import ClassSegFeat as Class
import subprocess
# functions to create the segments and the features
# create a segment with its first feature
def init_seg(line,feature):
line=line.split()
seg_id=line[3][1:]
chr=line[0]
start=line[1]
stop=line[2]
# add the current feature to the list of features that are on the segment
feature_strand=line[10]
feature_stranded=feature_strand+feature
feat=list()
feat.append(feature_stranded)
# create the segment
Segments[seg_id]=Class.Segment(seg_id,feat,chr,start,stop)
# create a feature with the segment its on
def init_feature(line):
line=line.split()
feature_id=line[12].split(';')[0].split("=")[1].replace(".","_").replace(":","_")
type=line[6]
annot=line[12]
chr=line[4]
start=line[7]
stop=line[8]
childs=list()
# add the current segment to the list of segments that have the feature
seg=line[3][1:]
strand=line[10]
seg_stranded=strand+seg
segments_list=list()
segments_list.append(seg_stranded)
# if the current feature has a parent, add the current feature in the childs list of its parent
if annot.split(";")[1].split("=")[0]=="Parent":
# for annotations that look like : ID=LOC_Os01g01010.1:exon_7;Parent=LOC_Os01g01010.1, where the parent is in the field 1
parent=annot.split(";")[1].split("=")[1].replace(".","_").replace(":","_")
add_child(parent,feature_id)
elif annot.split(";")[2].split("=")[0]=="Parent":
# for annotations that look like : ID=LOC_Os01g01010.1;Name=LOC_Os01g01010.1;Parent=LOC_Os01g01010, where the parent is in the field 2
parent=annot.split(";")[2].split("=")[1].replace(".","_").replace(":","_")
add_child(parent,feature_id)
else: parent=""
# create the feature
Features[feature_id]=Class.Feature(feature_id,type,chr,start,stop,annot,childs,parent,segments_list)
# add a feature to an existing segment
def add_feature(seg,new_feature,strand):
new_feature_stranded=strand+new_feature
if new_feature_stranded not in Segments[seg].features:
Segments[seg].features.append(new_feature_stranded)
# add a child feature to an existing feature
def add_child(feat,new_child):
if feat in Features.keys(): # if the parent feature exists
if new_child not in Features[feat].childs:
Features[feat].childs.append(new_child)
# add a segment to an existing feature
def add_seg(feat,new_seg,strand):
seg_stranded=strand+new_seg
if seg_stranded not in Features[feat].segments_list:
Features[feat].segments_list.append(seg_stranded)
# create a note for the child features that do not have annotation.
def set_note(id):
# the note contains information on the function of the feature and is used for statistics on hypothetical/putatives features.
feat=Features[id]
if feat.type=="gene": # if the feature is a gene, the note is the last field of its annotation.
feat.note=feat.annot.split(';')[-1]
else: # else, the note will be the note of the gene that contains the feature. in my gff, only the genes have an annotation.
# we go back to the parent of the feature, and its parent if necessary, etc, until we find the gene.
curent=feat.parent
annot_found=False
while annot_found==False:
if Features[curent].type=="gene": # if/once we found the gene, we get its note to transfer it to the child feature
note=Features[curent].annot.split(';')[-1]
feat.note=note
annot_found=True
else: # if we didn't find the gene, we go back to the current feature's parent until we find it
curent=Features[Features[curent].parent].id
# create all the Segment and Feature objects in the dictionnaries Segments and Features
def create_seg_feat(intersect_path):
global Features
global Segments
Segments={}
Features={}
# open the file with the intersect between the segments and the gff
file = open(intersect_path, 'r')
lines=file.readlines()
for line in lines:
# get the ids for the dictionnaries' keys
feature_id=line.split()[12].split(';')[0].split("=")[1].replace(".","_").replace(":","_")
segment_id=line.split()[3][1:]
if feature_id not in Features: # if the feature doesn't exist, create it and add the current segment to its seg list
init_feature(line)
else: # if it exists, add the current segment to the list of segments that have the existing feature
strand=line.split()[10]
add_seg(feature_id,segment_id,strand)
if segment_id not in Segments: # if the segment doesn't exist, create it and add the current feature to its feat list
init_seg(line, feature_id)
else: # if it exists, add the current feature to the list of features on the existing segment
strand=line.split()[10]
add_feature(segment_id,feature_id,strand)
# for all the features, add the note (information on the function of the feature), and the positions on the first and last seg.
