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from .intersect import Features
from .Functions import add_target_genome_paths,get_id_cov,stats_feature_missing_segment,stats_features
from .usefull_little_functions import get_featurePath_ends
from .load_gfa import get_segments_sequence
from .genome_transfer import generate_target_gff
from .variation_details import generate_variations_details
from .alignment import print_alignment
nina.marthe_ird.fr
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# handles all the transfer, variation, alignment (stats?), on the target genome.
def transfer_on_target(segments_file,genome_dir,target_genome,target_genome_paths,list_feat_absent,seg_size,args,segments_on_target_genome):
print(f' Generating the {target_genome} output')
stats=False
list_feature_to_transfer= Features.keys()
segments_list={} # list of segments usefull for the transfer. not all segments info will be loaded.
# create output files names
out_gff=genome_dir.joinpath(f'{target_genome}.gff')
out_var=genome_dir.joinpath(f'{target_genome}_var.txt')
out_aln=genome_dir.joinpath(f'{target_genome}_aln.txt')
# clear feature target path from the previous genome
for feature in Features.keys(): # empty the feature target paths for the next genome treated
Features[feature].segments_list_target=[]
for feat in tqdm(list_feature_to_transfer,desc=" Computing features paths in target genome",unit=" feature",disable=not args.verbose):
if Features[feat].parent=='':
add_target_genome_paths(feat,target_genome_paths,segments_on_target_genome)
if args.annotation:
with open(out_gff,'w') as file_out_gff:
reason_features_not_transfered=[0,0] # absent_features, low_cov_id
diff_size_transfered_features=[0,0] # [count,sum], to get the average
for feat in tqdm(list_feature_to_transfer,desc=f' Generating {target_genome} gff',unit=" feature",disable=not args.verbose):
feature=Features[feat]
if feature.parent=="": # usually a gene
for match in feature.segments_list_target: # compute cov and id for all matches.
if match[0]=='':
feature_target_path=[]
else:
feature_target_path=match[2]
match.append(get_id_cov(feat,seg_size,feature_target_path))
for match in feature.segments_list_target:
# if option no_dupl, only transfer the best match
if args.no_duplication:
# look for best match (best cov+id)
all_match=feature.segments_list_target
best_match=all_match[0]
for candidate_match in all_match:
candidate_cov=candidate_match[3][0]
candidate_id=candidate_match[3][1]
best_cov=best_match[3][0]
best_id=best_match[3][1]
if (candidate_cov+candidate_id)>(best_cov+best_id):
best_match=candidate_match
if match!=best_match: # if current match is not best match, dont transfer
match.append(False)
continue
transfer_stat=generate_target_gff(feat,seg_size,args,reason_features_not_transfered,match,file_out_gff,segments_on_target_genome) # the childs are handled in this function
if transfer_stat=="no":
list_feat_absent.append(feat)
else:
diff_size_transfered_features[0]+=1
diff_size_transfered_features[1]+=transfer_stat
if args.variation or args.alignment : # append dict of segments for which we may need the sequence
for feat in tqdm(list_feature_to_transfer,desc=" Fetching the sequence of the segments",unit=" feature",disable=not args.verbose):
list_seg=Features[feat].segments_list_source
if Features[feat].parent=="":
feature_target_path=[]
for occurence in Features[feat].segments_list_target: # add all the occurences of the feature in the target genome
feature_target_path+=occurence[2]
for segment in list_seg:
segments_list[segment[1:]]=''
for segment in feature_target_path:
segments_list[segment[1:]]=''
if not args.annotation:
# cov and id filter tests (if not done in annotation)
for feature_id in tqdm(list_feature_to_transfer,desc=" Computing the coverage and sequence id of the features before transfer",unit=' feature',disable=not args.verbose):
feature=Features[feature_id]
if feature.parent=="": # usually a gene
for match in feature.segments_list_target: # compute cov and id for all matches.
