def lift_single_feature(
threshold,
feature_order,
features_to_lift,
feature_hierarchy,
previous_feature_start,
previous_feature_ref_start,
previous_gene_seq,
unmapped_features,
aligned_feature,
seq_id_threshold,
feature_locations,
lifted_features_list,
distance_factor,
):
new_parent_name = aligned_feature[0].query_name
original_parent_name = liftoff_utils.convert_id_to_original(
new_parent_name)
parent = features_to_lift[original_parent_name]
if len(aligned_feature) > 0:
lifted_children, alignment_coverage, seq_id = find_best_mapping.find_best_mapping(
aligned_feature, parent.end - parent.start + 1, parent,
feature_hierarchy, previous_feature_start,
previous_feature_ref_start, previous_gene_seq, feature_locations,
lifted_features_list, distance_factor)
lifted_features = merge_lifted_features.merge_lifted_features(
lifted_children, parent, unmapped_features, threshold,
new_parent_name, feature_order, feature_hierarchy,
alignment_coverage, seq_id, seq_id_threshold)
else:
unmapped_features.append(parent)
return lifted_features, aligned_feature[0].query_name
def lift_features_subset(all_overlapping_features, threshold, feature_order,
parent_dict, children_dict, intermediate_dict,
previous_gene_start, unmapped_features,
aligned_feature, seq_id_threshold):
new_parent_name = aligned_feature[0].query_name
original_parent_name = liftoff_utils.convert_id_to_original(
new_parent_name)
copy_tag = liftoff_utils.get_copy_tag(new_parent_name)
if new_parent_name in all_overlapping_features:
overlapping_features = all_overlapping_features[new_parent_name]
else:
overlapping_features = []
parent = parent_dict[original_parent_name]
if len(aligned_feature) > 0:
lifted_children, shortest_path_weight, alignment_coverage, seq_id = find_best_mapping.find_best_mapping(
aligned_feature, parent.end - parent.start + 1, parent,
overlapping_features, children_dict, previous_gene_start, copy_tag)
lifted_feature_list, feature_start = merge_lifted_features.merge_lifted_features(
lifted_children, parent, unmapped_features, threshold,
new_parent_name, feature_order, parent_dict, intermediate_dict,
alignment_coverage, seq_id, seq_id_threshold)
else:
unmapped_features.append(parent)
feature_start = 0
return lifted_feature_list, aligned_feature[0].query_name, feature_start
def lift_single_feature(all_overlapping_features, threshold, feature_order,
features_to_lift, feature_hierarchy,
previous_gene_start, unmapped_features,
aligned_feature, seq_id_threshold):
new_parent_name = aligned_feature[0].query_name
original_parent_name = liftoff_utils.convert_id_to_original(
new_parent_name)
overlapping_features = get_overlapping_features_list(
new_parent_name, all_overlapping_features)
parent = features_to_lift[original_parent_name]
if len(aligned_feature) > 0:
lifted_children, alignment_coverage, seq_id = find_best_mapping.find_best_mapping(
aligned_feature, parent.end - parent.start + 1, parent,
overlapping_features, feature_hierarchy.children,
previous_gene_start)
lifted_feature_list, feature_start = merge_lifted_features.merge_lifted_features(
lifted_children, parent, unmapped_features, threshold,
new_parent_name, feature_order, feature_hierarchy,
alignment_coverage, seq_id, seq_id_threshold)
else:
unmapped_features.append(parent)
feature_start = 0
return lifted_feature_list, aligned_feature[0].query_name, feature_start