estrain@0: #!/usr/bin/env python3
estrain@0: 
estrain@0: 
estrain@0: import gzip
estrain@0: import io
estrain@0: import pickle
estrain@0: import os
estrain@0: import sys
estrain@0: 
estrain@0: from argparse import ArgumentParser
estrain@0: try:
estrain@0:     from .version import SalmID_version
estrain@0: except ImportError:
estrain@0:     SalmID_version = "version unknown"
estrain@0: 
estrain@0: 
estrain@0: def reverse_complement(sequence):
estrain@0:     """return the reverse complement of a nucleotide (including IUPAC ambiguous nuceotide codes)"""
estrain@0:     complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N', 'M': 'K', 'R': 'Y', 'W': 'W',
estrain@0:                             'S': 'S', 'Y': 'R', 'K': 'M', 'V': 'B', 'H': 'D', 'D': 'H', 'B': 'V'}
estrain@0:     return "".join(complement[base] for base in reversed(sequence))
estrain@0: 
estrain@0: 
estrain@0: def parse_args():
estrain@0:     "Parse the input arguments, use '-h' for help."
estrain@0:     parser = ArgumentParser(description='SalmID - rapid Kmer based Salmonella identifier from sequence data')
estrain@0:     # inputs
estrain@0:     parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + SalmID_version)
estrain@0:     parser.add_argument(
estrain@0:         '-i', '--input_file', type=str, required=False, default='None', metavar='your_fastqgz',
estrain@0:         help='Single fastq.gz file input, include path to file if file is not in same directory ')
estrain@0:     parser.add_argument(
estrain@0:         '-e', '--extension', type=str, required=False, default='.fastq.gz', metavar='file_extension',
estrain@0:         help='File extension, if specified without "--input_dir", SalmID will attempt to ID all files\n' +
estrain@0:              ' with this extension in current directory, otherwise files in input directory')
estrain@0: 
estrain@0:     parser.add_argument(
estrain@0:         '-d', '--input_dir', type=str, required=False, default='.', metavar='directory',
estrain@0:         help='Directory which contains data for identification, when not specified files in current directory will be analyzed.')
estrain@0:     parser.add_argument(
estrain@0:         '-r', '--report', type=str, required=False, default='percentage', metavar='percentage, coverage or taxonomy',
estrain@0:         help='Report either percentage ("percentage") of clade specific kmers recovered, average kmer-coverage ("cov"), or '
estrain@0:              'taxonomy (taxonomic species ID, plus observed mean k-mer coverages and expected coverage).')
estrain@0:     parser.add_argument(
estrain@0:         '-m', '--mode', type=str, required=False, default='quick', metavar='quick or thorough',
estrain@0:         help='Quick [quick] or thorough [thorough] mode')
estrain@0:     if len(sys.argv) == 1:
estrain@0:         parser.print_help(sys.stderr)
estrain@0:         sys.exit(1)
estrain@0:     return parser.parse_args()
estrain@0: 
estrain@0: 
estrain@0: def get_av_read_length(file):
estrain@0:     """Samples the first 100 reads from a fastq file and return the average read length."""
estrain@0:     i = 1
estrain@0:     n_reads = 0
estrain@0:     total_length = 0
estrain@0:     if file.endswith(".gz"):
estrain@0:         file_content = io.BufferedReader(gzip.open(file))
estrain@0:     else:
estrain@0:         file_content = open(file, "r").readlines()
estrain@0:     for line in file_content:
estrain@0:         if i % 4 == 2:
estrain@0:             total_length += len(line.strip())
estrain@0:             n_reads += 1
estrain@0:         i += 1
estrain@0:         if n_reads == 100:
estrain@0:             break
estrain@0:     return total_length / 100
estrain@0: 
estrain@0: 
estrain@0: def createKmerDict_reads(list_of_strings, kmer):
estrain@0:     """Count occurence of K-mers in a list of strings
estrain@0: 
estrain@0:     Args:
estrain@0:         list_of_strings(list of str): nucleotide sequences as a list of strings
estrain@0:         kmer(int): length of the K-mer to count
estrain@0: 
estrain@0:     Returns:
estrain@0:         dict: dictionary with kmers as keys, counts for each kmer as values"""
estrain@0:     kmer_table = {}
estrain@0:     for string in list_of_strings:
estrain@0:         sequence = string.strip('\n')
estrain@0:         if len(sequence) >= kmer:
estrain@0:             for i in range(len(sequence) - kmer + 1):
estrain@0:                 new_mer = sequence[i:i + kmer]
estrain@0:                 new_mer_rc = reverse_complement(new_mer)
estrain@0:                 if new_mer in kmer_table:
estrain@0:                     kmer_table[new_mer.upper()] += 1
