annotate vsnp_get_snps.py @ 14:44b84d32883c draft default tip

"planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 7f5ebaad2e650c53a8980e8567fe9943f9ccabb6"
author greg
date Mon, 15 Nov 2021 23:14:34 +0000
parents be5875f29ea4
children
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1 #!/usr/bin/env python
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2
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3 # Collect quality parsimonious SNPs from vcf files
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4 # and output alignment files in fasta format.
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5
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6 import argparse
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7 import multiprocessing
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8 import os
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9 import queue
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10 import shutil
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11 import sys
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12 import time
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13 from collections import OrderedDict
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14 from datetime import datetime
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15
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16 import pandas
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17 import vcf
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18
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19
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20 def get_time_stamp():
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21 return datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H-%M-%S')
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22
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23
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24 def set_num_cpus(num_files, processes):
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25 num_cpus = int(multiprocessing.cpu_count())
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26 if num_files < num_cpus and num_files < processes:
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27 return num_files
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28 if num_cpus < processes:
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29 half_cpus = int(num_cpus / 2)
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30 if num_files < half_cpus:
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31 return num_files
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32 return half_cpus
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33 return processes
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35
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36 def setup_all_vcfs(vcf_files, vcf_dirs):
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37 # Create the all_vcfs directory and link
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38 # all input vcf files into it for processing.
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39 all_vcfs_dir = 'all_vcf'
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40 os.makedirs(all_vcfs_dir)
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41 vcf_dirs.append(all_vcfs_dir)
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42 for vcf_file in vcf_files:
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43 file_name_base = os.path.basename(vcf_file)
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44 dst_file = os.path.join(all_vcfs_dir, file_name_base)
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45 os.symlink(vcf_file, dst_file)
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46 return vcf_dirs
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47
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48
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49 class SnpFinder:
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50
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51 def __init__(self, num_files, dbkey, input_excel, all_isolates, ac, min_mq, quality_score_n_threshold, min_quality_score, input_vcf_dir, output_json_avg_mq_dir, output_json_snps_dir, output_snps_dir, output_summary):
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52 # Allele count
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53 self.ac = ac
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54 # Create a group that will contain all isolates.
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55 self.all_isolates = all_isolates
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56 # Evolving positions dictionary.
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57 self.all_positions = None
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58 # Isolate groups.
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59 self.groups = []
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60 # Excel file for grouping.
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61 self.input_excel = input_excel
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62 # Directory of input zero coverage vcf files.
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63 self.input_vcf_dir = input_vcf_dir
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64 # Minimum map quality value.
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65 self.min_mq = min_mq
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66 # Minimum quality score value.
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67 self.min_quality_score = min_quality_score
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68 # Number of input zero coverage vcf files.
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69 self.num_files = num_files
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70 # Output directory for json average mq files.
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71 self.output_json_avg_mq_dir = output_json_avg_mq_dir
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72 # Output directory for json snps files.
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73 self.output_json_snps_dir = output_json_snps_dir
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74 # Output directory for snps files.
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75 self.output_snps_dir = output_snps_dir
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76 # Quality score N threshold value.
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77 self.quality_score_n_threshold = quality_score_n_threshold
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78 self.dbkey = dbkey
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79 self.start_time = get_time_stamp()
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80 self.summary_str = ""
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81 self.timer_start = datetime.now()
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82 self.initiate_summary(output_summary)
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83
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84 def append_to_summary(self, html_str):
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85 # Append a string to the html summary output file.
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86 self.summary_str = "%s%s" % (self.summary_str, html_str)
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87
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88 def bin_input_files(self, filename, samples_groups_dict, defining_snps, inverted_defining_snps, found_positions, found_positions_mix):
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89 # Categorize input files into closely related
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90 # isolate groups based on discovered SNPs, and
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91 # return a group dictionary.
