jpayne@1: #!/usr/bin/env python3
jpayne@1: 
jpayne@1: 
jpayne@1: import gzip
jpayne@1: import io
jpayne@1: import pickle
jpayne@1: import os
jpayne@1: import sys
jpayne@1: 
jpayne@1: from argparse import ArgumentParser
jpayne@1: try:
jpayne@1: 	from version import SalmID_version
jpayne@1: except:
jpayne@1:     SalmID_version = "version unknown"
jpayne@1: 
jpayne@1: 
jpayne@1: def reverse_complement(sequence):
jpayne@1:     complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N', 'M': 'K', 'R': 'Y', 'W': 'W',
jpayne@1:                             'S': 'S', 'Y': 'R', 'K': 'M', 'V': 'B', 'H': 'D', 'D': 'H', 'B': 'V'}
jpayne@1:     return "".join(complement[base] for base in reversed(sequence))
jpayne@1: 
jpayne@1: 
jpayne@1: def parse_args():
jpayne@1:     "Parse the input arguments, use '-h' for help."
jpayne@1:     parser = ArgumentParser(description='SalmID - rapid Kmer based Salmonella identifier from sequence data')
jpayne@1:     # inputs
jpayne@1:     parser.add_argument('-v','--version', action='version', version='%(prog)s ' + SalmID_version)
jpayne@1:     parser.add_argument(
jpayne@1:         '-i','--input_file', type=str, required=False, default= 'None', metavar = 'your_fastqgz',
jpayne@1:         help='Single fastq.gz file input, include path to file if file is not in same directory ')
jpayne@1:     parser.add_argument(
jpayne@1:         '-e', '--extension', type=str, required=False, default= '.fastq.gz', metavar = 'file_extension',
jpayne@1:         help='File extension, if specified without "--input_dir", SalmID will attempt to ID all files\n' +
jpayne@1:              ' with this extension in current directory, otherwise files in input directory')
jpayne@1: 
jpayne@1:     parser.add_argument(
jpayne@1:         '-d','--input_dir', type=str, required=False, default='.', metavar = 'directory',
jpayne@1:         help='Directory which contains data for identification, when not specified files in current directory will be analyzed.')
jpayne@1:     parser.add_argument(
jpayne@1:         '-r', '--report', type=str, required=False, default='percentage', metavar = 'percentage, coverage or taxonomy',
jpayne@1:         help='Report either percentage ("percentage") of clade specific kmers recovered, average kmer-coverage ("cov"), or '
jpayne@1:              'taxonomy (taxonomic species ID, plus observed mean k-mer coverages and expected coverage).')
jpayne@1:     parser.add_argument(
jpayne@1:         '-m', '--mode', type=str, required=False, default='quick', metavar = 'quick or thorough',
jpayne@1:         help='Quick [quick] or thorough [thorough] mode')
jpayne@1:     if len(sys.argv)==1:
jpayne@1:         parser.print_help(sys.stderr)
jpayne@1:         sys.exit(1)
jpayne@1:     return parser.parse_args()
jpayne@1: 
jpayne@1: def get_av_read_length(file):
jpayne@1:     i = 1
jpayne@1:     n_reads = 0
jpayne@1:     total_length = 0
jpayne@1:     if file.endswith(".gz"):
jpayne@1:         file_content=io.BufferedReader(gzip.open(file))
jpayne@1:     else:
jpayne@1:         file_content=open(file,"r").readlines()
jpayne@1:     for line in file_content:
jpayne@1:         if i % 4 == 2:
jpayne@1:             total_length += len(line.strip())
jpayne@1:             n_reads +=1
jpayne@1:         i += 1
jpayne@1:         if n_reads == 100:
jpayne@1:             break
jpayne@1:     return total_length/100
jpayne@1: 
jpayne@1: 
jpayne@1: def createKmerDict_reads(list_of_strings, kmer):
jpayne@1:     kmer_table = {}
jpayne@1:     for string in list_of_strings:
jpayne@1:         sequence = string.strip('\n')
jpayne@1:         for i in range(len(sequence)-kmer+1):
jpayne@1:             new_mer =sequence[i:i+kmer]
jpayne@1:             new_mer_rc = reverse_complement(new_mer)
jpayne@1:             if new_mer in kmer_table:
jpayne@1:                 kmer_table[new_mer.upper()] += 1
jpayne@1:             else:
jpayne@1:                 kmer_table[new_mer.upper()] = 1
jpayne@1:             if new_mer_rc in kmer_table:
jpayne@1:                 kmer_table[new_mer_rc.upper()] += 1
jpayne@1:             else:
jpayne@1:                 kmer_table[new_mer_rc.upper()] = 1
