Mercurial > repos > fubar > jbrowse2

annotate gff3_rebase.py @ 25:172acf9c7a53 draft

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planemo upload for repository https://github.com/usegalaxy-eu/temporary-tools/tree/master/jbrowse2 commit 50ecae3ea703a1b04501cc44f0d1fa6d714a6cc9
author fubar
date Sat, 03 Feb 2024 22:07:55 +0000
parents 39b717d934a8
children b1260bca5fdc
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d78175596286 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit cd77dffaad652cfb75b98bde5231beaa6d63cd5b-dirty
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1 #!/usr/bin/env python
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2 import argparse
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3 import copy
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4 import logging
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5 import sys
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6
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7 from BCBio import GFF
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8 from Bio.SeqFeature import FeatureLocation
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9
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10 logging.basicConfig(level=logging.INFO)
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11 log = logging.getLogger(__name__)
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12
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13 __author__ = "Eric Rasche"
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14 __version__ = "0.4.0"
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15 __maintainer__ = "Eric Rasche"
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16 __email__ = "esr@tamu.edu"
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17
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18
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19 def feature_lambda(feature_list, test, test_kwargs, subfeatures=True):
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20 """Recursively search through features, testing each with a test function, yielding matches.
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21
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22 GFF3 is a hierachical data structure, so we need to be able to recursively
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23 search through features. E.g. if you're looking for a feature with
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24 ID='bob.42', you can't just do a simple list comprehension with a test
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25 case. You don't know how deeply burried bob.42 will be in the feature tree. This is where feature_lambda steps in.
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26
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27 :type feature_list: list
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28 :param feature_list: an iterable of features
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29
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30 :type test: function reference
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31 :param test: a closure with the method signature (feature, **kwargs) where
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32 the kwargs are those passed in the next argument. This
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33 function should return True or False, True if the feature is
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34 to be yielded as part of the main feature_lambda function, or
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35 False if it is to be ignored. This function CAN mutate the
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36 features passed to it (think "apply").
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37
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38 :type test_kwargs: dictionary
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39 :param test_kwargs: kwargs to pass to your closure when it is called.
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40
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41 :type subfeatures: boolean
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42 :param subfeatures: when a feature is matched, should just that feature be
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43 yielded to the caller, or should the entire sub_feature
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44 tree for that feature be included? subfeatures=True is
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45 useful in cases such as searching for a gene feature,
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46 and wanting to know what RBS/Shine_Dalgarno_sequences
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47 are in the sub_feature tree (which can be accomplished
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48 with two feature_lambda calls). subfeatures=False is
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49 useful in cases when you want to process (and possibly
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50 return) the entire feature tree, such as applying a
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51 qualifier to every single feature.
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52
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53 :rtype: yielded list
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54 :return: Yields a list of matching features.
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55 """
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56 # Either the top level set of [features] or the subfeature attribute
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57 for feature in feature_list:
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58 if test(feature, **test_kwargs):
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59 if not subfeatures:
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60 feature_copy = copy.deepcopy(feature)
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61 feature_copy.sub_features = []
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62 yield feature_copy
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63 else:
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64 yield feature
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65
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66 if hasattr(feature, "sub_features"):
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67 for x in feature_lambda(
17
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68 feature.sub_features,
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69 test,
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70 test_kwargs,
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71 subfeatures=subfeatures,
0
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72 ):
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73 yield x
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74
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75
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76 def feature_test_qual_value(feature, **kwargs):
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77 """Test qualifier values.
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78
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79 For every feature, check that at least one value in
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80 feature.quailfiers(kwargs['qualifier']) is in kwargs['attribute_list']
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81 """
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82 for attribute_value in feature.qualifiers.get(kwargs["qualifier"], []):
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83 if attribute_value in kwargs["attribute_list"]:
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84 return True
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85 return False
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86
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87
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88 def __get_features(child, interpro=False):
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89 child_features = {}
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90 for rec in GFF.parse(child):
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91 # Only top level
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92 for feature in rec.features:
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93 # Get the record id as parent_feature_id (since this is how it will be during remapping)
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94 parent_feature_id = rec.id
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95 # If it's an interpro specific gff3 file
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96 if interpro:
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97 # Then we ignore polypeptide features as they're useless
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98 if feature.type == "polypeptide":
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99 continue
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100 # If there's an underscore, we strip up to that underscore?
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101 # I do not know the rationale for this, removing.
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102 # if '_' in parent_feature_id:
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103 # parent_feature_id = parent_feature_id[parent_feature_id.index('_') + 1:]
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104
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105 try:
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106 child_features[parent_feature_id].append(feature)
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107 except KeyError:
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108 child_features[parent_feature_id] = [feature]
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109 # Keep a list of feature objects keyed by parent record id
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110 return child_features
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111
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112
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113 def __update_feature_location(feature, parent, protein2dna):
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114 start = feature.location.start
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115 end = feature.location.end
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116 if protein2dna:
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117 start *= 3
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118 end *= 3
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119
23
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120 if parent.location.strand != None and parent.location.strand >= 0:
0
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121 ns = parent.location.start + start
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122 ne = parent.location.start + end
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123 st = +1
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124 else:
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125 ns = parent.location.end - end
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126 ne = parent.location.end - start
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127 st = -1
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128
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129 # Don't let start/stops be less than zero. It's technically valid for them
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130 # to be (at least in the model I'm working with) but it causes numerous
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131 # issues.
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132 #
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133 # Instead, we'll replace with %3 to try and keep it in the same reading
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134 # frame that it should be in.
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135 if ns < 0:
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136 ns %= 3
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137 if ne < 0:
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138 ne %= 3
19
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139 if ns > ne:
23
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140 ne, ns = ns, ne # dunno why but sometimes happens
0
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141 feature.location = FeatureLocation(ns, ne, strand=st)
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142
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143 if hasattr(feature, "sub_features"):
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144 for subfeature in feature.sub_features:
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145 __update_feature_location(subfeature, parent, protein2dna)
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146
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147
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148 def rebase(parent, child, interpro=False, protein2dna=False, map_by="ID"):
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149 # get all of the features we will be re-mapping in a dictionary, keyed by parent feature ID
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150 child_features = __get_features(child, interpro=interpro)
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151
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152 for rec in GFF.parse(parent):
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153 replacement_features = []
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154 for feature in feature_lambda(
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155 rec.features,
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156 # Filter features in the parent genome by those that are
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157 # "interesting", i.e. have results in child_features array.
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158 # Probably an unnecessary optimisation.
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159 feature_test_qual_value,
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160 {
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161 "qualifier": map_by,
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162 "attribute_list": child_features.keys(),
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163 },
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164 subfeatures=False,
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165 ):
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166
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167 # Features which will be re-mapped
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168 to_remap = child_features[feature.id]
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169 # TODO: update starts
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170 fixed_features = []
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171 for x in to_remap:
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172 # Then update the location of the actual feature
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173 __update_feature_location(x, feature, protein2dna)
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174
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175 if interpro:
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176 for y in ("status", "Target"):
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177 try:
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178 del x.qualifiers[y]
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179 except Exception:
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180 pass
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181
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182 fixed_features.append(x)
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183 replacement_features.extend(fixed_features)
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184 # We do this so we don't include the original set of features that we
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185 # were rebasing against in our result.
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186 rec.features = replacement_features
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187 rec.annotations = {}
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188 GFF.write([rec], sys.stdout)
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189
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190
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191 if __name__ == "__main__":
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192 parser = argparse.ArgumentParser(
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193 description="rebase gff3 features against parent locations", epilog=""
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194 )
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195 parser.add_argument(
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196 "parent", type=argparse.FileType("r"), help="Parent GFF3 annotations"
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197 )
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198 parser.add_argument(
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199 "child",
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200 type=argparse.FileType("r"),
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201 help="Child GFF3 annotations to rebase against parent",
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202 )
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203 parser.add_argument(
17
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204 "--interpro",
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205 action="store_true",
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206 help="Interpro specific modifications",
0
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207 )
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208 parser.add_argument(
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209 "--protein2dna",
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210 action="store_true",
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211 help="Map protein translated results to original DNA data",
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212 )
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213 parser.add_argument("--map_by", help="Map by key", default="ID")
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214 args = parser.parse_args()
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215 rebase(**vars(args))