for feat_id in Features:
feat=Features[feat_id]
set_note(feat.id)
feat.pos_start=get_start(feat.segments_list[0][1:],feat.id)
feat.pos_stop=get_stop(feat.segments_list[-1][1:],feat.id)
file.close()
# functions to generate the graph's gff from the segments and features created with create_seg_feat
# get the feature's start position on the segment
def get_start(seg,feat):
s=Segments[seg]
f=Features[feat]
if s.start>f.start:
result=1
else:
result=f.start-s.start
return result
# get the feature's stop position on the segment
def get_stop(seg,feat):
s=Segments[seg]
f=Features[feat]
if s.stop<f.stop:
result=s.size
else:
result=f.stop-s.start
return result
# go through all the segments in Segments and prints the gff, with one line for each segment/feature intersection
def graph_gff(file_out):
print("generation of the graph's gff")
file_out = open(file_out, 'w')
for segment in Segments:
# get the list of the features on the segment
features_seg=Segments[segment].features
# go through all these features, and print the gff line for each
for feature_stranded in features_seg:
strand=feature_stranded[0:1]
feature=feature_stranded[1:]
segment_stranded=strand+segment
type=Features[feature].type
start=get_start(segment,feature)
stop=get_stop(segment,feature)
# get the rank and the total number of ocurrences for the feature
rank=str(Features[feature].segments_list.index(segment_stranded)+1)
total=str(len(Features[feature].segments_list))
# create the annotation with the rank information
annotation=Features[feature].annot+";Rank_occurrence="+rank+";Total_occurrences="+total
# write the gff line in the output file
line=segment+"\tgraph_gff\t"+type+"\t"+str(start)+"\t"+str(stop)+"\t.\t"+strand+"\t.\t"+annotation+"\n"
file_out.write(line)
file_out.close()
# functions to generate a genome's gff from the graph's gff
# get the start position of the features on the linear genome, using their coordinates on the graph and the coordinantes of the segments on the genome
def get_start_2(seg_start, seg_genome, feat_id):
ns=seg_start[1:]
s_start=seg_genome[seg_start][1]
f_start=get_start(seg_start,feat_id)
start=int(s_start)+int(f_start)
return start
# get the stop position of the features on the linear genome, using their coordinates on the graph and the coordinantes of the segments on the genome
def get_stop_2(seg_stop, seg_genome, feat_id):
s_start=seg_genome[seg_stop][1]
f_stop=get_stop(seg_stop,feat_id)
stop=int(s_start)+int(f_stop)
return stop
# get the position of a part of a feature on the complete feature (on the original genome)
def get_position(seg_start,seg_stop,feat_start,feature,seg_genome):
# start position of the entire feature on the reference
start_gene_start=Segments[feat_start[1:]].start+get_start(feat_start[1:],feature.id)-1
# start position of the piece of the feature on the reference
start_first_seg=Segments[seg_start].start+get_start(seg_start,feature.id)-1
# start and stop position of the feature on azucena, to get the length of the piece of the feature
start_azu=get_start_2(seg_start,seg_genome,feature.id)
stop_azu=get_stop_2(seg_stop,seg_genome,feature.id)
# start position of the piece of the feature on the complete feature
start_gene=int(start_first_seg)-int(start_gene_start)+1
# stop position : start+length
stop_gene=start_gene+(stop_azu-start_azu)
position=";position="+str(start_gene)+"-"+str(stop_gene)
#position=";start_position_on_feature="+str(start_gene)+":stop_position_on_feature="+str(stop_gene)
return position
# get the proportion of a part of the feature on the total length
def get_proportion(seg_start,seg_stop,seg_genome,feature):
start_azu=get_start_2(seg_start,seg_genome,feature.id) # start position of the feature on azucena
stop_azu=get_stop_2(seg_stop,seg_genome,feature.id) # stop position of the feature on azucena
proportion=";proportion="+str(stop_azu-start_azu+1)+"/"+str(feature.size)
#proportion=";number_bases="+str(stop_azu-start_azu+1)+";total_bases="+str(feature.size)
return proportion
# returns the gff line to write in the output file
def write_line(list_seg,i,feat,seg_start,feat_start,seg_genome):
seg_stop=list_seg[i-1]
feature=Features[feat]
chr=seg_genome[seg_start[1:]][0]
strand=list_seg[i-1][0:1]
# start and stop position of the feature on azucena
start_azu=get_start_2(seg_start[1:],seg_genome,feature.id)
stop_azu=get_stop_2(seg_stop[1:],seg_genome,feature.id)
