
232 print "Sites:" + "\n"
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kaymccoy
parents:
diff changeset
233 print "1: + " + str(plus_ref_1["sites"]) + " - " + str(minus_ref_1["sites"]) + "\n"
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kaymccoy
parents:
diff changeset
234 print "2: + " + str(plus_ref_2["sites"]) + " - " + str(minus_ref_2["sites"]) + "\n"
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kaymccoy
parents:
diff changeset
235
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kaymccoy
parents:
diff changeset
236
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kaymccoy
parents:
diff changeset
237
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kaymccoy
parents:
diff changeset
238
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kaymccoy
parents:
diff changeset
239
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kaymccoy
parents:
diff changeset
240
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kaymccoy
parents:
diff changeset
241
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kaymccoy
parents:
diff changeset
242
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kaymccoy
parents:
diff changeset
243
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kaymccoy
parents:
diff changeset
244
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kaymccoy
parents:
diff changeset
245 ##### FITNESS CALCULATIONS #####
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kaymccoy
parents:
diff changeset
246
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kaymccoy
parents:
diff changeset
247 # If reads1 and reads2 weren't specified in the command line, sets them as the total number of reads (found in read_mapfile())
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kaymccoy
parents:
diff changeset
248
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kaymccoy
parents:
diff changeset
249 if not arguments.reads1:
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kaymccoy
parents:
diff changeset
250 arguments.reads1 = plus_ref_1["total"] + minus_ref_1["total"]
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kaymccoy
parents:
diff changeset
251 if not arguments.reads2:
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kaymccoy
parents:
diff changeset
252 arguments.reads2 = plus_ref_2["total"] + minus_ref_2["total"]
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kaymccoy
parents:
diff changeset
253
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kaymccoy
parents:
diff changeset
254 # Calculates the correction factors for reads from t1 and t2; cfactor1 and cfactor2 are the number of reads from t1 and t2 respectively divided by total, which is the average number of reads between the two.
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kaymccoy
parents:
diff changeset
255 # This is used later on to correct for pipetting errors, or any other error that would cause unequal amounts of DNA from t1 and t2 to be sequenced so that an unequal amount of reads is produced
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kaymccoy
parents:
diff changeset
256
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kaymccoy
parents:
diff changeset
257 total = (float(arguments.reads1) + float(arguments.reads2))/2
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kaymccoy
parents:
diff changeset
258 cfactor1 = float(arguments.reads1)/total
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kaymccoy
parents:
diff changeset
259 cfactor2 = float(arguments.reads2)/total
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kaymccoy
parents:
diff changeset
260 print "Cfactor 1: " + str(cfactor1) + "\n"
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kaymccoy
parents:
diff changeset
261 print "Cfactor 2: " + str(cfactor2) + "\n"
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kaymccoy
parents:
diff changeset
262 import math
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kaymccoy
parents:
diff changeset
263 import csv
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kaymccoy
parents:
diff changeset
264 results = [["position", "strand", "count_1", "count_2", "ratio", "mt_freq_t1", "mt_freq_t2", "pop_freq_t1", "pop_freq_t2", "gene", "D", "W", "nW"]]
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kaymccoy
parents:
diff changeset
265 genic = 0
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kaymccoy
parents:
diff changeset
266 total_inserts = 0
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kaymccoy
parents:
diff changeset
267 with open(arguments.ref_genome, "r") as file:
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kaymccoy
parents:
diff changeset
268 firstline = file.readline()
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kaymccoy
parents:
diff changeset
269 genomelength = firstline.split()[2]
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kaymccoy
parents:
diff changeset
270 i = 0
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kaymccoy
parents:
diff changeset
271 while i < float(genomelength):
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kaymccoy
parents:
diff changeset
272
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kaymccoy
parents:
diff changeset
273 # At each possible location for an insertion in the genome, counts the number of actual insertions at t1 and which strand(s) the corresponding reads came from.