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92 sample_groups_list = []
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93 table_name = self.get_sample_name(filename)
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94 defining_snp = False
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95 # Absolute positions in set union of two lists.
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96 for abs_position in list(defining_snps.keys() & (found_positions.keys() | found_positions_mix.keys())):
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97 group = defining_snps[abs_position]
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98 sample_groups_list.append(group)
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99 self.check_add_group(group)
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100 if len(list(defining_snps.keys() & found_positions_mix.keys())) > 0:
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101 table_name = self.get_sample_name(filename)
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102 table_name = '%s<font color="red">[[MIXED]]</font>' % table_name
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103 self.copy_file(filename, group)
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104 defining_snp = True
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105 if not set(inverted_defining_snps.keys()).intersection(found_positions.keys() | found_positions_mix.keys()):
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106 for abs_position in list(inverted_defining_snps.keys()):
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107 group = inverted_defining_snps[abs_position]
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108 sample_groups_list.append(group)
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109 self.check_add_group(group)
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110 self.copy_file(filename, group)
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111 defining_snp = True
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112 if defining_snp:
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113 samples_groups_dict[table_name] = sorted(sample_groups_list)
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114 else:
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115 samples_groups_dict[table_name] = ['<font color="red">No defining SNP</font>']
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116 return samples_groups_dict
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117
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118 def check_add_group(self, group):
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119 # Add a group if it is npt already in the list.
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120 if group not in self.groups:
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121 self.groups.append(group)
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122
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123 def copy_file(self, filename, dir):
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124 if not os.path.exists(dir):
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125 os.makedirs(dir)
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126 shutil.copy(filename, dir)
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127
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128 def decide_snps(self, filename):
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129 # Find the SNPs in a vcf file to produce a pandas data
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130 # frame and a dictionary containing sample map qualities.
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131 positions_dict = self.all_positions
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132 sample_map_qualities = {}
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133 # Eliminate the path.
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134 file_name_base = self.get_sample_name(filename)
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135 vcf_reader = vcf.Reader(open(filename, 'r'))
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136 sample_dict = {}
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137 for record in vcf_reader:
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138 alt = str(record.ALT[0])
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139 record_position = "%s:%s" % (str(record.CHROM), str(record.POS))
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140 if record_position in positions_dict:
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141 if alt == "None":
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142 sample_dict.update({record_position: "-"})
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143 else:
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144 # On rare occassions MQM gets called "NaN", thus passing
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145 # a string when a number is expected when calculating average.
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146 mq_val = self.get_mq_val(record.INFO, filename)
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147 if str(mq_val).lower() not in ["nan"]:
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148 sample_map_qualities.update({record_position: mq_val})
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149 if len(alt) == 1:
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150 qual_val = self.val_as_int(record.QUAL)
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151 ac = record.INFO['AC'][0]
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152 ref = str(record.REF[0])
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153 if ac == 2 and qual_val > self.quality_score_n_threshold:
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154 # Add the SNP to a group.
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155 sample_dict.update({record_position: alt})
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156 elif ac == 1 and qual_val > self.quality_score_n_threshold:
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157 # The position is ambiguous.
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158 alt_ref = "%s%s" % (alt, ref)
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159 if alt_ref == "AG":
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160 sample_dict.update({record_position: "R"})
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161 elif alt_ref == "CT":
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162 sample_dict.update({record_position: "Y"})
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163 elif alt_ref == "GC":
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164 sample_dict.update({record_position: "S"})
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165 elif alt_ref == "AT":
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166 sample_dict.update({record_position: "W"})
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167 elif alt_ref == "GT":
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168 sample_dict.update({record_position: "K"})
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169 elif alt_ref == "AC":
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170 sample_dict.update({record_position: "M"})
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171 elif alt_ref == "GA":
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172 sample_dict.update({record_position: "R"})
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173 elif alt_ref == "TC":
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174 sample_dict.update({record_position: "Y"})
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175 elif alt_ref == "CG":
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176 sample_dict.update({record_position: "S"})
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177 elif alt_ref == "TA":
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178 sample_dict.update({record_position: "W"})
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179 elif alt_ref == "TG":
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180 sample_dict.update({record_position: "K"})
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181 elif alt_ref == "CA":
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182 sample_dict.update({record_position: "M"})
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183 else:
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parents:
diff changeset
184 sample_dict.update({record_position: "N"})
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parents:
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185 # Poor calls
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parents:
diff changeset
186 elif qual_val <= 50:
3
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parents: 0
diff changeset
187 # Call the reference allele.