proportion=get_proportion(seg_start[1:],seg_stop[1:],seg_genome,feature)
position=get_position(seg_start[1:],seg_stop[1:],feat_start,feature,seg_genome)
annotation=feature.annot+proportion+position
out_line=chr+"\tgraph_gff\t"+feature.type+"\t"+str(start_azu)+"\t"+str(stop_azu)+"\t.\t"+strand+"\t.\t"+annotation+"\n"
return out_line
def stats_features(feature_missing_first,feature_missing_middle,feature_missing_last,feature_missing_all,feature_missing_total,list_feature,feature_ok):
# creates files with the features by category (missing first, middle, end segment, etc)
feat_miss_first=open('features_missing_first.txt','w')
feat_miss_first.write("features missing the first segment on azucena :\n")
for first in feature_missing_first :
feat_miss_first.write(Features[first].annot)
if Features[first].type!='gene':
feat_miss_first.write(';')
feat_miss_first.write(Features[first].note)
feat_miss_first.write("\n")
feat_miss_first.close()
feat_miss_middle=open('features_missing_middle.txt','w')
feat_miss_middle.write("\nfeatures missing a middle segment on azucena :\n")
for middle in feature_missing_middle :
feat_miss_middle.write(Features[middle].annot)
if Features[middle].type!='gene':
feat_miss_middle.write(';')
feat_miss_middle.write(Features[middle].note)
feat_miss_middle.write("\n")
feat_miss_middle.close()
feat_miss_last=open('features_missing_last.txt','w')
feat_miss_last.write("\nfeatures missing the last segment on azucena :\n")
for last in feature_missing_last :
feat_miss_last.write(Features[last].annot)
if Features[last].type!='gene':
feat_miss_last.write(';')
feat_miss_last.write(Features[last].note)
feat_miss_last.write("\n")
feat_miss_last.close()
feat_miss=open('features_missing.txt','w')
feat_miss.write("\nfeatures missing entirely on azucena :\n")
for entier in feature_missing_all :
feat_miss.write(Features[entier].annot)
if Features[entier].type!='gene':
feat_miss.write(';')
feat_miss.write(Features[entier].note)
feat_miss.write("\n")
feat_miss.close()
feat_miss_total=open('features_missing_total.txt','w')
feat_miss_total.write("\nfeatures missing at least one segment on azucena :\n")
for total in feature_missing_total :
feat_miss_total.write(Features[total].annot)
if Features[total].type!='gene':
feat_miss_total.write(';')
feat_miss_total.write(Features[total].note)
feat_miss_total.write("\n")
feat_miss_total.close()
feat_all=open('all_features.txt','w')
for feat in list_feature:
feat_all.write(Features[feat].annot)
if Features[feat].type!='gene':
feat_all.write(';')
feat_all.write(Features[feat].note)
feat_all.write("\n")
feat_all.close()
feat_ok=open('features_ok.txt','w')
feat_ok.write("\nfeatures entirely present in azucena :\n")
for ok in feature_ok :
feat_ok.write(Features[ok].annot)
if Features[ok].type!='gene':
feat_ok.write(';')
feat_ok.write(Features[ok].note)
feat_ok.write("\n")
feat_ok.close()
# prints the stats for the features (hypothetical/putative rate, by category)
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_missing_first.txt | wc -l",))
total=len(feature_missing_first)
print("\nthe first segment is missing for", len(feature_missing_first) ,"features, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_missing_middle.txt | wc -l",))
total=len(feature_missing_middle)
print("a middle segment is missing for", len(feature_missing_middle) ,"features, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_missing_last.txt | wc -l",))
total=len(feature_missing_last)
print("the last segment is missing for", len(feature_missing_last) ,"features, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_missing.txt | wc -l",))
total=len(feature_missing_all)
print(len(feature_missing_all) ,"features are entirely missing, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_missing_total.txt | wc -l",))
total=len(feature_missing_total)
print("there is at least one segment missing for", len(feature_missing_total) ,"features, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' features_ok.txt | wc -l",))
total=len(feature_ok)
print(len(feature_ok) ,"features are entirely present in the new genome, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
hyp_put=int(subprocess.getoutput("grep 'hypothetical\|putative' all_features.txt | wc -l",))
total=len(list_feature)
print("there is", len(list_feature) ,"features in total, including",round(100*(hyp_put)/total,2),"% hypothetical or putative.")