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kaymccoy
parents:
diff changeset
274
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kaymccoy
parents:
diff changeset
275 c1 = 0
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kaymccoy
parents:
diff changeset
276 if i in plus_ref_1:
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kaymccoy
parents:
diff changeset
277 c1 = float(plus_ref_1[i])
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kaymccoy
parents:
diff changeset
278 strand = "+/"
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kaymccoy
parents:
diff changeset
279 if i in minus_ref_1:
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kaymccoy
parents:
diff changeset
280 c1 += float(minus_ref_1[i])
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kaymccoy
parents:
diff changeset
281 strand = "b/"
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kaymccoy
parents:
diff changeset
282 elif i in minus_ref_1:
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kaymccoy
parents:
diff changeset
283 c1 = float(minus_ref_1[i])
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kaymccoy
parents:
diff changeset
284 strand = "-/"
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kaymccoy
parents:
diff changeset
285
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kaymccoy
parents:
diff changeset
286 # If there were no insertions at a certain location at t1 just continues to the next location; there can't be any comparison to make between t1 and t2 if there are no t1 insertions!
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kaymccoy
parents:
diff changeset
287
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kaymccoy
parents:
diff changeset
288 else:
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kaymccoy
parents:
diff changeset
289 i += 1
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kaymccoy
parents:
diff changeset
290 continue
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kaymccoy
parents:
diff changeset
291
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kaymccoy
parents:
diff changeset
292 # At each location where there was an insertion at t1, counts the number of insertions at t2 and which strand(s) the corresponding reads came from.
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kaymccoy
parents:
diff changeset
293
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kaymccoy
parents:
diff changeset
294 c2 = 0
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kaymccoy
parents:
diff changeset
295 if i in plus_ref_2:
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kaymccoy
parents:
diff changeset
296 c2 = float(plus_ref_2[i])
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kaymccoy
parents:
diff changeset
297 if i in minus_ref_2:
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kaymccoy
parents:
diff changeset
298 c2 += float(minus_ref_2[i])
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kaymccoy
parents:
diff changeset
299 strand += "b"
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kaymccoy
parents:
diff changeset
300 else:
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kaymccoy
parents:
diff changeset
301 strand += "+"
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kaymccoy
parents:
diff changeset
302 elif i in minus_ref_2:
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kaymccoy
parents:
diff changeset
303 c2 = float(minus_ref_2[i])
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kaymccoy
parents:
diff changeset
304 strand += "-"
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kaymccoy
parents:
diff changeset
305
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kaymccoy
parents:
diff changeset
306 # Corrects with cfactor1 and cfactor2
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kaymccoy
parents:
diff changeset
307
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kaymccoy
parents:
diff changeset
308 c1 /= cfactor1
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kaymccoy
parents:
diff changeset
309 if c2 != 0:
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kaymccoy
parents:
diff changeset
310 c2 /= cfactor2
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kaymccoy
parents:
diff changeset
311 ratio = c2/c1
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kaymccoy
parents:
diff changeset
312 else:
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kaymccoy
parents:
diff changeset
313 c2 = 0
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kaymccoy
parents:
diff changeset
314 ratio = 0
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kaymccoy
parents:
diff changeset
315
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kaymccoy
parents:
diff changeset
316 # Passes by all insertions with a number of reads smaller than the cutoff, as they may lead to inaccurate fitness calculations.
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kaymccoy
parents:
diff changeset
317
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kaymccoy
parents:
diff changeset
318 if (c1 + c2)/2 < float(arguments.cutoff):
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kaymccoy
parents:
diff changeset
319 i+= 1
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kaymccoy
parents:
diff changeset
320 continue
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kaymccoy
parents:
diff changeset
321
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kaymccoy
parents:
diff changeset
322 # Calculates each insertion's frequency within the populations at t1 and t2.
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kaymccoy
parents:
diff changeset
323
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kaymccoy
parents:
diff changeset
324 mt_freq_t1 = c1/total
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kaymccoy
parents:
diff changeset
325 mt_freq_t2 = c2/total
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kaymccoy
parents:
diff changeset
326 pop_freq_t1 = 1 - mt_freq_t1
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kaymccoy
parents:
diff changeset
327 pop_freq_t2 = 1 - mt_freq_t2
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kaymccoy
parents:
diff changeset
328
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kaymccoy
parents:
diff changeset
329 # Calculates each insertion's fitness! This is from the fitness equation log((frequency of mutation @ time 2 / frequency of mutation @ time 1)*expansion factor)/log((frequency of population without the mutation @ time 2 / frequency of population without the mutation @ time 1)*expansion factor)
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kaymccoy
parents:
diff changeset
330
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kaymccoy
parents:
diff changeset
331 w = 0
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kaymccoy
parents:
diff changeset
332 if mt_freq_t2 != 0:
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kaymccoy
parents:
diff changeset
333 top_w = math.log(mt_freq_t2*(float(arguments.expansion_factor)/mt_freq_t1))
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kaymccoy
parents:
diff changeset
334 bot_w = math.log(pop_freq_t2*(float(arguments.expansion_factor)/pop_freq_t1))
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kaymccoy
parents:
diff changeset
335 w = top_w/bot_w
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kaymccoy
parents:
diff changeset
336
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kaymccoy
parents:
diff changeset
337 # Checks which gene locus the insertion falls within, and records that.