0
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parents:
diff changeset
188 # Do not coerce record.REF[0] to a string!
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parents:
diff changeset
189 sample_dict.update({record_position: record.REF[0]})
3
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greg
parents: 0
diff changeset
190 elif qual_val <= self.quality_score_n_threshold:
0
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parents:
diff changeset
191 sample_dict.update({record_position: "N"})
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parents:
diff changeset
192 else:
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parents:
diff changeset
193 # Insurance -- Will still report on a possible
3
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parents: 0
diff changeset
194 # SNP even if missed with above statements.
0
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parents:
diff changeset
195 # Do not coerce record.REF[0] to a string!
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parents:
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196 sample_dict.update({record_position: record.REF[0]})
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parents:
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197 # Merge dictionaries and order
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parents:
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198 merge_dict = {}
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parents:
diff changeset
199 merge_dict.update(positions_dict)
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parents:
diff changeset
200 merge_dict.update(sample_dict)
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parents:
diff changeset
201 sample_df = pandas.DataFrame(merge_dict, index=[file_name_base])
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
202 return sample_df, file_name_base, sample_map_qualities
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
203
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parents:
diff changeset
204 def df_to_fasta(self, parsimonious_df, group):
3
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parents: 0
diff changeset
205 # Generate SNP alignment file from
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parents: 0
diff changeset
206 # the parsimonious_df data frame.
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parents: 0
diff changeset
207 snps_file = os.path.join(self.output_snps_dir, "%s.fasta" % group)
0
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parents:
diff changeset
208 test_duplicates = []
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parents:
diff changeset
209 has_sequence_data = False
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parents:
diff changeset
210 for index, row in parsimonious_df.iterrows():
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parents:
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211 for pos in row:
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parents:
diff changeset
212 if len(pos) > 0:
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parents:
diff changeset
213 has_sequence_data = True
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parents:
diff changeset
214 break
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parents:
diff changeset
215 if has_sequence_data:
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parents:
diff changeset
216 with open(snps_file, 'w') as fh:
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parents:
diff changeset
217 for index, row in parsimonious_df.iterrows():
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parents:
diff changeset
218 test_duplicates.append(row.name)
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parents:
diff changeset
219 if test_duplicates.count(row.name) < 2:
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parents:
diff changeset
220 print(f'>{row.name}', file=fh)
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greg
parents:
diff changeset
221 for pos in row:
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
222 print(pos, end='', file=fh)
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
223 print("", file=fh)
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
224 return has_sequence_data
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
225
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
226 def find_initial_positions(self, filename):
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parents:
diff changeset
227 # Find SNP positions in a vcf file.