jpayne@1:     return kmer_table
jpayne@1: 
jpayne@1: 
jpayne@1: def target_read_kmerizer_multi(file, k, kmerDict_1, kmerDict_2, mode):
jpayne@1:     mean_1 = None
jpayne@1:     mean_2 = None
jpayne@1:     i = 1
jpayne@1:     n_reads_1 = 0
jpayne@1:     n_reads_2 = 0
jpayne@1:     total_coverage_1 = 0
jpayne@1:     total_coverage_2 = 0
jpayne@1:     reads_1 = []
jpayne@1:     reads_2 = []
jpayne@1:     total_reads = 0
jpayne@1:     if file.endswith(".gz"):
jpayne@1:       file_content=io.BufferedReader(gzip.open(file))
jpayne@1:     else:
jpayne@1:       file_content=open(file,"r").readlines()
jpayne@1:     for line in file_content:
jpayne@1:         start = int((len(line) - k) // 2)
jpayne@1:         if i % 4 == 2:
jpayne@1:             total_reads += 1
jpayne@1:             if file.endswith(".gz"):
jpayne@1:                 s1 = line[start:k + start].decode()
jpayne@1:                 line=line.decode()
jpayne@1:             else:
jpayne@1:                 s1 = line[start:k + start]
jpayne@1:             if s1 in kmerDict_1:
jpayne@1:                 n_reads_1 += 1
jpayne@1:                 total_coverage_1 += len(line)
jpayne@1:                 reads_1.append(line)
jpayne@1:             if s1 in kmerDict_2:
jpayne@1:                 n_reads_2 += 1
jpayne@1:                 total_coverage_2 += len(line)
jpayne@1:                 reads_2.append(line)
jpayne@1:         i += 1
jpayne@1:         if mode == 'quick':
jpayne@1:             if total_coverage_2 >= 800000:
jpayne@1:                 break
jpayne@1: 
jpayne@1:     if len(reads_1) == 0:
jpayne@1:         kmer_Dict1 = {}
jpayne@1:     else:
jpayne@1:         kmer_Dict1 = createKmerDict_reads(reads_1, k)
jpayne@1:         mers_1 = set([key for key in kmer_Dict1])
jpayne@1:         mean_1 = sum([kmer_Dict1[key] for key in kmer_Dict1])/len(mers_1)
jpayne@1:     if len(reads_2) == 0:
jpayne@1:         kmer_Dict2 = {}
jpayne@1:     else:
jpayne@1:         kmer_Dict2 = createKmerDict_reads(reads_2, k)
jpayne@1:         mers_2 = set([key for key in kmer_Dict2])
jpayne@1:         mean_2 = sum([kmer_Dict2[key] for key in kmer_Dict2])/len(mers_2)
jpayne@1:     return kmer_Dict1, kmer_Dict2, mean_1, mean_2, total_reads
jpayne@1: 
jpayne@1: def mean_cov_selected_kmers(iterable, kmer_dict, clade_specific_kmers):
jpayne@1:     '''
jpayne@1:     Given an iterable (list, set, dictrionary) returns mean coverage for the kmers in iterable
jpayne@1:     :param iterable: set, list or dictionary containing kmers
jpayne@1:     :param kmer_dict: dictionary with kmers as keys, kmer-frequency as value
jpayne@1:     :param  clade_specific_kmers: list, dict or set of clade specific kmers
jpayne@1:     :return: mean frequency as float
jpayne@1:     '''
jpayne@1:     if len(iterable) == 0:
jpayne@1:         return 0
jpayne@1:     return sum([kmer_dict[value] for value in iterable])/len(clade_specific_kmers)
jpayne@1: 
jpayne@1: def kmer_lists(query_fastq_gz, k,
jpayne@1:                allmers,allmers_rpoB,
jpayne@1:                uniqmers_bongori,
jpayne@1:                uniqmers_I,
jpayne@1:                uniqmers_IIa,
jpayne@1:                uniqmers_IIb,
jpayne@1:                uniqmers_IIIa,
jpayne@1:                uniqmers_IIIb,
jpayne@1:                uniqmers_IV,
jpayne@1:                uniqmers_VI,
jpayne@1:                uniqmers_VII,
jpayne@1:                uniqmers_VIII,
jpayne@1:                uniqmers_bongori_rpoB,
jpayne@1:                uniqmers_S_enterica_rpoB,
jpayne@1:                uniqmers_Escherichia_rpoB,
jpayne@1:                uniqmers_Listeria_ss_rpoB,
jpayne@1:                uniqmers_Lmono_rpoB,
jpayne@1:                mode):
jpayne@1:     dict_invA, dict_rpoB, mean_invA, mean_rpoB , total_reads = target_read_kmerizer_multi(query_fastq_gz, k, allmers,
jpayne@1:                                                                             allmers_rpoB, mode)
jpayne@1:     target_mers_invA = set([key for key in dict_invA])
jpayne@1:     target_mers_rpoB = set([key for key in dict_rpoB])
jpayne@1:     if target_mers_invA == 0:
jpayne@1:         print('No reads found matching invA, no Salmonella in sample?')
