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151 lines
6.1 KiB
Python
151 lines
6.1 KiB
Python
# Last updated 2/23/2022
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# This script is intended to remove intra-plate contamination
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# by removing sequences with low coverage relative to other
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# very similar sequences from samples sequenced on the same
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# plate.
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# Before running this script, you must run Script 1a.
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import sys
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import os
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import re
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import time
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import string
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import os.path
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from Bio import SeqIO
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from sys import argv
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listtaxa=[]
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toosim = 0.99
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seqcoverage = 0.7
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def merge_files(folder, minlen, conspecific_names):
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mergefile = open('/'.join(folder.split('/')[:-1]) + '/forclustering.fasta','w+')
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print("MERGE following files")
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for taxafile in os.listdir(folder):
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if taxafile[0] != ".":
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listtaxa.append(taxafile.split('.' + str(minlen) + 'bp')[0])
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for line2 in SeqIO.parse(folder+'/'+taxafile, 'fasta'):
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if int(len(str(line2.seq))) >= int(minlen):
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mergefile.write('>'+taxafile.split('.' + str(minlen) + 'bp')[0] + '_' + line2.description + '\n' + str(line2.seq) + '\n')
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else:
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print(line2, " is too short")
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mergefile.close()
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sort_cluster(folder, listtaxa, minlen, conspecific_names)
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def sort_cluster(folder, listtaxa, minlen, conspecific_names):
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if not os.path.exists('/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/'):
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os.makedirs('/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
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fastalist = []; fastadict= {}
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conspecific_names_dict = { line.split('\t')[0] : line.split('\t')[1].strip() for line in open(conspecific_names) }
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print('CREATE a dictionnary of sequences')
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for record in SeqIO.parse(open('/'.join(folder.split('/')[:-1]) + '/forclustering.fasta','r'),'fasta'):
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if record.id[:10] not in conspecific_names_dict:
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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')
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quit()
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IDL = record.description, int(record.description.split('_Cov')[1].replace('\n',''))
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fastalist.append(IDL)
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fastadict[record.description] = record.seq
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print("CLUSTER sequences that overlap at least 70%")
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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' )
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cluster_output = '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/results_forclustering.uc'
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out2 = open('/'.join(folder.split('/')[:-1]) + '/fastatokeep.fas','w+')
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out3 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.fas','w+')
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out4 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.uc','w+')
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print("CREATE a dictionary with clustering results")
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clustdict= {}; clustlist = []; allseq = []; clustline = {}; list= []; i=0; j=0
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for row2 in open(cluster_output, 'r'):
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if row2.split('\t')[0] == 'C' and int(row2.split('\t')[2]) < 2: # keep all unique sequences
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out2.write('>'+row2.split('\t')[8] + '\n' + str(fastadict[row2.split('\t')[8]])+ '\n')
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if row2.split('\t')[0] == 'C' and int(row2.split('\t')[2]) > 1: # create another dictionary
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clustdict.setdefault(row2.split('\t')[8], [row2.split('\t')[8]])
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clustlist.append(row2.split('\t')[8])
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for row3 in open(cluster_output, 'r'):
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if row3.split('\t')[0] == 'H':
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clustdict[row3.split('\t')[9].replace('\n','')].append(row3.split('\t')[8].replace('\n',''))
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clustline[row3.split('\t')[8].replace('\n','')] = row3.replace('\n','')
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clustline[row3.split('\t')[9].replace('\n','')] = row3.replace('\n','')
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print("PARSE the clusters: keep seed sequences (highest coverage) for each cluster")
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for clust in clustlist:
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list = sorted(clustdict[clust], reverse = True, key=lambda x: int(x.split('_Cov')[1]))
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master = list[0]
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Covmaster = int(list[0].split('_Cov')[1])
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master8dig = ('_').join(list[0].split('_')[0:3])[:-2]
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for seq in list:
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clustered = seq.replace('\n','')
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Covclustered = int(clustered.split('_Cov')[1])
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clustered8dig = ('_').join(clustered.split('_')[0:3])[:-2]
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if float(Covmaster/Covclustered) < 10:
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out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
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i +=1
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elif conspecific_names_dict[master[:10]] == conspecific_names_dict[clustered[:10]]:
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out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
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i +=1
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elif Covclustered >= 50:
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out2.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
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i +=1
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else:
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j +=1
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out4 = open('/'.join(folder.split('/')[:-1]) + '/fastatoremoved.uc','a')
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out3.write('>'+clustered + '\n' + str(fastadict[clustered])+ '\n')
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print(clustline[clustered],'\t' , master )
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out4.write(clustline[clustered]+ '\t' + master + '\n')
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out4.close()
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print('there are ', str(i),' sequences kept and ',str(j),' sequences removed')
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out2.close()
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out3.close()
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splittaxa(folder, listtaxa, minlen)
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def splittaxa(folder, listtaxa, minlen):
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for taxa in listtaxa:
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tax_sf_path = '/'.join(folder.split('/')[:-1]) + '/' + taxa + '/SizeFiltered/'
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os.system('mv ' + tax_sf_path + taxa + '.' + str(minlen) + 'bp.fasta' + ' ' + tax_sf_path + taxa + '.' + str(minlen) + 'bp.preXPlate.fasta')
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with open(tax_sf_path + taxa + '.' + str(minlen) + 'bp.fasta','w') as o:
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for kept in SeqIO.parse('/'.join(folder.split('/')[:-1]) + '/fastatokeep.fas','fasta'):
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if taxa in kept.description:
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o.write('>' + kept.description.replace(taxa + '_', '') + '\n' + str(kept.seq) + '\n')
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os.system('mv ' + '/'.join(folder.split('/')[:-1]) + '/fastatokeep.fas ' + '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
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os.system('mv ' + '/'.join(folder.split('/')[:-1]) + '/fastatoremoved.fas ' + '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
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os.system('mv ' + '/'.join(folder.split('/')[:-1]) + '/fastatoremoved.uc ' + '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
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os.system('mv ' + '/'.join(folder.split('/')[:-1]) + '/forclustering.fasta ' + '/'.join(folder.split('/')[:-1]) + '/clusteringresults_vsearch/')
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def main():
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script, folder, minlen, conspecific_names = argv
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merge_files(folder, minlen, conspecific_names)
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main()
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