estrain@0:                 else:
estrain@0:                     kmer_table[new_mer.upper()] = 1
estrain@0:                 if new_mer_rc in kmer_table:
estrain@0:                     kmer_table[new_mer_rc.upper()] += 1
estrain@0:                 else:
estrain@0:                     kmer_table[new_mer_rc.upper()] = 1
estrain@0:     return kmer_table
estrain@0: 
estrain@0: 
estrain@0: def target_read_kmerizer_multi(file, k, kmerDict_1, kmerDict_2, mode):
estrain@0:     mean_1 = None
estrain@0:     mean_2 = None
estrain@0:     i = 1
estrain@0:     n_reads_1 = 0
estrain@0:     n_reads_2 = 0
estrain@0:     total_coverage_1 = 0
estrain@0:     total_coverage_2 = 0
estrain@0:     reads_1 = []
estrain@0:     reads_2 = []
estrain@0:     total_reads = 0
estrain@0:     if file.endswith(".gz"):
estrain@0:         file_content = io.BufferedReader(gzip.open(file))
estrain@0:     else:
estrain@0:         file_content = open(file, "r").readlines()
estrain@0:     for line in file_content:
estrain@0:         start = int((len(line) - k) // 2)
estrain@0:         if i % 4 == 2:
estrain@0:             total_reads += 1
estrain@0:             if file.endswith(".gz"):
estrain@0:                 s1 = line[start:k + start].decode()
estrain@0:                 line = line.decode()
estrain@0:             else:
estrain@0:                 s1 = line[start:k + start]
estrain@0:             if s1 in kmerDict_1:
estrain@0:                 n_reads_1 += 1
estrain@0:                 total_coverage_1 += len(line)
estrain@0:                 reads_1.append(line)
estrain@0:             if s1 in kmerDict_2:
estrain@0:                 n_reads_2 += 1
estrain@0:                 total_coverage_2 += len(line)
estrain@0:                 reads_2.append(line)
estrain@0:         i += 1
estrain@0:         if mode == 'quick':
estrain@0:             if total_coverage_2 >= 800000:
estrain@0:                 break
estrain@0: 
estrain@0:     if len(reads_1) == 0:
estrain@0:         kmer_Dict1 = {}
estrain@0:     else:
estrain@0:         kmer_Dict1 = createKmerDict_reads(reads_1, k)
estrain@0:         mers_1 = set([key for key in kmer_Dict1])
estrain@0:         mean_1 = sum([kmer_Dict1[key] for key in kmer_Dict1]) / len(mers_1)
estrain@0:     if len(reads_2) == 0:
estrain@0:         kmer_Dict2 = {}
estrain@0:     else:
estrain@0:         kmer_Dict2 = createKmerDict_reads(reads_2, k)
estrain@0:         mers_2 = set([key for key in kmer_Dict2])
estrain@0:         mean_2 = sum([kmer_Dict2[key] for key in kmer_Dict2]) / len(mers_2)
estrain@0:     return kmer_Dict1, kmer_Dict2, mean_1, mean_2, total_reads
estrain@0: 
estrain@0: 
estrain@0: def mean_cov_selected_kmers(iterable, kmer_dict, clade_specific_kmers):
estrain@0:     '''
estrain@0:     Given an iterable (list, set, dictrionary) returns mean coverage for the kmers in iterable
estrain@0:     :param iterable: set, list or dictionary containing kmers
estrain@0:     :param kmer_dict: dictionary with kmers as keys, kmer-frequency as value
estrain@0:     :param  clade_specific_kmers: list, dict or set of clade specific kmers
estrain@0:     :return: mean frequency as float
estrain@0:     '''
estrain@0:     if len(iterable) == 0:
estrain@0:         return 0
estrain@0:     return sum([kmer_dict[value] for value in iterable]) / len(clade_specific_kmers)
estrain@0: 
estrain@0: 
estrain@0: def kmer_lists(query_fastq_gz, k,
estrain@0:                allmers, allmers_rpoB,
estrain@0:                uniqmers_bongori,
estrain@0:                uniqmers_I,
estrain@0:                uniqmers_IIa,
estrain@0:                uniqmers_IIb,
estrain@0:                uniqmers_IIIa,
estrain@0:                uniqmers_IIIb,
estrain@0:                uniqmers_IV,
estrain@0:                uniqmers_VI,
estrain@0:                uniqmers_VII,
estrain@0:                uniqmers_VIII,
estrain@0:                uniqmers_bongori_rpoB,
estrain@0:                uniqmers_S_enterica_rpoB,
estrain@0:                uniqmers_Escherichia_rpoB,
estrain@0:                uniqmers_Listeria_ss_rpoB,
estrain@0:                uniqmers_Lmono_rpoB,
estrain@0:                mode):
estrain@0:     dict_invA, dict_rpoB, mean_invA, mean_rpoB, total_reads = target_read_kmerizer_multi(query_fastq_gz, k, allmers,
estrain@0:                                                                                          allmers_rpoB, mode)
estrain@0:     target_mers_invA = set([key for key in dict_invA])
estrain@0:     target_mers_rpoB = set([key for key in dict_rpoB])
estrain@0:     if target_mers_invA == 0:
estrain@0:         print('No reads found matching invA, no Salmonella in sample?')