if match[0]=='':
feature_target_path=[]
else:
feature_target_path=match[2]
match.append(get_id_cov(feat,seg_size,feature_target_path))
for match in feature.segments_list_target:
[first_seg,last_seg,walk,copy_id,feature_target_path]=get_featurePath_ends(match)
# add the filter info in all the matches...
[cov,id]=get_id_cov(feature_id,seg_size,feature_target_path)
if (cov*100<args.coverage) or (id*100<args.identity) or (first_seg==''): # didnt put the "right_size" filter)
match.append(False) # store the information that this gene copy didnt pass the filters
else:
# if option no_dupl, only transfer the best match
if args.no_duplication:
# look for best match (best cov+id)
all_match=feature.segments_list_target
best_match=all_match[0]
for candidate_match in all_match:
candidate_cov=candidate_match[3][0]
candidate_id=candidate_match[3][1]
best_cov=best_match[3][0]
best_id=best_match[3][1]
if (candidate_cov+candidate_id)>(best_cov+best_id):
best_match=candidate_match
if match!=best_match: # if current match is not best match, dont transfer
match.append(False)
continue
match.append(True)
if args.variation:
seg_seq=get_segments_sequence(segments_file,segments_list)
with open(out_var, 'w') as file_out_var:
file_out_var.write(f'# feature_id\tfeature_type\tsequence_id\ttarget_start_position\ttarget_stop_position\ttarget_feature_length\tinversion\tlength_difference\tvariation_type\tref_sequence\talt_sequence\tvariation_length\tvariation_position_on_source_feature\tvariation_position_on_target_feature\n')
for feat in tqdm(list_feature_to_transfer,desc=f' Generating {target_genome} genes variation details',unit=" feature",disable=not args.verbose):
feature=Features[feat]
if feature.parent=="": # usually a gene
for match in feature.segments_list_target: # for all occurences of the gene
generate_variations_details(feat,seg_seq,match,file_out_var,segments_on_target_genome)
if args.alignment:
if not args.variation:
seg_seq=get_segments_sequence(segments_file,segments_list)
with open(out_aln,'w') as file_out_aln:
line="Sequence alignment generated from feature path comparison in pangenome graph. Made with GrAnnoT v1.\n\n"
file_out_aln.write(line)
for feat in tqdm(list_feature_to_transfer,desc=f' Generating the {args.source_genome} features alignment with {target_genome} features',unit=" feature",disable=not args.verbose):
feature=Features[feat]
if feature.parent=="": # usually a gene
for match in feature.segments_list_target: # for all occurences of the gene
if match[4]==True:
print_alignment(feat,match,seg_seq,file_out_aln)
if stats:
# create objects for stats on how many segments are absent in target genome, their average length, etc
feature_missing_segments=[[],[],[],[],[],[],[]] # [feature_missing_first,feature_missing_middle,feature_missing_last,feature_missing_all,feature_missing_total,feature_total,feature_ok]
# the fist segment of the feature is missing - feature_missing_first
# the last segment of the feature is missing - feature_missing_last
# at least one middle segment of the feature is missing - feature_missing_middle
# the entire feature is missing - feature_missing_all
# at least one segment is missing first, last, or middle) - feature_missing_total
# no segment is missing, the feature is complete - feature_ok
# total number of features, with missing segments or not - feature_total
# for each feature, get list of the segments where it is and the first and last segment of the feature on the new genome
list_seg=Features[feat].segments_list_source
[first_seg,last_seg,walk,copy_id,feature_target_path]=get_featurePath_ends(Features[feat].segments_list_target[0])
for feat in list_feature_to_transfer:
stats_feature_missing_segment(feature_missing_segments,first_seg,last_seg,list_seg,feat,walk,segments_on_target_genome)
if args.annotation:
absent_features=reason_features_not_transfered[0];low_cov_id=reason_features_not_transfered[1]
print(len(Features)-(absent_features+low_cov_id),"out of",len(Features),"features are transfered.")
print(absent_features,"out of",len(Features),"features are not transfered because they are absent in the new genome.")
print(low_cov_id,"out of",len(Features),"features are not transfered because their coverage or sequence identity is below threshold.")
print("average length difference of the transfered genes : ",diff_size_transfered_features[1]/diff_size_transfered_features[0])
stats_features(feature_missing_segments)
#clear segment info for next transfer
segments_on_target_genome.clear() # empty dict for the next genome treated