command="rm features_missing*.txt && rm all_features.txt && rm features_ok.txt"
subprocess.run(command,shell=True)
def gff_detail(list_seg,seg_genome,feat,file_out_detail):
# outputs each fragment of each feature, with its position on the new genome and its annotation :
# loop that goes through all the segments that have the current feature
# keeps the first segment present in the genome found, and when it finds a segment absent in the genome, prints the part of the fragment, and resets the first segment present.
# continues to go through the segments, keeps the next segment present in the genome, and when it finds a segment absent, prints the second part of the feature.
# etc.
# at the end of the loop, prints the last part of the fragment.
size_list=len(list_seg)
feat_start="empty" # stocks the first segment of the feature
seg_start="empty" # stocks the first segment of the current part of the feature
#seg_stop="empty" # stocks the last segment of the current part of the feature
for i in range(0,size_list):
if list_seg[i][1:] in seg_genome: # look for a segment present in the genome
if feat_start=="empty":
feat_start=list_seg[i]
if seg_start=="empty": # if we dont have a start, take the current segment for the start of the part of the feature
seg_start=list_seg[i]
#else: if we already have a start, keep going though the segments until we find a stop (segment not in azucena)
else:
if seg_start!="empty": # found a stop. so print the line, reset seg_start, and keep going through the segments to find the next seg_start
out_line=write_line(list_seg,i,feat,seg_start,feat_start,seg_genome)
file_out_detail.write(out_line)
seg_start="empty"
#seg_stop="empty"
#else: if the current segment is not in azucena but there is no start, keep looking for a start
# print the last piece of the feature in the end
if list_seg[i][1:] in seg_genome:
out_line=write_line(list_seg,i+1,feat,seg_start,feat_start,seg_genome)
file_out_detail.write(out_line)
def gff_one(first_seg,last_seg,seg_genome,feat,list_seg,file_out,n):
# outputs each feature once, from the first to the last segment present on the new genome and its annotation (if the size is ok) :
bad_size=0
absent_features=0
if (first_seg!='0') & (last_seg!='0'):
start2=get_start_2(first_seg,seg_genome,feat)
stop2=get_stop_2(last_seg,seg_genome,feat)
size_on_genome=int(stop2)-int(start2)+1
size_diff=abs(size_on_genome-Features[feat].size)
if not ((size_on_genome>Features[feat].size*n) | (size_on_genome<Features[feat].size/n)) : # si le nouveau gene est pas 2x plus grand ou plus petit que l'original
chr=seg_genome[first_seg][0]
strand=list_seg[0][0:1]
start2=get_start_2(first_seg,seg_genome,feat)
stop2=get_stop_2(last_seg,seg_genome,feat)
size_on_genome=int(stop2)-int(start2)+1
size_diff=abs(size_on_genome-Features[feat].size)
nb_variants=0
for segment in list_seg:
if segment[1:] not in seg_genome:
nb_variants+=1
annotation=Features[feat].annot+";Size_diff="+str(size_diff)+";Nb_variants="+str(nb_variants)
line=chr+" graph_gff "+Features[feat].type+" "+str(get_start_2(first_seg,seg_genome,feat))+" "+str(get_stop_2(last_seg,seg_genome,feat))+" . "+strand+" . "+annotation+"\n"
file_out.write(line)
else:
bad_size+=1
else :
absent_features+=1
return list([bad_size,absent_features])
# writes the gff of azucena using the gff of the graph
def genome_gff(pos_seg, gff, out, gfa):
print("generation of the genome's gff ")
# create a dictionnary with the positions of the segments on the genome to transfer on
seg_genome={}
bed=open(pos_seg,'r')
lines=bed.readlines()
for line in lines:
line=line.split()
if line[3][0:1]=='>':
strand='+'
elif line[3][0:1]=='<':
strand='-'
else:
strand=''
seg=line[3][1:]
ch=line[0]
start=line[1]
stop=line[2]
seg_genome[seg]=list([ch,start,stop,strand])
bed.close()
# create a dictionnary with the sequences of the segments of the graph and a dictionary with the paths from the gfa. each genome name gives the list of its segments in order
file_gfa=open(gfa,'r')