estrain@0:     else:
estrain@0:         p_bongori = (len(uniqmers_bongori & target_mers_invA) / len(uniqmers_bongori)) * 100
estrain@0:         p_I = (len(uniqmers_I & target_mers_invA) / len(uniqmers_I)) * 100
estrain@0:         p_IIa = (len(uniqmers_IIa & target_mers_invA) / len(uniqmers_IIa)) * 100
estrain@0:         p_IIb = (len(uniqmers_IIb & target_mers_invA) / len(uniqmers_IIb)) * 100
estrain@0:         p_IIIa = (len(uniqmers_IIIa & target_mers_invA) / len(uniqmers_IIIa)) * 100
estrain@0:         p_IIIb = (len(uniqmers_IIIb & target_mers_invA) / len(uniqmers_IIIb)) * 100
estrain@0:         p_VI = (len(uniqmers_VI & target_mers_invA) / len(uniqmers_VI)) * 100
estrain@0:         p_IV = (len(uniqmers_IV & target_mers_invA) / len(uniqmers_IV)) * 100
estrain@0:         p_VII = (len(uniqmers_VII & target_mers_invA) / len(uniqmers_VII)) * 100
estrain@0:         p_VIII = (len(uniqmers_VIII & target_mers_invA) / len(uniqmers_VIII)) * 100
estrain@0:         p_bongori_rpoB = (len(uniqmers_bongori_rpoB & target_mers_rpoB) / len(uniqmers_bongori_rpoB)) * 100
estrain@0:         p_Senterica = (len(uniqmers_S_enterica_rpoB & target_mers_rpoB) / len(uniqmers_S_enterica_rpoB)) * 100
estrain@0:         p_Escherichia = (len(uniqmers_Escherichia_rpoB & target_mers_rpoB) / len(uniqmers_Escherichia_rpoB)) * 100
estrain@0:         p_Listeria_ss = (len(uniqmers_Listeria_ss_rpoB & target_mers_rpoB) / len(uniqmers_Listeria_ss_rpoB)) * 100
estrain@0:         p_Lmono = (len(uniqmers_Lmono_rpoB & target_mers_rpoB) / len(uniqmers_Lmono_rpoB)) * 100
estrain@0:         bongori_invA_cov = mean_cov_selected_kmers(uniqmers_bongori & target_mers_invA, dict_invA, uniqmers_bongori)
estrain@0:         I_invA_cov = mean_cov_selected_kmers(uniqmers_I & target_mers_invA, dict_invA, uniqmers_I)
estrain@0:         IIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIa & target_mers_invA, dict_invA, uniqmers_IIa)
estrain@0:         IIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIb & target_mers_invA, dict_invA, uniqmers_IIb)
estrain@0:         IIIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIIa & target_mers_invA, dict_invA, uniqmers_IIIa)
estrain@0:         IIIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIIb & target_mers_invA, dict_invA, uniqmers_IIIb)
estrain@0:         IV_invA_cov = mean_cov_selected_kmers(uniqmers_IV & target_mers_invA, dict_invA, uniqmers_IV)
estrain@0:         VI_invA_cov = mean_cov_selected_kmers(uniqmers_VI & target_mers_invA, dict_invA, uniqmers_VI)
estrain@0:         VII_invA_cov = mean_cov_selected_kmers(uniqmers_VII & target_mers_invA, dict_invA, uniqmers_VII)
estrain@0:         VIII_invA_cov = mean_cov_selected_kmers(uniqmers_VIII & target_mers_invA, dict_invA, uniqmers_VIII)
estrain@0:         S_enterica_rpoB_cov = mean_cov_selected_kmers((uniqmers_S_enterica_rpoB & target_mers_rpoB), dict_rpoB,
estrain@0:                                                       uniqmers_S_enterica_rpoB)
estrain@0:         S_bongori_rpoB_cov = mean_cov_selected_kmers((uniqmers_bongori_rpoB & target_mers_rpoB), dict_rpoB,
estrain@0:                                                      uniqmers_bongori_rpoB)
estrain@0:         Escherichia_rpoB_cov = mean_cov_selected_kmers((uniqmers_Escherichia_rpoB & target_mers_rpoB), dict_rpoB,
estrain@0:                                                        uniqmers_Escherichia_rpoB)
estrain@0:         Listeria_ss_rpoB_cov = mean_cov_selected_kmers((uniqmers_Listeria_ss_rpoB & target_mers_rpoB), dict_rpoB,
