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Annotating 1b_CrossPlateContamination.py

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Auden Cote-L'Heureux 2024-01-16 12:07:32 -05:00 committed by GitHub
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1 changed files with 25 additions and 6 deletions
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PTL1/Transcriptomes/Scripts/1b_CrossPlateContamination.py
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@ -7,6 +7,7 @@
# Before running this script, you must run Script 1a.
#Dependencies
import sys
import os
import re
@ -15,10 +16,15 @@ import string
import os.path
from Bio import SeqIO
from sys import argv
#Holds a list of all taxon names
listtaxa=[]
#Clustering parameters
toosim = 0.99
seqcoverage = 0.7
#Group all sequences across all samples into one fasta file, which will then be clustered.
def merge_files(folder, minlen, conspecific_names):
mergefile = open('/'.join(folder.split('/')[:-1]) + '/forclustering.fasta','w+')
print("MERGE following files")
@ -35,7 +41,7 @@ def merge_files(folder, minlen, conspecific_names):
sort_cluster(folder, listtaxa, minlen, conspecific_names)
#Cluster all sequences across all samples using Vsearch
def sort_cluster(folder, listtaxa, minlen, conspecific_names):
if not os.path.exists('/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/'):
os.makedirs('/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
@ -43,7 +49,7 @@ def sort_cluster(folder, listtaxa, minlen, conspecific_names):
fastalist = []; fastadict= {}
conspecific_names_dict = { line.split('\t')[0] : line.split('\t')[1].strip() for line in open(conspecific_names) }
print('CREATE a dictionnary of sequences')
print('Creating a dictionnary of sequences\n')
for record in SeqIO.parse(open('/'.join(folder.split('/')[:-1]) + '/forclustering.fasta','r'),'fasta'):
if record.id[:10] not in conspecific_names_dict:
print('\nError in cross-plate contamination assessment: the ten-digit code ' + record.id[:10] + ' is not found in the conspecific names file. Please check that this file is correct and try again.\n')
@ -53,14 +59,17 @@ def sort_cluster(folder, listtaxa, minlen, conspecific_names):
fastalist.append(IDL)
fastadict[record.description] = record.seq
print("CLUSTER sequences that overlap at least 70%")
print("\nClustering sequences that overlap at least 70%")
#Cluster at 99% identity over 70% of the length
os.system('vsearch --cluster_fast ' + '/'.join(folder.split('/')[:-1]) + '/forclustering.fasta --strand both --query_cov '+str(seqcoverage)+' --id '+str(toosim) +' --uc ' + '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/results_forclustering.uc --threads 60' )
cluster_output = '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/results_forclustering.uc'
out2 = open('/'.join(folder.split('/')[:-1]) + '/fastatokeep.fas','w+')
out3 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.fas','w+')
out4 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.uc','w+')
print("CREATE a dictionary with clustering results")
print("Creating a dictionary with clustering results\n")
clustdict= {}; clustlist = []; allseq = []; clustline = {}; list= []; i=0; j=0
for row2 in open(cluster_output, 'r'):
if row2.split('\t')[0] == 'C' and int(row2.split('\t')[2]) < 2: # keep all unique sequences
@ -75,26 +84,35 @@ def sort_cluster(folder, listtaxa, minlen, conspecific_names):
clustline[row3.split('\t')[8].replace('\n','')] = row3.replace('\n','')
clustline[row3.split('\t')[9].replace('\n','')] = row3.replace('\n','')
print("PARSE the clusters: keep seed sequences (highest coverage) for each cluster")
print("Parsing the clusters: keeping seed sequences (highest coverage) for each cluster")
#For each cluster
for clust in clustlist:
#Define the highest covered sequence in the cluster as the 'master,' against which all
#more lowly covered sequences will be compared.
list = sorted(clustdict[clust], reverse = True, key=lambda x: int(x.split('_Cov')[1]))
master = list[0]
Covmaster = int(list[0].split('_Cov')[1])
master8dig = ('_').join(list[0].split('_')[0:3])[:-2]
#For each sequence that is not the highest covered sequence in the cluster
for seq in list:
clustered = seq.replace('\n','')
Covclustered = int(clustered.split('_Cov')[1])
clustered8dig = ('_').join(clustered.split('_')[0:3])[:-2]
#Keep any sequence if it has more than 1/10 the coverage of the highest covered sequence in the cluster
if float(Covmaster/Covclustered) < 10:
out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
i +=1
#Don't remove a sequence if it is from the same taxon as the highest covered sequence in the cluster
elif conspecific_names_dict[master[:10]] == conspecific_names_dict[clustered[:10]]:
out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
i +=1
#Keep any sequence with coverage >= 50
elif Covclustered >= 50:
out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
i +=1
#Otherwise, remove the lower covered sequence
else:
j +=1
out4 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.uc','a')
@ -111,6 +129,7 @@ def sort_cluster(folder, listtaxa, minlen, conspecific_names):
splittaxa(folder, listtaxa, minlen)
#Rewriting the files per taxon, minus the sequences removed by the similarity comparison
def splittaxa(folder, listtaxa, minlen):
for taxa in listtaxa:
tax_sf_path = '/'.join(folder.split('/')[:-1]) + '/' + taxa + '/SizeFiltered/'