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annotate dapars.py @ 1:1b20ba32b4c5 draft

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planemo upload for repository https://github.com/mvdbeek/dapars commit a02ced0b40d8946aa78a58321bd8f9771148391e-dirty
author mvdbeek
date Wed, 28 Oct 2015 05:32:30 -0400
parents bb84ee2f2137
children 47b0044bc7f8
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bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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1 import argparse
bb84ee2f2137 planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
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2 import os
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3 import csv
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4 import numpy as np
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5 from collections import OrderedDict, namedtuple
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6 import filter_utr
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7 import subprocess
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8 from multiprocessing import Pool
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9 import warnings
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10
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11
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12 def parse_args():
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13 """
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14 Returns floating point values except for input files.
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15 My initial approach will not filter anything. (FDR. fold_change, PDUI, Num_least ...)
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16 :param argv:
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17 :return:
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18 """
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19 parser = argparse.ArgumentParser(prog='DaPars', description='Determines the usage of proximal polyA usage')
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20 parser.add_argument("-c", "--control_alignments", nargs="+", required=True,
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21 help="Alignment files in BAM format from control condition")
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22 parser.add_argument("-t", "--treatment_alignments", nargs="+", required=True,
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23 help="Alignment files in BAM format from treatment condition")
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24 parser.add_argument("-u", "--utr_bed_file", required=True, type=file,
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25 help="Bed file describing longest 3UTR positions")
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26 parser.add_argument("-o", "--output_file", required=True, type=argparse.FileType('w'),
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27 help="file containing output")
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28 parser.add_argument("-cpu", required=False, type=int, default=1,
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29 help="Number of CPU cores to use.")
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30 parser.add_argument("-s", "--search_start", required=False, type=int, default=50,
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31 help="Start search for breakpoint n nucleotides downstream of UTR start")
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32 parser.add_argument("-ct", "--coverage_threshold", required=False, type=float, default=20,
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33 help="minimum coverage in each aligment to be considered for determining breakpoints")
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34 parser.add_argument("-b", "--breakpoint_bed", required=False, type=argparse.FileType('w'),
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35 help="Write bedfile with coordinates of breakpoint positions to supplied path.")
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36 parser.add_argument("-v", "--version", action='version', version='%(prog)s 0.1.4')
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37 return parser.parse_args()
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38
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39
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40 class UtrFinder():
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41 """
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42 This seems to be the main caller.
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43 """
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44
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45 def __init__(self, args):
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46 self.control_alignments = [file for file in args.control_alignments]
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47 self.treatment_alignments = [file for file in args.treatment_alignments]
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48 self.n_cpus = args.cpu
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49 self.search_start = args.search_start
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50 self.coverage_threshold = args.coverage_threshold
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51 self.utr = args.utr_bed_file
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52 self.gtf_fields = filter_utr.get_gtf_fields()
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53 self.result_file = args.output_file
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54 self.all_alignments = self.control_alignments + self.treatment_alignments
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55 self.alignment_names = { file: os.path.basename(file) for file in self.all_alignments }
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56 self.num_samples = len(self.all_alignments)
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57 self.utr_dict = self.get_utr_dict(0.2)
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58 self.dump_utr_dict_to_bedfile()
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59 print "Established dictionary of 3\'UTRs"
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60 self.coverage_files = self.run_bedtools_coverage()
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61 self.utr_coverages = self.read_coverage_result()
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62 print "Established dictionary of 3\'UTR coverages"
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63 self.coverage_weights = self.get_coverage_weights()
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64 self.result_tuple = self.get_result_tuple()
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65 self.result_d = self.calculate_apa_ratios()
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66 self.write_results()
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67 if args.breakpoint_bed:
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68 self.bed_output = args.breakpoint_bed
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69 self.write_bed()
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70
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71
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72 def dump_utr_dict_to_bedfile(self):
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73 w = csv.writer(open("tmp_bedfile.bed", "w"), delimiter="\t")
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74 for gene, utr in self.utr_dict.iteritems():
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75 w.writerow([utr["chr"], utr["new_start"]-1, utr["new_end"], gene, ".", utr["strand"]])
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76
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77 def run_bedtools_coverage(self):
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78 """
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79 Use bedtools coverage to generate pileup data for all alignment files for the regions specified in utr_dict.