estrain@0:                                                        uniqmers_Listeria_ss_rpoB)
estrain@0:         Lmono_rpoB_cov = mean_cov_selected_kmers((uniqmers_Lmono_rpoB & target_mers_rpoB), dict_rpoB,
estrain@0:                                                  uniqmers_Lmono_rpoB)
estrain@0:         coverages = [Listeria_ss_rpoB_cov, Lmono_rpoB_cov, Escherichia_rpoB_cov, S_bongori_rpoB_cov,
estrain@0:                      S_enterica_rpoB_cov, bongori_invA_cov, I_invA_cov, IIa_invA_cov, IIb_invA_cov,
estrain@0:                      IIIa_invA_cov, IIIb_invA_cov, IV_invA_cov, VI_invA_cov, VII_invA_cov, VIII_invA_cov]
estrain@0:         locus_scores = [p_Listeria_ss, p_Lmono, p_Escherichia, p_bongori_rpoB, p_Senterica, p_bongori,
estrain@0:                         p_I, p_IIa, p_IIb, p_IIIa, p_IIIb, p_IV, p_VI, p_VII, p_VIII]
estrain@0:     return locus_scores, coverages, total_reads
estrain@0: 
estrain@0: 
estrain@0: def report_taxon(locus_covs, average_read_length, number_of_reads):
estrain@0:     list_taxa = [ 'Listeria ss', 'Listeria monocytogenes', 'Escherichia sp.',  # noqa: E201
estrain@0:                   'Salmonella bongori (rpoB)', 'Salmonella enterica (rpoB)',
estrain@0:                   'Salmonella bongori (invA)', 'S. enterica subsp. enterica (invA)',
estrain@0:                   'S. enterica subsp. salamae (invA: clade a)', 'S. enterica subsp. salamae (invA: clade b)',
estrain@0:                   'S. enterica subsp. arizonae (invA)', 'S. enterica subsp. diarizonae (invA)',
estrain@0:                   'S. enterica subsp. houtenae (invA)', 'S. enterica subsp. indica (invA)',
estrain@0:                   'S. enterica subsp. VII (invA)', 'S. enterica subsp. salamae (invA: clade VIII)' ]  # noqa: E202
estrain@0:     if sum(locus_covs) < 1:
estrain@0:         rpoB = ('No rpoB matches!', 0)
estrain@0:         invA = ('No invA matches!', 0)
estrain@0:         return rpoB, invA, 0.0
estrain@0:     else:
estrain@0:         # given list of scores get taxon
estrain@0:         if sum(locus_covs[0:5]) > 0:
estrain@0:             best_rpoB = max(range(len(locus_covs[1:5])), key=lambda x: locus_covs[1:5][x]) + 1
estrain@0:             all_rpoB = max(range(len(locus_covs[0:5])), key=lambda x: locus_covs[0:5][x])
estrain@0:             if (locus_covs[best_rpoB] != 0) & (all_rpoB == 0):
estrain@0:                 rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
estrain@0:             elif (all_rpoB == 0) & (round(sum(locus_covs[1:5]), 1) < 1):
estrain@0:                 rpoB = (list_taxa[0], locus_covs[0])
estrain@0:             else:
estrain@0:                 rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
estrain@0:         else:
estrain@0:             rpoB = ('No rpoB matches!', 0)
estrain@0:         if sum(locus_covs[5:]) > 0:
estrain@0:             best_invA = max(range(len(locus_covs[5:])), key=lambda x: locus_covs[5:][x]) + 5
estrain@0:             invA = (list_taxa[best_invA], locus_covs[best_invA])
estrain@0:         else:
estrain@0:             invA = ('No invA matches!', 0)
estrain@0:         if 'Listeria' in rpoB[0]:
estrain@0:             return rpoB, invA, (average_read_length * number_of_reads) / 3000000
estrain@0:         else:
estrain@0:             return rpoB, invA, (average_read_length * number_of_reads) / 5000000
estrain@0: 
estrain@0: 
estrain@0: def main():
estrain@0:     ex_dir = os.path.dirname(os.path.realpath(__file__))
estrain@0:     args = parse_args()
estrain@0:     input_file = args.input_file
estrain@0:     if input_file != 'None':