lines_gfa=file_gfa.readlines()
seg_seq={}
paths={}
for line in lines_gfa:
line=line.split()
if (line[0]=="S"):
seg_id='s'+line[1]
seg_seq[seg_id]=line[2]
if (line[0]=="W") & (line[1]!="_MINIGRAPH_"):
path=line[6].replace(">",";>")
path=path.replace("<",";<").split(';')
list_path=list()
for segment in path:
if segment[0:1]=='>':
list_path.append('+s'+segment[1:])
elif segment[0:1]=='<':
list_path.append('-s'+segment[1:])
else:
list_path.append('s'+segment[1:])
#couple=['s'+segment[1:],segment[0:1]]
##list_path.append(couple)
#list_path.append('s'+segment[1:])
paths[line[3]]=list_path
file_out_detail = open("azucena_chr10_detail.gff", 'w')
file_out=open(out,'w')
file_out2 = open("segments_manquants.txt", 'w')
diff_list=list()
bad_size=0
absent_features=0
stats=True
# get the list of all the features to transfer from the gff
gff=open(gff,'r')
list_feature=list()
lines=gff.readlines()
for line in lines:
id=(line.split()[8].split(";")[0].split("=")[1].replace(".","_").replace(":","_"))
if id not in list_feature:
list_feature.append(id)
gff.close()
# create objects for stats on how many segments are absent in azucena, their average length, etc
if stats==True:
seg_first=list()
seg_middle=list()
seg_last=list()
seg_total_abs=list()
seg_ok=list()
seg_entier=list()
seg_total=list()
feature_missing_first=list()
feature_missing_middle=list()
feature_missing_last=list()
feature_missing_all=list()
feature_missing_total=list()
feature_total=list()
feature_ok=list()
# for each feature, get list of the segments where it is and the first and last segment of the feature on the genome
for feat in list_feature:
list_seg=Features[feat].segments_list
first_seg="0"
for segment in list_seg:
if segment[1:] in seg_genome:
first_seg=segment[1:]
break
last_seg="0"
for segment in reversed(list_seg):
if segment[1:] in seg_genome:
last_seg=segment[1:]
break
# stats on how many segments are absent in azucena, their average length, etc
# for the segments
# segment missing that is at the start of a feature - seg_first
# segment missing that is at the end of a feature - seg_last
# segment missing that is in the middle of a feature - seg_middle
# segment missing that contains the entire feature - seg_entier
# total number of genes missing - seg_total_abs
# segments not missing - seg_ok
# segments missing of not - seg_total
if (stats==True):
for segment in list_seg: # counts the segments in each category
seg_len=Segments[segment[1:]].size
if segment[1:] not in seg_genome: # the segment is missing
seg_total.append(seg_len)
if segment==list_seg[0]: # the segment is the first of the feature
if segment==list_seg[-1]: # the segment is the last of the feature
seg_entier.append(seg_len) # the segment is the first and the last, so it contains the entire feature
else:
seg_first.append(seg_len)
elif segment==list_seg[-1]: # the segment is the last of the feature
seg_last.append(seg_len)
else:
seg_middle.append(seg_len)
else: # the segment is present on the genome
seg_ok.append(seg_len)
# for the features :
# 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
if (stats==True):
feature_total.append(feat)
if (first_seg=='0') & (last_seg=='0') : # no segment of the feature is in the genome, the feature is missing entirely
feature_missing_all.append(feat)
elif first_seg != list_seg[0][1:]: # the first segment is missing
feature_missing_first.append(feat)
elif last_seg!=list_seg[-1][1:]: # the last segment is missing
feature_missing_last.append(feat)
# go through all the segments, check if some are missing in the middle of the feature
elif (len(list_seg)!=1) & (feat not in feature_missing_all): # to access the second to last element
for segment in list_seg[1-(len(list_seg)-2)]:
if segment[1:] not in seg_genome:
feature_missing_middle.append(feat)
break
# go through the segments, to see if one is missing anywhere on the feature
for segment in list_seg:
if segment[1:] not in seg_genome:
if feat not in feature_missing_total:
feature_missing_total.append(feat)