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kaymccoy
parents:
diff changeset
338
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kaymccoy
parents:
diff changeset
339 gene = ''
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kaymccoy
parents:
diff changeset
340 for feature_dictionary in feature_list:
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kaymccoy
parents:
diff changeset
341 if feature_dictionary["start"] <= i and feature_dictionary["end"] >= i:
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kaymccoy
parents:
diff changeset
342 gene = "".join(feature_dictionary["gene"])
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kaymccoy
parents:
diff changeset
343 genic += 1
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kaymccoy
parents:
diff changeset
344 break
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kaymccoy
parents:
diff changeset
345 total_inserts += 1
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kaymccoy
parents:
diff changeset
346
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kaymccoy
parents:
diff changeset
347 # Writes all relevant information on each insertion and its fitness to a cvs file: the location of the insertion, its strand, c1, c2, etc. (the variable names are self-explanatiory)
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kaymccoy
parents:
diff changeset
348 # w is written twice, because the second w will be normalized if normalization is called for, thus becoming nW.
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kaymccoy
parents:
diff changeset
349
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kaymccoy
parents:
diff changeset
350 row = [i, strand, c1, c2, ratio, mt_freq_t1, mt_freq_t2, pop_freq_t1, pop_freq_t2, gene, arguments.expansion_factor, w, w]
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kaymccoy
parents:
diff changeset
351 results.append(row)
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kaymccoy
parents:
diff changeset
352 i += 1
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kaymccoy
parents:
diff changeset
353 with open(arguments.outfile, "wb") as csvfile:
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kaymccoy
parents:
diff changeset
354 writer = csv.writer(csvfile)
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kaymccoy
parents:
diff changeset
355 writer.writerows(results)
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kaymccoy
parents:
diff changeset
356
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kaymccoy
parents:
diff changeset
357 print "Done comparing mapfiles " + str(get_time()) + "\n"
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kaymccoy
parents:
diff changeset
358 print "Genic: " + str(genic) + "\n"
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kaymccoy
parents:
diff changeset
359 print "Total: " + str(total_inserts) + "\n"
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kaymccoy
parents:
diff changeset
360
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
361
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
362
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
363
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
364
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
365
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
366
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
367
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
368
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kaymccoy
parents:
diff changeset
369
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kaymccoy
parents:
diff changeset
370 ##### BOTTLENECK VALUE CALCULATION #####
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kaymccoy
parents:
diff changeset
371
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kaymccoy
parents:
diff changeset
372 #the bottleneck value is calculated here if done from all genes - otherwise it's done in the normalization section if only taken from normalization genes
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kaymccoy
parents:
diff changeset
373
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kaymccoy
parents:
diff changeset
374 if (arguments.bottleall):
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kaymccoy
parents:
diff changeset
375 overall_blank_count = 0
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kaymccoy
parents:
diff changeset
376 for list in results:
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kaymccoy
parents:
diff changeset
377 if (list[2] != 0 and list[3] == 0):
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kaymccoy
parents:
diff changeset
378 overall_blank_count += 1
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kaymccoy
parents:
diff changeset
379 overall_original_count = len(results)
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kaymccoy
parents:
diff changeset
380
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kaymccoy
parents:
diff changeset
381 pc_blank_normals = float(overall_blank_count) / float(overall_original_count)
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kaymccoy
parents:
diff changeset
382
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kaymccoy
parents:
diff changeset
383 with open(arguments.outfile2, "w") as f:
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kaymccoy
parents:
diff changeset
384 f.write("bottleneck_value: " + str(pc_blank_normals) + "\n" + "total: " + str(total) + "\n" + "refname: " + refname)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
385
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
386
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
387
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
388
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
389
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
390
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
391
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
392
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
393
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
394
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
395 ##### NORMALIZATION #####
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kaymccoy
parents:
diff changeset
396
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kaymccoy
parents:
diff changeset
397 # If making a WIG file is requested in the arguments, starts a string to be added to and then written to the WIG file with a typical WIG file header.