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
228 found_positions = {}
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parents:
diff changeset
229 found_positions_mix = {}
3
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parents: 0
diff changeset
230 vcf_reader = vcf.Reader(open(filename, 'r'))
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
231 for record in vcf_reader:
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parents: 0
diff changeset
232 qual_val = self.val_as_int(record.QUAL)
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
233 chrom = record.CHROM
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parents: 0
diff changeset
234 position = record.POS
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parents: 0
diff changeset
235 absolute_position = "%s:%s" % (str(chrom), str(position))
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
236 alt = str(record.ALT[0])
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parents: 0
diff changeset
237 if alt != "None":
14285a94fb13 Uploaded
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parents: 0
diff changeset
238 mq_val = self.get_mq_val(record.INFO, filename)
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
239 ac = record.INFO['AC'][0]
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parents: 0
diff changeset
240 if ac == self.ac and len(record.REF) == 1 and qual_val > self.min_quality_score and mq_val > self.min_mq:
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parents: 0
diff changeset
241 found_positions.update({absolute_position: record.REF})
14285a94fb13 Uploaded
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parents: 0
diff changeset
242 if ac == 1 and len(record.REF) == 1 and qual_val > self.min_quality_score and mq_val > self.min_mq:
14285a94fb13 Uploaded
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parents: 0
diff changeset
243 found_positions_mix.update({absolute_position: record.REF})
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parents: 0
diff changeset
244 return found_positions, found_positions_mix
0
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
245
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
246 def gather_and_filter(self, prefilter_df, mq_averages, group_dir):
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
247 # Group a data frame of SNPs.
3
14285a94fb13 Uploaded
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parents: 0
diff changeset
248 if self.input_excel is None:
0
ee4ef1fc23c6 Uploaded
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parents:
diff changeset
249 filtered_all_df = prefilter_df
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
250 sheet_names = None
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
251 else:
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parents:
diff changeset
252 # Filter positions to be removed from all.
3
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parents: 0
diff changeset
253 xl = pandas.ExcelFile(self.input_excel)
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
254 sheet_names = xl.sheet_names
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
255 # Use the first column to filter "all" postions.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
256 exclusion_list_all = self.get_position_list(sheet_names, 0)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
257 exclusion_list_group = self.get_position_list(sheet_names, group_dir)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
258 exclusion_list = exclusion_list_all + exclusion_list_group
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
259 # Filters for all applied.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
260 filtered_all_df = prefilter_df.drop(columns=exclusion_list, errors='ignore')
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
261 json_snps_file = os.path.join(self.output_json_snps_dir, "%s.json" % group_dir)
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
262 parsimonious_df = self.get_parsimonious_df(filtered_all_df)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
263 samples_number, columns = parsimonious_df.shape
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
264 if samples_number >= 4:
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
265 # Sufficient samples have been found
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
266 # to build a phylogenetic tree.
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
267 has_sequence_data = self.df_to_fasta(parsimonious_df, group_dir)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
268 if has_sequence_data:
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
269 json_avg_mq_file = os.path.join(self.output_json_avg_mq_dir, "%s.json" % group_dir)
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
270 mq_averages.to_json(json_avg_mq_file, orient='split')
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
271 parsimonious_df.to_json(json_snps_file, orient='split')
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
272 else:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
273 msg = "<br/>No sequence data"
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
274 if group_dir is not None:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
275 msg = "%s for group: %s" % (msg, group_dir)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
276 self.append_to_summary("%s<br/>\n" % msg)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
277 else:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
278 msg = "<br/>Too few samples to build tree"
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
279 if group_dir is not None:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
280 msg = "%s for group: %s" % (msg, group_dir)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
281 self.append_to_summary("%s<br/>\n" % msg)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
282
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
283 def get_sample_name(self, file_path):
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
284 # Return the sample part of a file name.