break
# if the feature doesnt have a missing segment, it is complete. ADD THE PATH CHECK !!
if feat not in feature_missing_total:
feature_ok.append(feat)
# outputs each fragment of each feature, with its position on the new genome and its annotation :
gff_detail(list_seg,seg_genome,feat,file_out_detail)
# outputs each feature once, from the first to the last segment present on the new genome and its annotation (if the size is ok) :
n=2
result=gff_one(first_seg,last_seg,seg_genome,feat,list_seg,file_out,n)
bad_size+=result[0]
absent_features+=result[1]
# outputs the detail of the missing fragments for each feature :
dif_list=diff_list+gff_missing(list_seg,seg_genome,first_seg,last_seg,feat,file_out2,paths,seg_seq)
diff_list=dif_list
#print(feat,"ok")
#gff_missing(list_seg,seg_genome,first_seg,last_seg,feat,file_out2,paths,seg_seq)
file_out_detail.close()
file_out.close()
file_out2.close()
# print stats
from statistics import median, mean
if stats==True:
print(len(Features)-(bad_size+absent_features),"out of",len(Features),"features are transfered.")
print(bad_size,"out of",len(Features), "features are not transfered because they are too big or too small compared to the original genome.")
print(absent_features,"out of",len(Features),"features are not transfered because they are absent in the new genome.")
print("average length difference of the transfered genes : ",sum(diff_list)/len(diff_list))
stats_features(feature_missing_first,feature_missing_middle,feature_missing_last,feature_missing_all,feature_missing_total,list_feature,feature_ok)
# prints the stats for the segments
#print(len(seg_first),"segments missing at the beginning of a feature, of mean length", round(mean(seg_first),2), "and median length",median(seg_first))
#print(len(seg_middle),"segments missing in the middle of a feature, of mean length", round(mean(seg_middle),2), "and median length",median(seg_middle))
#print(len(seg_last), "segments missing at the end of a feature, of mean length", round(mean(seg_last),2), "and median length",median(seg_last))
#print(len(seg_entier),"segments that have an entiere feature (not in beggining/middle/end) missing, of mean length", round(mean(seg_entier),2), "and median length",median(seg_entier))
#print(len(seg_total),"segments that have a feature piece missing, of mean length", round(mean(seg_total),2), "and median length",median(seg_total))
#print(len(seg_ok),"segments that have a feature found, of mean length", round(mean(seg_ok),2), "and median length", median(seg_ok))
def gff_missing(list_seg,seg_genome,first_seg,last_seg,feat,file_out2,paths,seg_seq):
# outputs the detail of the missing fragments for each feature :
diff_list=list()
# get the list of the segments in a feature that are missing in the genome
segments_missing=list()
for segment in list_seg:
if segment[1:] not in seg_genome:
segments_missing.append(Segments[segment[1:]])
# if there are missing segments, print them
if (first_seg!='0') & (last_seg!='0'):
var=0 # count variations, to see if there is any
feature=Features[feat]
feat_start=feature.start
# get the lengths of the feature, on the original genome and on the new one
start2=get_start_2(first_seg,seg_genome,feat)
stop2=get_stop_2(last_seg,seg_genome,feat)
size_on_genome=int(stop2)-int(start2)+1
size_diff=size_on_genome-feature.size
diff_list.append(abs(int(size_diff))) # to have stats on the size of the segments missing
line=feat+" : \nlength on the original genome = "+str(feature.size)+"\nlength difference (length in the new genome-length in the original genome) = "+str(size_diff)+"\n"
file_out2.write(line)