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kaymccoy
parents:
diff changeset
398 # The header is just in a typical WIG file format; if you'd like to look into this more UCSC has notes on formatting WIG files on their site.
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kaymccoy
parents:
diff changeset
399
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kaymccoy
parents:
diff changeset
400 if (arguments.wig):
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kaymccoy
parents:
diff changeset
401 wigstring = "track type=wiggle_0 name=" + arguments.wig + "\n" + "variableStep chrom=" + refname + "\n"
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kaymccoy
parents:
diff changeset
402
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kaymccoy
parents:
diff changeset
403 # Takes normalization genes (which should all be predicted or known to have fitness values of exactly 1.0, like transposons for example) and uses them to normalize the fitnesses of all insertion locations
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kaymccoy
parents:
diff changeset
404
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kaymccoy
parents:
diff changeset
405 if (arguments.normalize):
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kaymccoy
parents:
diff changeset
406 with open(arguments.normalize) as file:
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kaymccoy
parents:
diff changeset
407 transposon_genes = file.read().splitlines()
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
408 print "Normalize genes loaded" + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
409 blank_ws = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
410 sum = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
411 count = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
412 weights = []
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
413 scores = []
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
414 for list in results:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
415 if list[9] != '' and list[9] in transposon_genes:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
416 c1 = list[2]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
417 c2 = list[3]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
418 score = list[11]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
419 avg = (c1 + c2)/2
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
420
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
421 # Skips over those insertion locations with too few insertions - their fitness values are less accurate because they're based on such small insertion numbers.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
422
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
423 if float(c1) >= float(arguments.cutoff2):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
424
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
425 # Sets a max weight, to prevent insertion location scores with huge weights from unbalancing the normalization.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
426
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
427 if (avg >= float(arguments.max_weight)):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
428 avg = float(arguments.max_weight)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
429
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
430 # Tallies how many w values are 0 within the blank_ws value; you might get many transposon genes with a w value of 0 if a bottleneck occurs, for example, which is especially common with in vivo experiments. This is used later by aggregate.py
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
431 # For example, when studying a nasal infection in a mouse model, what bacteria "sticks" and is able to survive and what bacteria is swallowed and killed or otherwise flushed out tends to be a matter of chance not fitness; all mutants with an insertion in a specific transposon gene could be flushed out by chance!
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
432
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
433 if score == 0:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
434 blank_ws += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
435
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
436 sum += score
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
437 count += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
438 weights.append(avg)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
439 scores.append(score)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
440
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
441 print str(list[9]) + " " + str(score) + " " + str(c1)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
442
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
443 # Counts and removes all "blank" fitness values of normalization genes - those that = 0 - because they most likely don't really have a fitness value of 0, and you just happened to not get any reads from that location at t2.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
444
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
445 blank_count = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
446 original_count = len(scores)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
447 curr_count = original_count
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
448 i = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
449 while i < curr_count:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
450 w_value = scores[i]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
451 if w_value == 0:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
452 blank_count += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
453 weights.pop(i)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
454 scores.pop(i)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
455 i -= 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
456 curr_count = len(scores)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
457 i += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
458
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
459 # If no normalization genes can pass the cutoff, normalization cannot occur, so this ends the script advises the user to try again and lower cutoff and/or cutoff2.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
460
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
461 if len(scores) == 0:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
462 print 'ERROR: The normalization genes do not have enough reads to pass cutoff and/or cutoff2; please lower one or both of those arguments.' + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
463 quit()
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
464
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
465 pc_blank_normals = float(blank_count) / float(original_count)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
466 print "# blank out of " + str(original_count) + ": " + str(pc_blank_normals) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
467 if (not arguments.bottleall):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
468 with open(arguments.outfile2, "w") as f:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
469 f.write("bottleneck_value: " + str(pc_blank_normals) + "\n" + "total: " + str(total) + "\n" + "refname: " + refname)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
470
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
471 average = sum / count
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
472 i = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
473 weighted_sum = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
474 weight_sum = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
475 while i < len(weights):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
476 weighted_sum += weights[i]*scores[i]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
477 weight_sum += weights[i]
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
478 i += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
479 weighted_average = weighted_sum/weight_sum
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
480
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
481 print "Normalization step:" + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
482 print "Regular average: " + str(average) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
483 print "Weighted Average: " + str(weighted_average) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
484 print "Total Insertions: " + str(count) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
485
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
486 old_ws = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
487 new_ws = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
488 wcount = 0
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
489
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
490 for list in results:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
491 if list[11] == 'W':
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
492 continue
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
493 new_w = float(list[11])/weighted_average
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
494
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
495 # Sometimes you want to multiply all the fitness values by a constant; this does that.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
496 # For example you might multiply all the values by a constant for a genetic interaction screen - where Tn-Seq is performed as usual except there's one background knockout all the mutants share.