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
285 base_file_name = os.path.basename(file_path)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
286 if base_file_name.find(".") > 0:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
287 # Eliminate the extension.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
288 return os.path.splitext(base_file_name)[0]
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
289 return base_file_name
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
290
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
291 def get_mq_val(self, record_info, filename):
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
292 # Get the MQ (gatk) or MQM (freebayes) value
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
293 # from the record.INFO component of the vcf file.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
294 try:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
295 mq_val = record_info['MQM']
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
296 return self.return_val(mq_val)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
297 except Exception:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
298 try:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
299 mq_val = record_info['MQ']
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
300 return self.return_val(mq_val)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
301 except Exception:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
302 msg = "Invalid or unsupported vcf header %s in file: %s\n" % (str(record_info), filename)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
303 sys.exit(msg)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
304
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
305 def get_parsimonious_df(self, filtered_all_df):
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
306 # Get the parsimonious SNPs data frame
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
307 # from a data frame of filtered SNPs.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
308 try:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
309 ref_series = filtered_all_df.loc['root']
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
310 # In all_vcf root needs to be removed.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
311 filtered_all_df = filtered_all_df.drop(['root'])
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
312 except KeyError:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
313 pass
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
314 parsimony = filtered_all_df.loc[:, (filtered_all_df != filtered_all_df.iloc[0]).any()]
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
315 parsimony_positions = list(parsimony)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
316 parse_df = filtered_all_df[parsimony_positions]
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
317 ref_df = ref_series.to_frame()
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
318 ref_df = ref_df.T
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
319 parsimonious_df = pandas.concat([parse_df, ref_df], join='inner')
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
320 return parsimonious_df
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
321
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
322 def get_position_list(self, sheet_names, group):
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
323 # Get a list of positions defined by an excel file.
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
324 exclusion_list = []
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
325 try:
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
326 filter_to_all = pandas.read_excel(self.input_excel, header=1, usecols=[group])
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
327 for value in filter_to_all.values:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
328 value = str(value[0])
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
329 if "-" not in value.split(":")[-1]:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
330 exclusion_list.append(value)
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
331 elif "-" in value:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
332 try:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
333 chrom, sequence_range = value.split(":")
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
334 except Exception as e:
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
335 sys.exit(str(e))
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
336 value = sequence_range.split("-")
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
337 for position in range(int(value[0].replace(',', '')), int(value[1].replace(',', '')) + 1):
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
338 exclusion_list.append(chrom + ":" + str(position))
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
339 return exclusion_list
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
340 except ValueError:
3
14285a94fb13 Uploaded
greg
parents: 0
diff changeset
341 return []
0
ee4ef1fc23c6 Uploaded
greg
parents:
diff changeset
342
9
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
343 def get_snps(self, task_queue, timeout):
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
344 while True:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
345 try:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
346 group_dir = task_queue.get(block=True, timeout=timeout)
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
347 except queue.Empty:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
348 break
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
349 # Parse all vcf files to accumulate
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
350 # the SNPs into a data frame.
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
351 positions_dict = {}
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
352 group_files = []
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
353 for file_name in os.listdir(os.path.abspath(group_dir)):
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
354 file_path = os.path.abspath(os.path.join(group_dir, file_name))
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
355 group_files.append(file_path)
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
356 for file_name in group_files:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
357 found_positions, found_positions_mix = self.find_initial_positions(file_name)
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
358 positions_dict.update(found_positions)
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
diff changeset
359 # Order before adding to file to match
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360 # with ordering of individual samples.
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361 # all_positions is abs_pos:REF
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362 self.all_positions = OrderedDict(sorted(positions_dict.items()))
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363 ref_positions_df = pandas.DataFrame(self.all_positions, index=['root'])
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364 all_map_qualities = {}
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365 df_list = []
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366 for file_name in group_files:
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367 sample_df, file_name_base, sample_map_qualities = self.decide_snps(file_name)
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368 df_list.append(sample_df)
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369 all_map_qualities.update({file_name_base: sample_map_qualities})
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370 all_sample_df = pandas.concat(df_list)
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371 # All positions have now been selected for each sample,
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372 # so select parisomony informative SNPs. This removes
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373 # columns where all fields are the same.
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374 # Add reference to top row.
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375 prefilter_df = pandas.concat([ref_positions_df, all_sample_df], join='inner')
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376 all_mq_df = pandas.DataFrame.from_dict(all_map_qualities)
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377 mq_averages = all_mq_df.mean(axis=1).astype(int)
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378 self.gather_and_filter(prefilter_df, mq_averages, group_dir)
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379 task_queue.task_done()
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380
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381 def group_vcfs(self, vcf_files):
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382 # Parse an excel file to produce a
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383 # grouping dictionary for SNPs.