# find the path in azucena.
first_strand=seg_genome[first_seg][3]
first_seg_stranded=first_strand+first_seg
last_strand=seg_genome[last_seg][3]
last_seg_stranded=last_strand+last_seg
id_first_seg=int(paths["CM020642.1_Azucena_chromosome10"].index(first_seg_stranded))
id_last_seg=int(paths["CM020642.1_Azucena_chromosome10"].index(last_seg_stranded))
list_segfeat_azu=paths["CM020642.1_Azucena_chromosome10"][id_first_seg:id_last_seg+1]
list_segfeat_nb=Features[feat].segments_list
# get list segments present in azucena.
# for segment in list_seg:
# boucles parcourir les deux listes.
i=0
j=0
last=''
last_taille=0
last_seq=''
last_start=''
start=0
pos=''
# check if the gene is inverted :
# if all the segments in common (no strand) have the oposite strand, change the strand (or ignore it ?).
# the list from the paths are stranded.
# first get the list of the segments in common without the strand
# then compare the strands
# if all the strands are different, print 'there is an inversion'.
# else print nothing. then ignore the strand.
# check if there is an inversion
if detect_inversion(list_segfeat_nb,list_segfeat_azu):
line="inversion detected\n"
file_out2.write(line)
# remove the strands from the lists of segments :
list_segfeat_nb=remove_strand(list_segfeat_nb)
list_segfeat_azu=remove_strand(list_segfeat_azu)
# detect and print variations ignoring the strands
while (i<len(list_segfeat_nb)) & (j<len(list_segfeat_azu)):
#for i in range(0,len(list_segfeat_nb)):
if list_segfeat_nb[i] != list_segfeat_azu[j]: # if there is a difference between the two paths
if list_segfeat_azu[j] not in list_segfeat_nb: # if the segment in azu is absent in nb
if list_segfeat_nb[i] not in list_segfeat_azu: # if the segment in nb is absent is azu
# is both segments are absent in the other genome, its a substitution
# print if we had an insertion or deletion running
if last=='insertion':
pos=int(Segments[last_start].start)-int(feat_start)+1
line='insertion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
elif last=='deletion':
if start==1:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion at the start of the feature of '+last_seq+" at the position "+str(pos)+'\n'
start=0
else:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
last=''
last_taille=0
last_seq=''
# print the substitution
# substitution of segment list_segfeat_nb[i][1:] by segment list_segfeat_azu[j][1:]
if i==0:
pos=int(Segments[list_segfeat_nb[i]].start)-int(feat_start)+1
line='substitution at the start of the feature of '+list_segfeat_nb[i]+' '+seg_seq[list_segfeat_nb[i]]+" by "+list_segfeat_azu[j]+' '+seg_seq[list_segfeat_azu[j]]+" at the position "+str(pos)+'\n'
else:
pos=int(Segments[list_segfeat_nb[i]].start)-int(feat_start)+1
line='substitution of '+list_segfeat_nb[i]+' '+seg_seq[list_segfeat_nb[i]]+" by "+list_segfeat_azu[j]+' '+seg_seq[list_segfeat_azu[j]]+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
# go to the next segments on the paths
i+=1
j+=1
else: # azu segment not in nb, but nb segment in azu : insertion
if last=='deletion':
if start==1:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion at the start of the feature of '+last_seq+" at the position "+str(pos)+'\n'
start=0
else:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
last=''
last_taille=0
last_start=''
last_seq=''
if last=='insertion':
last_taille+=int(seg_genome[list_segfeat_azu[j]][2])-int(seg_genome[list_segfeat_azu[j]][1])+1
last_seq=last_seq+seg_seq[list_segfeat_azu[j]]
else:
last='insertion'
last_seq=seg_seq[list_segfeat_azu[j]]
last_taille=int(seg_genome[list_segfeat_azu[j]][2])-int(seg_genome[list_segfeat_azu[j]][1])+1
last_start=list_segfeat_nb[i]
j+=1
elif list_segfeat_nb[i] not in list_segfeat_azu: # nb segment not in azu, but azu segment in nb : deletion
if last=='insertion':
pos=int(Segments[last_start].start)-int(feat_start)+1
line='insertion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
last=''
last_start=''
last_taille=0
last_seq=''
if last=='deletion':
last_taille+=Segments[list_segfeat_nb[i]].size
last_seq=last_seq+seg_seq[list_segfeat_nb[i]]
else:
last='deletion'
last_start=list_segfeat_nb[i]
last_taille=Segments[list_segfeat_nb[i]].size
last_seq=seg_seq[list_segfeat_nb[i]]
if i==0:
start=1
i+=1
else : # idk yet.