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80 """
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81 coverage_files = []
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82 cmds = []
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83 for alignment_file in self.all_alignments:
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84 cmd = "sort -k1,1 -k2,2n tmp_bedfile.bed | "
1
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85 cmd = cmd + "~/bin/bedtools coverage -d -s -abam {alignment_file} -b stdin |" \
0
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86 " cut -f 4,7,8 > coverage_file_{alignment_name}".format(
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87 alignment_file = alignment_file, alignment_name= self.alignment_names[alignment_file] )
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88 cmds.append(cmd)
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89 pool = Pool(self.n_cpus)
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90 subprocesses = [subprocess.Popen([cmd], shell=True) for cmd in cmds]
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91 [p.wait() for p in subprocesses]
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92 coverage_files = ["gene_position_coverage_{alignment_name}".format(
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93 alignment_name = self.alignment_names[alignment_file]) for alignment_file in self.all_alignments ]
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94 return coverage_files
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95
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96 def read_coverage_result(self):
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97 """
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98 Read coverages back in and store as dictionary of numpy arrays
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99 """
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100 coverage_dict = { gene: { name: np.zeros(utr_d["new_end"]+1-utr_d["new_start"]) for name in self.alignment_names.itervalues() } for gene, utr_d in self.utr_dict.iteritems() }
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101 for alignment_name in self.alignment_names.itervalues():
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102 with open("coverage_file_{alignment_name}".format(alignment_name = alignment_name)) as coverage_file:
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103 for line in coverage_file:
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104 gene, position, coverage= line.strip().split("\t")
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105 coverage_dict[gene][alignment_name][int(position)-1] = coverage
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106 for utr_d in self.utr_dict.itervalues():
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107 if utr_d["strand"] == "-":
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108 for alignment_name in self.alignment_names.values():
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109 coverage_dict[gene][alignment_name] = coverage_dict[gene][alignment_name][::-1]
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110 return coverage_dict
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111
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112 def get_utr_dict(self, shift):
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113 utr_dict = OrderedDict()
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114 for line in self.utr:
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115 if not line.startswith("#"):
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116 filter_utr.get_feature_dict( line=line, gtf_fields=self.gtf_fields, utr_dict=utr_dict, feature="UTR" )
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117 gene, utr_d = utr_dict.popitem()
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118 utr_d = utr_d[0]
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119 end_shift = int(round(abs(utr_d["start"] - utr_d["end"]) * shift))
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120 if utr_d["strand"] == "+":
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121 utr_d["new_end"] = utr_d["end"] - end_shift
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122 utr_d["new_start"] = utr_d["start"]
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123 else:
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124 utr_d["new_end"] = utr_d["end"]
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125 utr_d["new_start"] = utr_d["start"] + end_shift
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126 if utr_d["new_start"] + 50 < utr_d["new_end"]:
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127 utr_dict[gene] = utr_d
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128 return utr_dict
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129
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130 def get_utr_coverage(self):
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131 """
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132 Returns a dict:
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133 { UTR : [coverage_aligment1, ...]}
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134 """
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135 utr_coverages = {}
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136 for utr, utr_d in self.utr_dict.iteritems():
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137 if utr_d["chr"] in self.available_chromosomes:
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diff changeset
138 if utr_d["strand"] == "+":
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diff changeset
139 is_reverse = False
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140 else:
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diff changeset
141 is_reverse = True
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142 utr_coverage = []
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143 for bam in self.all_alignments:
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144 bp_coverage = get_bp_coverage(bam, utr_d["chr"], utr_d["new_start"], utr_d["new_end"], is_reverse)
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145 utr_coverage.append(bp_coverage)
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146 utr_coverages[utr] = utr_coverage
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147 return utr_coverages
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148
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diff changeset
149 def get_coverage_weights(self):
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150 """
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diff changeset
151 Return weights for normalizing coverage.
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diff changeset
152 utr_coverage is still confusing.