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384 xl = pandas.ExcelFile(self.input_excel)
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385 sheet_names = xl.sheet_names
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386 ws = pandas.read_excel(self.input_excel, sheet_name=sheet_names[0])
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387 defining_snps = ws.iloc[0]
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388 defsnp_iterator = iter(defining_snps.iteritems())
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389 next(defsnp_iterator)
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390 defining_snps = {}
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391 inverted_defining_snps = {}
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392 for abs_pos, group in defsnp_iterator:
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393 if '!' in abs_pos:
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394 inverted_defining_snps[abs_pos.replace('!', '')] = group
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395 else:
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396 defining_snps[abs_pos] = group
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397 samples_groups_dict = {}
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398 for vcf_file in vcf_files:
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399 found_positions, found_positions_mix = self.find_initial_positions(vcf_file)
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400 samples_groups_dict = self.bin_input_files(vcf_file, samples_groups_dict, defining_snps, inverted_defining_snps, found_positions, found_positions_mix)
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401 # Output summary grouping table.
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402 self.append_to_summary('<br/>')
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403 self.append_to_summary('<b>Groupings with %d listed:</b><br/>\n' % len(samples_groups_dict))
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404 self.append_to_summary('<table cellpadding="5" cellspaging="5" border="1">\n')
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405 for key, value in samples_groups_dict.items():
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406 self.append_to_summary('<tr align="left"><th>Sample Name</th>\n')
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407 self.append_to_summary('<td>%s</td>' % key)
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408 for group in value:
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409 self.append_to_summary('<td>%s</td>\n' % group)
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410 self.append_to_summary('</tr>\n')
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411 self.append_to_summary('</table><br/>\n')
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412
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413 def initiate_summary(self, output_summary):
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414 # Output summary file handle.
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415 self.append_to_summary('<html>\n')
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416 self.append_to_summary('<head></head>\n')
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417 self.append_to_summary('<body style=\"font-size:12px;">')
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418 self.append_to_summary("<b>Time started:</b> %s<br/>" % get_time_stamp())
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419 self.append_to_summary("<b>Number of VCF inputs:</b> %d<br/>" % self.num_files)
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420 self.append_to_summary("<b>Reference:</b> %s<br/>" % self.dbkey)
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421 self.append_to_summary("<b>All isolates:</b> %s<br/>" % str(self.all_isolates))
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422
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423 def return_val(self, val, index=0):
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424 # Handle element and single-element list values.
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425 if isinstance(val, list):
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426 return val[index]
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427 return val
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428
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429 def val_as_int(self, val):
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430 # Handle integer value conversion.
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431 try:
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432 return int(val)
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433 except TypeError:
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434 # val is likely None here.
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435 return 0
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436
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437
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438 if __name__ == '__main__':
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439
0
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440 parser = argparse.ArgumentParser()
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441
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442 parser.add_argument('--ac', action='store', dest='ac', type=int, help='Allele count value'),
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443 parser.add_argument('--all_isolates', action='store_true', dest='all_isolates', help='Create table with all isolates'),
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444 parser.add_argument('--input_excel', action='store', dest='input_excel', required=False, default=None, help='Optional Excel filter file'),
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445 parser.add_argument('--input_vcf_dir', action='store', dest='input_vcf_dir', help='Input vcf directory'),
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446 parser.add_argument('--min_mq', action='store', dest='min_mq', type=int, help='Minimum map quality value'),
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447 parser.add_argument('--min_quality_score', action='store', dest='min_quality_score', type=int, help='Minimum quality score value'),
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448 parser.add_argument('--output_json_avg_mq_dir', action='store', dest='output_json_avg_mq_dir', help='Output json average mq directory'),
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449 parser.add_argument('--output_json_snps_dir', action='store', dest='output_json_snps_dir', help='Output json snps directory'),
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450 parser.add_argument('--output_snps_dir', action='store', dest='output_snps_dir', help='Output snps directory'),
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451 parser.add_argument('--output_summary', action='store', dest='output_summary', help='Output summary html file'),
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452 parser.add_argument('--processes', action='store', dest='processes', type=int, help='Configured processes for job'),
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453 parser.add_argument('--quality_score_n_threshold', action='store', dest='quality_score_n_threshold', type=int, help='Minimum quality score N value for alleles'),
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454 parser.add_argument('--dbkey', action='store', dest='dbkey', help='Galaxy genome build dbkey'),
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455
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456 args = parser.parse_args()
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457
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458 # Build the list of all input zero coverage vcf
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459 # files, both the samples and the "database".