jpayne@1:     else:
jpayne@1:         p_bongori = (len(uniqmers_bongori & target_mers_invA) / len(uniqmers_bongori)) * 100
jpayne@1:         p_I = (len(uniqmers_I & target_mers_invA) / len(uniqmers_I)) * 100
jpayne@1:         p_IIa = (len(uniqmers_IIa & target_mers_invA) / len(uniqmers_IIa)) * 100
jpayne@1:         p_IIb = (len(uniqmers_IIb & target_mers_invA) / len(uniqmers_IIb)) * 100
jpayne@1:         p_IIIa = (len(uniqmers_IIIa & target_mers_invA) / len(uniqmers_IIIa)) * 100
jpayne@1:         p_IIIb = (len(uniqmers_IIIb & target_mers_invA) / len(uniqmers_IIIb)) * 100
jpayne@1:         p_VI = (len(uniqmers_VI & target_mers_invA) / len(uniqmers_VI)) * 100
jpayne@1:         p_IV = (len(uniqmers_IV & target_mers_invA) / len(uniqmers_IV)) * 100
jpayne@1:         p_VII = (len(uniqmers_VII & target_mers_invA) / len(uniqmers_VII)) * 100
jpayne@1:         p_VIII = (len(uniqmers_VIII & target_mers_invA) / len(uniqmers_VIII)) * 100
jpayne@1:         p_bongori_rpoB = (len(uniqmers_bongori_rpoB & target_mers_rpoB) / len(uniqmers_bongori_rpoB)) * 100
jpayne@1:         p_Senterica = (len(uniqmers_S_enterica_rpoB & target_mers_rpoB) / len(uniqmers_S_enterica_rpoB)) * 100
jpayne@1:         p_Escherichia = (len(uniqmers_Escherichia_rpoB & target_mers_rpoB) / len(uniqmers_Escherichia_rpoB)) * 100
jpayne@1:         p_Listeria_ss = (len(uniqmers_Listeria_ss_rpoB & target_mers_rpoB) / len(uniqmers_Listeria_ss_rpoB)) * 100
jpayne@1:         p_Lmono = (len(uniqmers_Lmono_rpoB & target_mers_rpoB) / len(uniqmers_Lmono_rpoB)) * 100
jpayne@1:         bongori_invA_cov = mean_cov_selected_kmers(uniqmers_bongori & target_mers_invA, dict_invA, uniqmers_bongori)
jpayne@1:         I_invA_cov = mean_cov_selected_kmers(uniqmers_I & target_mers_invA, dict_invA, uniqmers_I)
jpayne@1:         IIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIa & target_mers_invA, dict_invA, uniqmers_IIa)
jpayne@1:         IIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIb & target_mers_invA, dict_invA, uniqmers_IIb)
jpayne@1:         IIIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIIa & target_mers_invA, dict_invA, uniqmers_IIIa)
jpayne@1:         IIIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIIb & target_mers_invA, dict_invA, uniqmers_IIIb)
jpayne@1:         IV_invA_cov = mean_cov_selected_kmers(uniqmers_IV & target_mers_invA, dict_invA, uniqmers_IV)
jpayne@1:         VI_invA_cov = mean_cov_selected_kmers(uniqmers_VI & target_mers_invA, dict_invA, uniqmers_VI)
jpayne@1:         VII_invA_cov = mean_cov_selected_kmers(uniqmers_VII & target_mers_invA, dict_invA, uniqmers_VII)
jpayne@1:         VIII_invA_cov = mean_cov_selected_kmers(uniqmers_VIII & target_mers_invA, dict_invA, uniqmers_VIII)
jpayne@1:         S_enterica_rpoB_cov = mean_cov_selected_kmers((uniqmers_S_enterica_rpoB & target_mers_rpoB), dict_rpoB,
jpayne@1:                                                       uniqmers_S_enterica_rpoB)
jpayne@1:         S_bongori_rpoB_cov = mean_cov_selected_kmers((uniqmers_bongori_rpoB & target_mers_rpoB), dict_rpoB,
jpayne@1:                                                      uniqmers_bongori_rpoB)
jpayne@1:         Escherichia_rpoB_cov = mean_cov_selected_kmers((uniqmers_Escherichia_rpoB & target_mers_rpoB), dict_rpoB,
jpayne@1:                                                        uniqmers_Escherichia_rpoB)
jpayne@1:         Listeria_ss_rpoB_cov = mean_cov_selected_kmers((uniqmers_Listeria_ss_rpoB & target_mers_rpoB), dict_rpoB,