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
497
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
498 if arguments.multiply:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
499 new_w *= float(arguments.multiply)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
500
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
501 if float(list[11]) > 0:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
502 old_ws += float(list[11])
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
503 new_ws += new_w
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
504 wcount += 1
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
505
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
506 list[12] = new_w
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
507
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
508 if (arguments.wig):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
509 wigstring += str(list[0]) + " " + str(new_w) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
510
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
511 old_w_mean = old_ws / wcount
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
512 new_w_mean = new_ws / wcount
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
513 print "Old W Average: " + str(old_w_mean) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
514 print "New W Average: " + str(new_w_mean) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
515
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
516 with open(arguments.outfile, "wb") as csvfile:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
517 writer = csv.writer(csvfile)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
518 writer.writerows(results)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
519
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
520 if (arguments.wig):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
521 if (arguments.normalize):
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
522 with open(arguments.wig, "wb") as wigfile:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
523 wigfile.write(wigstring)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
524 else:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
525 for list in results:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
526 wigstring += str(list[0]) + " " + str(list[11]) + "\n"
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
527 with open(arguments.wig, "wb") as wigfile:
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
528 wigfile.write(wigstring)
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
529
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
530
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
531
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
532
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
533
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
534
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
535
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
536
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
537
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
538
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
539
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
540
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
541
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
542
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
543
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
544
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
545
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
546
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
547
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
548
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
549
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
550
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
551
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
552
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
553
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
554
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
555
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
556
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
557
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
558
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
559
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
560
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
561
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
562
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
563
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
564
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
565
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
566
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
567
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
568
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
569
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
570
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
571
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
572
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
573
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
574
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
575
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
576
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
577
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
578
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
579
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
580
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
581
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
582
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
583
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
584
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
585
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
586
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
587
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
588
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
589
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
590
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
591
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
592
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
593
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
594
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
595
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
596
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
597
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
598
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
599
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
600
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
601
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
602
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
603
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
604
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
605
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
606
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
607
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
608
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
609
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
610
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
611
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
612
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
613
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
614
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
615
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
616
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
617
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
618
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
619
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
620
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
621
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
622
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
623
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
624
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
625
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
626
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
627
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
628
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
629
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
630 # `````````````
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
631 # `````````````
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
632 # ``@@@@@@@@@``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
633 # ``@@@@@@@@@```
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
634 # ``@@@@@@@@@``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
635 # ``@@@@@@@@@``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
636 # ``@@@@@@@@@``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
637 # ``@@@@@@@@@``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
638 # ```@@@@@@@@#``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
639 # ```@@@@@@@@#``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
640 # ```@@@@@@@@+``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
641 # ```@@@@@@@@'``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
642 # ```@@@@@@@@;``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
643 # ```@@@@@@@@;``
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
644 # ```@@@@@@@@:``
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
diff changeset
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
diff changeset
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kaymccoy
parents:
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
parents:
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kaymccoy
parents:
diff changeset
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
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kaymccoy
parents:
diff changeset
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kaymccoy
parents:
diff changeset
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kaymccoy
parents:
diff changeset
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
parents:
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kaymccoy
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kaymccoy
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diff changeset
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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kaymccoy
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
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d099ed535baf Uploaded
kaymccoy
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diff changeset
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d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
776 #
d099ed535baf Uploaded
kaymccoy
parents:
diff changeset
777 #