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460 vcf_files = []
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461 for file_name in os.listdir(args.input_vcf_dir):
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462 file_path = os.path.abspath(os.path.join(args.input_vcf_dir, file_name))
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463 vcf_files.append(file_path)
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464
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465 multiprocessing.set_start_method('spawn')
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466 queue1 = multiprocessing.JoinableQueue()
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467 num_files = len(vcf_files)
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468 cpus = set_num_cpus(num_files, args.processes)
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469 # Set a timeout for get()s in the queue.
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470 timeout = 0.05
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471
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472 # Initialize the snp_finder object.
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473 snp_finder = SnpFinder(num_files, args.dbkey, args.input_excel, args.all_isolates, args.ac, args.min_mq, args.quality_score_n_threshold, args.min_quality_score, args.input_vcf_dir, args.output_json_avg_mq_dir, args.output_json_snps_dir, args.output_snps_dir, args.output_summary)
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474
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475 # Define and make the set of directories into which the input_zc_vcf
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476 # files will be placed. Selected input values (e.g., the use of
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477 # an Excel file for grouping and filtering, creating a group with
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478 # all isolates) are used to define the directories.
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479 vcf_dirs = []
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480 if args.input_excel is None:
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481 vcf_dirs = setup_all_vcfs(vcf_files, vcf_dirs)
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482 else:
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483 if args.all_isolates:
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484 vcf_dirs = setup_all_vcfs(vcf_files, vcf_dirs)
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485 # Parse the Excel file to detemine groups for filtering.
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486 snp_finder.group_vcfs(vcf_files)
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487 # Append the list of group directories created by
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488 # the above call to the set of directories containing
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489 # vcf files for analysis.
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490 group_dirs = [d for d in os.listdir(os.getcwd()) if os.path.isdir(d) and d in snp_finder.groups]
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491 vcf_dirs.extend(group_dirs)
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492
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493 # Populate the queue for job splitting.
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494 for vcf_dir in vcf_dirs:
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495 queue1.put(vcf_dir)
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496
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497 # Complete the get_snps task.
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498 processes = [multiprocessing.Process(target=snp_finder.get_snps, args=(queue1, timeout, )) for _ in range(cpus)]
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
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499 for p in processes:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
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500 p.start()
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
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501 for p in processes:
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
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502 p.join()
0fe292b20b9d "planemo upload for repository https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_get_snps commit 3b7fef2d17fec96647345e89c774d4af417d23d7"
greg
parents: 8
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503 queue1.join()
0
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504
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505 # Finish summary log.
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506 snp_finder.append_to_summary("<br/><b>Time finished:</b> %s<br/>\n" % get_time_stamp())
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507 total_run_time = datetime.now() - snp_finder.timer_start
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508 snp_finder.append_to_summary("<br/><b>Total run time:</b> %s<br/>\n" % str(total_run_time))
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509 snp_finder.append_to_summary('</body>\n</html>\n')
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510 with open(args.output_summary, "w") as fh:
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511 fh.write("%s" % snp_finder.summary_str)