jpayne@1:                                                        uniqmers_Listeria_ss_rpoB)
jpayne@1:         Lmono_rpoB_cov = mean_cov_selected_kmers((uniqmers_Lmono_rpoB & target_mers_rpoB), dict_rpoB,
jpayne@1:                                                  uniqmers_Lmono_rpoB)
jpayne@1:         coverages = [Listeria_ss_rpoB_cov, Lmono_rpoB_cov, Escherichia_rpoB_cov, S_bongori_rpoB_cov,
jpayne@1:                      S_enterica_rpoB_cov, bongori_invA_cov, I_invA_cov, IIa_invA_cov, IIb_invA_cov,
jpayne@1:                      IIIa_invA_cov, IIIb_invA_cov, IV_invA_cov, VI_invA_cov, VII_invA_cov, VIII_invA_cov]
jpayne@1:         locus_scores = [p_Listeria_ss, p_Lmono, p_Escherichia, p_bongori_rpoB, p_Senterica, p_bongori,
jpayne@1:                         p_I, p_IIa,p_IIb, p_IIIa, p_IIIb, p_IV, p_VI, p_VII, p_VIII]
jpayne@1:     return locus_scores, coverages, total_reads
jpayne@1: 
jpayne@1: def report_taxon(locus_covs, average_read_length, number_of_reads):
jpayne@1:     list_taxa = [ 'Listeria ss', 'Listeria monocytogenes', 'Escherichia sp.',
jpayne@1:                   'Salmonella bongori (rpoB)', 'Salmonella enterica (rpoB)',
jpayne@1:                   'Salmonella bongori (invA)', 'S. enterica subsp. enterica (invA)',
jpayne@1:                   'S. enterica subsp. salamae (invA: clade a)','S. enterica subsp. salamae (invA: clade b)',
jpayne@1:                   'S. enterica subsp. arizonae (invA)', 'S. enterica subsp. diarizonae (invA)',
jpayne@1:                   'S. enterica subsp. houtenae (invA)', 'S. enterica subsp. indica (invA)',
jpayne@1:                   'S. enterica subsp. VII (invA)', 'S. enterica subsp. salamae (invA: clade VIII)']
jpayne@1:     if sum(locus_covs) < 1:
jpayne@1:         rpoB = ('No rpoB matches!', 0)
jpayne@1:         invA = ('No invA matches!', 0)
jpayne@1:         return rpoB, invA, 0.0
jpayne@1:     else:
jpayne@1:         # given list of scores get taxon
jpayne@1:         if sum(locus_covs[0:5]) > 0:
jpayne@1:             best_rpoB = max(range(len(locus_covs[1:5])), key=lambda x: locus_covs[1:5][x])+1
jpayne@1:             all_rpoB = max(range(len(locus_covs[0:5])), key=lambda x: locus_covs[0:5][x])
jpayne@1:             if (locus_covs[best_rpoB] != 0) & (all_rpoB == 0):
jpayne@1:                 rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
jpayne@1:             elif (all_rpoB == 0) &  (round(sum(locus_covs[1:5]),1) < 1):
jpayne@1:                 rpoB = (list_taxa[0], locus_covs[0])
jpayne@1:             else:
jpayne@1:                 rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
jpayne@1:         else:
jpayne@1:             rpoB = ('No rpoB matches!', 0)
jpayne@1:         if sum(locus_covs[5:]) > 0:
jpayne@1:             best_invA = max(range(len(locus_covs[5:])), key=lambda x: locus_covs[5:][x])+5
jpayne@1:             invA = (list_taxa[best_invA], locus_covs[best_invA])
jpayne@1:         else:
jpayne@1:             invA = ('No invA matches!', 0)
jpayne@1:         if 'Listeria' in rpoB[0]:
jpayne@1:             return rpoB, invA, (average_read_length * number_of_reads) / 3000000
jpayne@1:         else:
jpayne@1:             return rpoB, invA, (average_read_length * number_of_reads) / 5000000
jpayne@1: 
jpayne@1: 
jpayne@1: 
jpayne@1: def main():
jpayne@1:     ex_dir = os.path.dirname(os.path.realpath(__file__))
jpayne@1:     args = parse_args()
jpayne@1:     input_file = args.input_file