line="weird order change"
file_out2.write(line)
var+=1
i+=1
j+=1
else: # segment present in both. print the running indel if there is one
#print("segment",list_segfeat_nb[i][1:],'ok')
if last=='insertion':
pos=int(Segments[last_start].start)-int(feat_start)+1
line='insertion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
elif last=='deletion':
if start==1:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion at the start of the feature of '+last_seq+" at the position "+str(pos)+'\n'
start=0
else:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
last=''
last_taille=0
last_start=''
last_seq=''
i+=1
j+=1
# finish printing the current indel
if last=='insertion':
pos=int(Segments[last_start].start)-int(feat_start)+1
line='insertion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
elif last=='deletion':
if start==1:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion at the start of the feature of '+last_seq+" at the position "+str(pos)+'\n'
start=0
else:
pos=int(Segments[last_start].start)-int(feat_start)+1
line='deletion of '+last_seq+" at the position "+str(pos)+'\n'
file_out2.write(line)
var+=1
# see if the end is missing for one of the two genomes
# si i a la taille max, peut etre que j aussi est arrivé à la fin, si j+1 est trop grand, donc si j==len(list_segfeat_azu)-1
# si i et j taille max (ou i et j+1), c bon les deux sont arrivés à la fin (je crois).
# si i taille max et pas j, insertion. si j taille max et pas i, délétion.
if not((i>=len(list_segfeat_nb)-1) & (j>=len(list_segfeat_azu)-1)):
if (i<len(list_segfeat_nb)-1):
if not (j>=len(list_segfeat_azu)-1):
print('pb')
# length of the deletion = position of the end of the feature - position of the start of the deletion
length=feature.stop-Segments[list_segfeat_nb[i]].start
line="deletion at the end of length "+str(length)+"\n"
file_out2.write(line)
var+=1
elif (j<len(list_segfeat_azu)-1):
file_out2.write("insertion at the end\n")
var+=1
# au début et à la fin, si on a une délétion, on peut que donner la taille du segment, pas la taille de la délétion.
# dans le segment ou la feature, garder l'info de où commence le gène sur le segment.....
# taille de la délétion = (début segment fin + position de fin) - début last_seg
# ici last seg c list_nb[i], et segment fin c'est list_nb[-1]
if var==0:
line=feat+" : no variation\n"
file_out2.write(line)
else:
line=feat+" : feature entirely absent.\n"
file_out2.write(line)
file_out2.write('\n')
return diff_list
def remove_strand(list_seg):
list_unstrand=list()
for seg_stranded in list_seg:
list_unstrand.append(seg_stranded[1:])
return list_unstrand
def detect_inversion(list_1,list_2):
list_1_unstrand=remove_strand(list_1)
list_2_unstrand=remove_strand(list_2)
# get the list of segments in common
seg_common=list()
for seg in list_1_unstrand:
if seg in list_2_unstrand:
seg_common.append(seg)
# for each segment in common, check if the strand is the same. check index in list unstranded to get the segment in list stranded
same_strand_count=0
for seg in seg_common:
index_1=list_1_unstrand.index(seg)
index_2=list_2_unstrand.index(seg)
if list_1[index_1]==list_2[index_2]:
same_strand_count+=1
# if there is less than 10% of strand difference among the common segments, return False, no inversion
if len(seg_common)-same_strand_count<len(seg_common)/10:
return False
else:
return True