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diff changeset
153 """
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diff changeset
154 coverage_per_alignment = []
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diff changeset
155 for utr in self.utr_coverages.itervalues(): # TODO: be smarter about this.
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diff changeset
156 utr_coverage = []
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diff changeset
157 for vector in utr.itervalues():
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diff changeset
158 utr_coverage.append(np.sum(vector))
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diff changeset
159 coverage_per_alignment.append(utr_coverage)
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160 coverages = np.array([ sum(x) for x in zip(*coverage_per_alignment) ])
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diff changeset
161 coverage_weights = coverages / np.mean(coverages) # TODO: proabably median is better suited?
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diff changeset
162 return coverage_weights
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163
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164 def get_result_tuple(self):
1
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165 static_desc = ["chr", "start", "end", "strand", "gene", "breakpoint",
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166 "breakpoint_type", "control_mean_percent", "treatment_mean_percent" ]
0
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diff changeset
167 samples_desc = []
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168 for statistic in ["coverage_long", "coverage_short", "percent_long"]:
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169 for i, sample in enumerate(self.control_alignments):
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170 samples_desc.append("control_{i}_{statistic}".format(i=i, statistic = statistic))
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171 for i, sample in enumerate(self.treatment_alignments):
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172 samples_desc.append("treatment_{i}_{statistic}".format(i=i, statistic = statistic))
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173 return namedtuple("result", static_desc + samples_desc)
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174
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175 def calculate_apa_ratios(self):
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176 result_d = OrderedDict()
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177 arg_d = {"result_tuple": self.result_tuple,
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178 "coverage_weights":self.coverage_weights,
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179 "num_samples":self.num_samples,
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180 "num_control":len(self.control_alignments),
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181 "num_treatment":len(self.treatment_alignments),
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182 "result_d":result_d}
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183 pool = Pool(self.n_cpus)
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184 tasks = [ (self.utr_coverages[utr], utr, utr_d, self.result_tuple._fields, self.coverage_weights, self.num_samples,
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185 len(self.control_alignments), len(self.treatment_alignments), self.search_start,
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186 self.coverage_threshold) for utr, utr_d in self.utr_dict.iteritems() ]
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187 processed_tasks = [ pool.apply_async(calculate_all_utr, t) for t in tasks]
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188 result = [res.get() for res in processed_tasks]
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189 for res_control, res_treatment in result:
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190 if isinstance(res_control, dict):
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191 t = self.result_tuple(**res_control)
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192 result_d[res_control["gene"]+"_bp_control"] = t
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193 if isinstance(res_treatment, dict):
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194 t = self.result_tuple(**res_treatment)
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195 result_d[res_treatment["gene"]+"_bp_treatment"] = t
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196 return result_d
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197
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198 def write_results(self):
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199 w = csv.writer(self.result_file, delimiter='\t')
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200 header = list(self.result_tuple._fields)
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201 header[0] = "#chr"
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202 w.writerow(header) # field header
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203 w.writerows( self.result_d.values())
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204
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205 def write_bed(self):
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206 w = csv.writer(self.bed_output, delimiter='\t')
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207 bed = [(result.chr, result.breakpoint, result.breakpoint+1, result.gene+"_"+result.breakpoint_type, 0, result.strand) for result in self.result_d.itervalues()]
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208 w.writerows(bed)
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209
1
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210
0
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211 def calculate_all_utr(utr_coverage, utr, utr_d, result_tuple_fields, coverage_weights, num_samples, num_control,
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212 num_treatment, search_start, coverage_threshold):
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213 res_control = dict(zip(result_tuple_fields, result_tuple_fields))
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214 res_treatment = res_control.copy()