jpayne@1:     if input_file != 'None':
jpayne@1:         files = [input_file]
jpayne@1:     else:
jpayne@1:         extension = args.extension
jpayne@1:         inputdir = args.input_dir
jpayne@1:         files = [inputdir + '/'+ f for f in os.listdir(inputdir) if f.endswith(extension)]
jpayne@1:     report = args.report
jpayne@1:     mode = args.mode
jpayne@1:     f_invA = open(ex_dir + "/invA_mers_dict", "rb")
jpayne@1:     sets_dict_invA = pickle.load(f_invA)
jpayne@1:     f_invA.close()
jpayne@1:     allmers = sets_dict_invA['allmers']
jpayne@1:     uniqmers_I = sets_dict_invA['uniqmers_I']
jpayne@1:     uniqmers_IIa = sets_dict_invA['uniqmers_IIa']
jpayne@1:     uniqmers_IIb = sets_dict_invA['uniqmers_IIb']
jpayne@1:     uniqmers_IIIa = sets_dict_invA['uniqmers_IIIa']
jpayne@1:     uniqmers_IIIb = sets_dict_invA['uniqmers_IIIb']
jpayne@1:     uniqmers_IV = sets_dict_invA['uniqmers_IV']
jpayne@1:     uniqmers_VI = sets_dict_invA['uniqmers_VI']
jpayne@1:     uniqmers_VII = sets_dict_invA['uniqmers_VII']
jpayne@1:     uniqmers_VIII = sets_dict_invA['uniqmers_VIII']
jpayne@1:     uniqmers_bongori = sets_dict_invA['uniqmers_bongori']
jpayne@1: 
jpayne@1:     f = open(ex_dir + "/rpoB_mers_dict", "rb")
jpayne@1:     sets_dict = pickle.load(f)
jpayne@1:     f.close()
jpayne@1: 
jpayne@1:     allmers_rpoB = sets_dict['allmers']
jpayne@1:     uniqmers_bongori_rpoB = sets_dict['uniqmers_bongori']
jpayne@1:     uniqmers_S_enterica_rpoB = sets_dict['uniqmers_S_enterica']
jpayne@1:     uniqmers_Escherichia_rpoB = sets_dict['uniqmers_Escherichia']
jpayne@1:     uniqmers_Listeria_ss_rpoB = sets_dict['uniqmers_Listeria_ss']
jpayne@1:     uniqmers_Lmono_rpoB = sets_dict['uniqmers_L_mono']
jpayne@1:     #todo: run kmer_lists() once, create list of tuples containing data to be used fro different reports
jpayne@1:     if report == 'taxonomy':
jpayne@1:         print('file\trpoB\tinvA\texpected coverage')
jpayne@1:         for f in files:
jpayne@1:             locus_scores, coverages, reads = kmer_lists(f, 27,
jpayne@1:                                                         allmers, allmers_rpoB,
jpayne@1:                                                         uniqmers_bongori,
jpayne@1:                                                         uniqmers_I,
jpayne@1:                                                         uniqmers_IIa,
jpayne@1:                                                         uniqmers_IIb,
jpayne@1:                                                         uniqmers_IIIa,
jpayne@1:                                                         uniqmers_IIIb,
jpayne@1:                                                         uniqmers_IV,
jpayne@1:                                                         uniqmers_VI,
jpayne@1:                                                         uniqmers_VII,
jpayne@1:                                                         uniqmers_VIII,
jpayne@1:                                                         uniqmers_bongori_rpoB,
jpayne@1:                                                         uniqmers_S_enterica_rpoB,
jpayne@1:                                                         uniqmers_Escherichia_rpoB,
jpayne@1:                                                         uniqmers_Listeria_ss_rpoB,
jpayne@1:                                                         uniqmers_Lmono_rpoB,
jpayne@1:                                                         mode)
