comparison jbrowse2/gff3_rebase.py @ 6:88b9b105c09b draft

Uploaded

5:42ca8804cd93

                feature_copy.sub_features = []
                yield feature_copy
            else:
                yield feature

        if hasattr(feature, 'sub_features'):
            for x in feature_lambda(feature.sub_features, test, test_kwargs, subfeatures=subfeatures):
                yield x


def feature_test_qual_value(feature, **kwargs):
    """Test qualifier values.

    For every feature, check that at least one value in
    feature.quailfiers(kwargs['qualifier']) is in kwargs['attribute_list']
    """
    for attribute_value in feature.qualifiers.get(kwargs['qualifier'], []):
        if attribute_value in kwargs['attribute_list']:
            return True
    return False


def __get_features(child, interpro=False):

            # Get the record id as parent_feature_id (since this is how it will be during remapping)
            parent_feature_id = rec.id
            # If it's an interpro specific gff3 file
            if interpro:
                # Then we ignore polypeptide features as they're useless
                if feature.type == 'polypeptide':
                    continue
                # If there's an underscore, we strip up to that underscore?
                # I do not know the rationale for this, removing.
                # if '_' in parent_feature_id:
                    # parent_feature_id = parent_feature_id[parent_feature_id.index('_') + 1:]

            try:
                child_features[parent_feature_id].append(feature)
            except KeyError:
                child_features[parent_feature_id] = [feature]

    if ne < 0:
        ne %= 3

    feature.location = FeatureLocation(ns, ne, strand=st)

    if hasattr(feature, 'sub_features'):
        for subfeature in feature.sub_features:
            __update_feature_location(subfeature, parent, protein2dna)


def rebase(parent, child, interpro=False, protein2dna=False, map_by='ID'):
    # get all of the features we will be re-mapping in a dictionary, keyed by parent feature ID
    child_features = __get_features(child, interpro=interpro)

    for rec in GFF.parse(parent):
        replacement_features = []
        for feature in feature_lambda(
                rec.features,
                # Filter features in the parent genome by those that are
                # "interesting", i.e. have results in child_features array.
                # Probably an unnecessary optimisation.
                feature_test_qual_value,
                {
                    'qualifier': map_by,
                    'attribute_list': child_features.keys(),
                },
                subfeatures=False):

            # Features which will be re-mapped
            to_remap = child_features[feature.id]
            # TODO: update starts
            fixed_features = []
            for x in to_remap:
                # Then update the location of the actual feature
                __update_feature_location(x, feature, protein2dna)

                if interpro:
                    for y in ('status', 'Target'):
                        try:
                            del x.qualifiers[y]
                        except Exception:
                            pass

        rec.features = replacement_features
        rec.annotations = {}
        GFF.write([rec], sys.stdout)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='rebase gff3 features against parent locations', epilog="")
    parser.add_argument('parent', type=argparse.FileType('r'), help='Parent GFF3 annotations')
    parser.add_argument('child', type=argparse.FileType('r'), help='Child GFF3 annotations to rebase against parent')
    parser.add_argument('--interpro', action='store_true',
                        help='Interpro specific modifications')
    parser.add_argument('--protein2dna', action='store_true',
                        help='Map protein translated results to original DNA data')
    parser.add_argument('--map_by', help='Map by key', default='ID')
    args = parser.parse_args()
    rebase(**vars(args))

6:88b9b105c09b

                feature_copy.sub_features = []
                yield feature_copy
            else:
                yield feature

        if hasattr(feature, "sub_features"):
            for x in feature_lambda(
                feature.sub_features, test, test_kwargs, subfeatures=subfeatures
            ):
                yield x


def feature_test_qual_value(feature, **kwargs):
    """Test qualifier values.

    For every feature, check that at least one value in
    feature.quailfiers(kwargs['qualifier']) is in kwargs['attribute_list']
    """
    for attribute_value in feature.qualifiers.get(kwargs["qualifier"], []):
        if attribute_value in kwargs["attribute_list"]:
            return True
    return False


def __get_features(child, interpro=False):

            # Get the record id as parent_feature_id (since this is how it will be during remapping)
            parent_feature_id = rec.id
            # If it's an interpro specific gff3 file
            if interpro:
                # Then we ignore polypeptide features as they're useless
                if feature.type == "polypeptide":
                    continue
                # If there's an underscore, we strip up to that underscore?
                # I do not know the rationale for this, removing.
                # if '_' in parent_feature_id:
                # parent_feature_id = parent_feature_id[parent_feature_id.index('_') + 1:]

            try:
                child_features[parent_feature_id].append(feature)
            except KeyError:
                child_features[parent_feature_id] = [feature]

    if ne < 0:
        ne %= 3

    feature.location = FeatureLocation(ns, ne, strand=st)

    if hasattr(feature, "sub_features"):
        for subfeature in feature.sub_features:
            __update_feature_location(subfeature, parent, protein2dna)


def rebase(parent, child, interpro=False, protein2dna=False, map_by="ID"):
    # get all of the features we will be re-mapping in a dictionary, keyed by parent feature ID
    child_features = __get_features(child, interpro=interpro)

    for rec in GFF.parse(parent):
        replacement_features = []
        for feature in feature_lambda(
            rec.features,
            # Filter features in the parent genome by those that are
            # "interesting", i.e. have results in child_features array.
            # Probably an unnecessary optimisation.
            feature_test_qual_value,
            {
                "qualifier": map_by,
                "attribute_list": child_features.keys(),
            },
            subfeatures=False,
        ):

            # Features which will be re-mapped
            to_remap = child_features[feature.id]
            # TODO: update starts
            fixed_features = []
            for x in to_remap:
                # Then update the location of the actual feature
                __update_feature_location(x, feature, protein2dna)

                if interpro:
                    for y in ("status", "Target"):
                        try:
                            del x.qualifiers[y]
                        except Exception:
                            pass

        rec.features = replacement_features
        rec.annotations = {}
        GFF.write([rec], sys.stdout)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="rebase gff3 features against parent locations", epilog=""
    )
    parser.add_argument(
        "parent", type=argparse.FileType("r"), help="Parent GFF3 annotations"
    )
    parser.add_argument(
        "child",
        type=argparse.FileType("r"),
        help="Child GFF3 annotations to rebase against parent",
    )
    parser.add_argument(
        "--interpro", action="store_true", help="Interpro specific modifications"
    )
    parser.add_argument(
        "--protein2dna",
        action="store_true",
        help="Map protein translated results to original DNA data",
    )
    parser.add_argument("--map_by", help="Map by key", default="ID")
    args = parser.parse_args()
    rebase(**vars(args))