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215 if utr_d["strand"] == "+":
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216 is_reverse = False
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217 else:
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218 is_reverse = True
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219 control_breakpoint, \
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220 control_abundance, \
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221 treatment_breakpoint, \
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222 treatment_abundance = optimize_breakpoint(utr_coverage, utr_d["new_start"], utr_d["new_end"], coverage_weights,
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223 search_start, coverage_threshold, num_control)
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224 if control_breakpoint:
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225 breakpoint_to_result(res_control, utr, utr_d, control_breakpoint, "control_breakpoint", control_abundance, is_reverse, num_samples,
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226 num_control, num_treatment)
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227 if treatment_breakpoint:
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228 breakpoint_to_result(res_treatment, utr, utr_d, treatment_breakpoint, "treatment_breakpoint", treatment_abundance, is_reverse,
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229 num_samples, num_control, num_treatment)
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230 return res_control, res_treatment
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231
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232
0
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233
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234
1
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235 def breakpoint_to_result(res, utr, utr_d, breakpoint, breakpoint_type,
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236 abundances, is_reverse, num_samples, num_control, num_treatment):
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237 """
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238 Takes in a result dictionary res and fills the necessary fields
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239 """
1
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240 long_coverage_vector = abundances[0]
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241 short_coverage_vector = abundances[1]
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242 num_non_zero = sum((np.array(long_coverage_vector) + np.array(short_coverage_vector)) > 0) # TODO: This introduces bias
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243 if num_non_zero == num_samples:
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244 percentage_long = []
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245 for i in range(num_samples):
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246 ratio = float(long_coverage_vector[i]) / (long_coverage_vector[i] + short_coverage_vector[i]) # long 3'UTR percentage
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247 percentage_long.append(ratio)
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248 for i in range(num_control):
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249 res["control_{i}_coverage_long".format(i=i)] = float(long_coverage_vector[i])
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250 res["control_{i}_coverage_short".format(i=i)] = float(short_coverage_vector[i])
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251 res["control_{i}_percent_long".format(i=i)] = percentage_long[i]
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252 for k in range(num_treatment):
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253 i = k + num_control
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254 res["treatment_{i}_coverage_long".format(i=k)] = float(long_coverage_vector[i])
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255 res["treatment_{i}_coverage_short".format(i=k)] = float(short_coverage_vector[i])
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256 res["treatment_{i}_percent_long".format(i=k)] = percentage_long[i]
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257 control_mean_percent = np.mean(np.array(percentage_long[:num_control]))
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258 treatment_mean_percent = np.mean(np.array(percentage_long[num_control:]))
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259 res["chr"] = utr_d["chr"]
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260 res["start"] = utr_d["start"]
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261 res["end"] = utr_d["end"]
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262 res["strand"] = utr_d["strand"]
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263 if is_reverse:
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264 breakpoint = utr_d["new_end"] - breakpoint
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265 else:
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266 breakpoint = utr_d["new_start"] + breakpoint
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267 res["breakpoint"] = breakpoint
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268 res["breakpoint_type"] = breakpoint_type
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269 res["control_mean_percent"] = control_mean_percent
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270 res["treatment_mean_percent"] = treatment_mean_percent
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271 res["gene"] = utr
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272
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273
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274 def optimize_breakpoint(utr_coverage, UTR_start, UTR_end, coverage_weigths, search_start, coverage_threshold, num_control):
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275 """
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276 We are searching for a point within the UTR that minimizes the mean squared error, if the coverage vector was divided
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277 at that point. utr_coverage is a list with items corresponding to numpy arrays of coverage for a sample.