jpayne@1:             pretty_covs = [round(cov, 1) for cov in coverages]
jpayne@1:             report = report_taxon(pretty_covs, get_av_read_length(f), reads)
jpayne@1:             print(f.split('/')[-1] + '\t' + report[0][0] + '[' + str(report[0][1]) + ']' + '\t' + report[1][0] +
jpayne@1:                   '[' + str(report[1][1]) + ']' +
jpayne@1:                   '\t' + str(round(report[2], 1)))
jpayne@1:     else:
jpayne@1:         print(
jpayne@1:         'file\tListeria sensu stricto (rpoB)\tL. monocytogenes (rpoB)\tEscherichia spp. (rpoB)\tS. bongori (rpoB)\tS. enterica' +
jpayne@1:         '(rpoB)\tS. bongori (invA)\tsubsp. I (invA)\tsubsp. II (clade a: invA)\tsubsp. II' +
jpayne@1:         ' (clade b: invA)\tsubsp. IIIa (invA)\tsubsp. IIIb (invA)\tsubsp.IV (invA)\tsubsp. VI (invA)\tsubsp. VII (invA)' +
jpayne@1:         '\tsubsp. II (clade VIII : invA)')
jpayne@1:         if report == 'percentage':
jpayne@1:             for f in files:
jpayne@1:                 locus_scores, coverages , reads = kmer_lists( f, 27,
jpayne@1:                            allmers,allmers_rpoB,
jpayne@1:                            uniqmers_bongori,
jpayne@1:                            uniqmers_I,
jpayne@1:                            uniqmers_IIa,
jpayne@1:                            uniqmers_IIb,
jpayne@1:                            uniqmers_IIIa,
jpayne@1:                            uniqmers_IIIb,
jpayne@1:                            uniqmers_IV,
jpayne@1:                            uniqmers_VI,
jpayne@1:                            uniqmers_VII,
jpayne@1:                            uniqmers_VIII,
jpayne@1:                            uniqmers_bongori_rpoB,
jpayne@1:                            uniqmers_S_enterica_rpoB,
jpayne@1:                            uniqmers_Escherichia_rpoB,
jpayne@1:                            uniqmers_Listeria_ss_rpoB,
jpayne@1:                            uniqmers_Lmono_rpoB,
jpayne@1:                            mode)
jpayne@1:                 pretty_scores = [str(round(score)) for score in locus_scores]
jpayne@1:                 print(f.split('/')[-1] +'\t' + '\t'.join(pretty_scores))
jpayne@1:         else:
jpayne@1:             for f in files:
jpayne@1:                 locus_scores, coverages , reads = kmer_lists( f, 27,
jpayne@1:                            allmers,allmers_rpoB,
jpayne@1:                            uniqmers_bongori,
jpayne@1:                            uniqmers_I,
jpayne@1:                            uniqmers_IIa,
jpayne@1:                            uniqmers_IIb,
jpayne@1:                            uniqmers_IIIa,
jpayne@1:                            uniqmers_IIIb,
jpayne@1:                            uniqmers_IV,
jpayne@1:                            uniqmers_VI,
jpayne@1:                            uniqmers_VII,
jpayne@1:                            uniqmers_VIII,
jpayne@1:                            uniqmers_bongori_rpoB,
jpayne@1:                            uniqmers_S_enterica_rpoB,
jpayne@1:                            uniqmers_Escherichia_rpoB,
jpayne@1:                            uniqmers_Listeria_ss_rpoB,
jpayne@1:                            uniqmers_Lmono_rpoB,
jpayne@1:                            mode)
jpayne@1:                 pretty_covs = [str(round(cov, 1)) for cov in coverages]
jpayne@1:                 print(f.split('/')[-1] + '\t' + '\t'.join(pretty_covs))
jpayne@1: 
jpayne@1: if __name__ == '__main__':
jpayne@1:     main()
jpayne@1: