Mercurial > repos > fubar > jbrowse2

annotate gff3_rebase.py @ 129:d08080933718 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit 6d8a6a308c219c112dbfc09fe48ad462746d6fb0
author fubar
date Mon, 07 Oct 2024 08:55:19 +0000
parents b1260bca5fdc
children
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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
d78175596286 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit cd77dffaad652cfb75b98bde5231beaa6d63cd5b-dirty
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2 import argparse
d78175596286 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit cd77dffaad652cfb75b98bde5231beaa6d63cd5b-dirty
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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
d78175596286 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit cd77dffaad652cfb75b98bde5231beaa6d63cd5b-dirty
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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
d78175596286 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/jbrowse2 commit cd77dffaad652cfb75b98bde5231beaa6d63cd5b-dirty
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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
98
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66 if hasattr(feature, 'sub_features'):
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67 for x in feature_lambda(feature.sub_features, test, test_kwargs, subfeatures=subfeatures):
0
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68 yield x
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69
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70
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71 def feature_test_qual_value(feature, **kwargs):
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72 """Test qualifier values.
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73
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74 For every feature, check that at least one value in
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75 feature.quailfiers(kwargs['qualifier']) is in kwargs['attribute_list']
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76 """
98
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77 for attribute_value in feature.qualifiers.get(kwargs['qualifier'], []):
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78 if attribute_value in kwargs['attribute_list']:
0
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79 return True
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80 return False
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81
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82
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83 def __get_features(child, interpro=False):
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84 child_features = {}
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85 for rec in GFF.parse(child):
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86 # Only top level
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87 for feature in rec.features:
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88 # Get the record id as parent_feature_id (since this is how it will be during remapping)
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89 parent_feature_id = rec.id
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90 # If it's an interpro specific gff3 file
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91 if interpro:
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92 # Then we ignore polypeptide features as they're useless
98
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93 if feature.type == 'polypeptide':
0
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94 continue
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95 # If there's an underscore, we strip up to that underscore?
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96 # I do not know the rationale for this, removing.
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97 # if '_' in parent_feature_id:
98
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98 # parent_feature_id = parent_feature_id[parent_feature_id.index('_') + 1:]
0
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99
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100 try:
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101 child_features[parent_feature_id].append(feature)
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102 except KeyError:
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103 child_features[parent_feature_id] = [feature]
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104 # Keep a list of feature objects keyed by parent record id
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105 return child_features
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106
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107
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108 def __update_feature_location(feature, parent, protein2dna):
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109 start = feature.location.start
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110 end = feature.location.end
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111 if protein2dna:
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112 start *= 3
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113 end *= 3
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114
98
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115 if parent.location.strand >= 0:
0
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116 ns = parent.location.start + start
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117 ne = parent.location.start + end
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118 st = +1
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119 else:
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120 ns = parent.location.end - end
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121 ne = parent.location.end - start
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122 st = -1
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123
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124 # Don't let start/stops be less than zero. It's technically valid for them
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125 # to be (at least in the model I'm working with) but it causes numerous
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126 # issues.
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127 #
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128 # Instead, we'll replace with %3 to try and keep it in the same reading
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129 # frame that it should be in.
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130 if ns < 0:
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131 ns %= 3
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132 if ne < 0:
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133 ne %= 3
98
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134
0
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135 feature.location = FeatureLocation(ns, ne, strand=st)
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136
98
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137 if hasattr(feature, 'sub_features'):
0
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138 for subfeature in feature.sub_features:
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139 __update_feature_location(subfeature, parent, protein2dna)
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140
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141
98
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142 def rebase(parent, child, interpro=False, protein2dna=False, map_by='ID'):
0
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143 # get all of the features we will be re-mapping in a dictionary, keyed by parent feature ID
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144 child_features = __get_features(child, interpro=interpro)
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145
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146 for rec in GFF.parse(parent):
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147 replacement_features = []
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148 for feature in feature_lambda(
98
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149 rec.features,
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150 # Filter features in the parent genome by those that are
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151 # "interesting", i.e. have results in child_features array.
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152 # Probably an unnecessary optimisation.
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153 feature_test_qual_value,
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154 {
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155 'qualifier': map_by,
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156 'attribute_list': child_features.keys(),
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157 },
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158 subfeatures=False):
0
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159
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160 # Features which will be re-mapped
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161 to_remap = child_features[feature.id]
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162 # TODO: update starts
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163 fixed_features = []
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164 for x in to_remap:
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165 # Then update the location of the actual feature
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166 __update_feature_location(x, feature, protein2dna)
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167
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168 if interpro:
98
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169 for y in ('status', 'Target'):
0
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170 try:
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171 del x.qualifiers[y]
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172 except Exception:
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173 pass
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174
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175 fixed_features.append(x)
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176 replacement_features.extend(fixed_features)
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177 # We do this so we don't include the original set of features that we
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178 # were rebasing against in our result.
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179 rec.features = replacement_features
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180 rec.annotations = {}
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181 GFF.write([rec], sys.stdout)
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182
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183
98
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184 if __name__ == '__main__':
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185 parser = argparse.ArgumentParser(description='rebase gff3 features against parent locations', epilog="")
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186 parser.add_argument('parent', type=argparse.FileType('r'), help='Parent GFF3 annotations')
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187 parser.add_argument('child', type=argparse.FileType('r'), help='Child GFF3 annotations to rebase against parent')
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188 parser.add_argument('--interpro', action='store_true',
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189 help='Interpro specific modifications')
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190 parser.add_argument('--protein2dna', action='store_true',
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191 help='Map protein translated results to original DNA data')
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192 parser.add_argument('--map_by', help='Map by key', default='ID')
0
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193 args = parser.parse_args()
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194 rebase(**vars(args))