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278 """
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279 search_point_end = int(abs((UTR_end - UTR_start)) * 0.1) # TODO: This is 10% of total UTR end. Why?
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280 num_samples = len(utr_coverage)
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281 normalized_utr_coverage = np.array([coverage/ coverage_weigths[i] for i, coverage in enumerate( utr_coverage.values() )])
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282 start_coverage = [np.mean(coverage[0:99]) for coverage in utr_coverage.values()] # filters threshold on mean coverage over first 100 nt
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283 is_above_threshold = sum(np.array(start_coverage) >= coverage_threshold) >= num_samples # This filters on the raw threshold. Why?
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284 is_above_length = UTR_end - UTR_start >= 150
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285 if (is_above_threshold) and (is_above_length):
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286 search_end = UTR_end - UTR_start - search_point_end
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287 breakpoints = range(search_start, search_end + 1)
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288 mse_list = [ estimate_mse(normalized_utr_coverage, bp, num_samples, num_control) for bp in breakpoints ]
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289 if len(mse_list) > 0:
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290 return mse_to_breakpoint(mse_list, normalized_utr_coverage, breakpoints, num_samples)
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291 return False, False, False, False
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292
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293
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294 def mse_to_breakpoint(mse_list, normalized_utr_coverage, breakpoints, num_samples):
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295 """
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296 Take in mse_list with control and treatment mse and return breakpoint and utr abundance
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297 """
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298 mse_control = [mse[0] for mse in mse_list]
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299 mse_treatment = [mse[1] for mse in mse_list]
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300 control_index = mse_control.index(min(mse_control))
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301 treatment_index = mse_treatment.index(min(mse_treatment))
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302 control_breakpoint = breakpoints[control_index]
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303 treatment_breakpoint = breakpoints[treatment_index]
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304 control_abundance = estimate_abundance(normalized_utr_coverage, control_breakpoint, num_samples)
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305 treatment_abundance = estimate_abundance(normalized_utr_coverage, treatment_breakpoint, num_samples)
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306 return control_breakpoint, control_abundance, treatment_breakpoint, treatment_abundance
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307
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308
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309 def estimate_mse(cov, bp, num_samples, num_control):
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310 """
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311 get abundance of long utr vs short utr with breakpoint specifying the position of long and short utr.
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312 """
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313 with warnings.catch_warnings():
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314 warnings.simplefilter("ignore", category=RuntimeWarning)
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315 long_utr_vector = cov[:num_samples, bp:]
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316 short_utr_vector = cov[:num_samples, 0:bp]
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317 mean_long_utr = np.mean(long_utr_vector, 1)
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318 mean_short_utr = np.mean(short_utr_vector, 1)
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319 square_mean_centered_short_utr_vector = (short_utr_vector[:num_samples] - mean_short_utr[:, np.newaxis] )**2
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320 square_mean_centered_long_utr_vector = (long_utr_vector[:num_samples] - mean_long_utr[:, np.newaxis])**2
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321 mse_control = np.mean(np.append(square_mean_centered_long_utr_vector[:num_control], square_mean_centered_short_utr_vector[:num_control]))
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322 mse_treatment = np.mean(np.append(square_mean_centered_long_utr_vector[num_control:], square_mean_centered_short_utr_vector[num_control:]))
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323 return mse_control, mse_treatment
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324
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325
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326 def estimate_abundance(cov, bp, num_samples):
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327 with warnings.catch_warnings():
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328 warnings.simplefilter("ignore", category=RuntimeWarning)
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329 long_utr_vector = cov[:num_samples, bp:]
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330 short_utr_vector = cov[:num_samples, 0:bp]
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331 mean_long_utr = np.mean(long_utr_vector, 1)
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332 mean_short_utr = np.mean(short_utr_vector, 1)
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333 return mean_long_utr, mean_short_utr
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334
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335
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336 if __name__ == '__main__':
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337 args = parse_args()
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338 find_utr = UtrFinder(args)
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339