estrain@0:         files = [input_file]
estrain@0:     else:
estrain@0:         extension = args.extension
estrain@0:         inputdir = args.input_dir
estrain@0:         files = [inputdir + '/' + f for f in os.listdir(inputdir) if f.endswith(extension)]
estrain@0:     report = args.report
estrain@0:     mode = args.mode
estrain@0:     f_invA = open(ex_dir + "/invA_mers_dict", "rb")
estrain@0:     sets_dict_invA = pickle.load(f_invA)
estrain@0:     f_invA.close()
estrain@0:     allmers = sets_dict_invA['allmers']
estrain@0:     uniqmers_I = sets_dict_invA['uniqmers_I']
estrain@0:     uniqmers_IIa = sets_dict_invA['uniqmers_IIa']
estrain@0:     uniqmers_IIb = sets_dict_invA['uniqmers_IIb']
estrain@0:     uniqmers_IIIa = sets_dict_invA['uniqmers_IIIa']
estrain@0:     uniqmers_IIIb = sets_dict_invA['uniqmers_IIIb']
estrain@0:     uniqmers_IV = sets_dict_invA['uniqmers_IV']
estrain@0:     uniqmers_VI = sets_dict_invA['uniqmers_VI']
estrain@0:     uniqmers_VII = sets_dict_invA['uniqmers_VII']
estrain@0:     uniqmers_VIII = sets_dict_invA['uniqmers_VIII']
estrain@0:     uniqmers_bongori = sets_dict_invA['uniqmers_bongori']
estrain@0: 
estrain@0:     f = open(ex_dir + "/rpoB_mers_dict", "rb")
estrain@0:     sets_dict = pickle.load(f)
estrain@0:     f.close()
estrain@0: 
estrain@0:     allmers_rpoB = sets_dict['allmers']
estrain@0:     uniqmers_bongori_rpoB = sets_dict['uniqmers_bongori']
estrain@0:     uniqmers_S_enterica_rpoB = sets_dict['uniqmers_S_enterica']
estrain@0:     uniqmers_Escherichia_rpoB = sets_dict['uniqmers_Escherichia']
estrain@0:     uniqmers_Listeria_ss_rpoB = sets_dict['uniqmers_Listeria_ss']
estrain@0:     uniqmers_Lmono_rpoB = sets_dict['uniqmers_L_mono']
estrain@0:     # todo: run kmer_lists() once, create list of tuples containing data to be used fro different reports
estrain@0:     if report == 'taxonomy':
estrain@0:         print('file\trpoB\tinvA\texpected coverage')
estrain@0:         for f in files:
estrain@0:             locus_scores, coverages, reads = kmer_lists(f, 27,
estrain@0:                                                         allmers, allmers_rpoB,
estrain@0:                                                         uniqmers_bongori,
estrain@0:                                                         uniqmers_I,
estrain@0:                                                         uniqmers_IIa,
estrain@0:                                                         uniqmers_IIb,
estrain@0:                                                         uniqmers_IIIa,
estrain@0:                                                         uniqmers_IIIb,
estrain@0:                                                         uniqmers_IV,
estrain@0:                                                         uniqmers_VI,
estrain@0:                                                         uniqmers_VII,
estrain@0:                                                         uniqmers_VIII,
estrain@0:                                                         uniqmers_bongori_rpoB,
estrain@0:                                                         uniqmers_S_enterica_rpoB,
estrain@0:                                                         uniqmers_Escherichia_rpoB,
estrain@0:                                                         uniqmers_Listeria_ss_rpoB,
estrain@0:                                                         uniqmers_Lmono_rpoB,
estrain@0:                                                         mode)
estrain@0:             pretty_covs = [round(cov, 1) for cov in coverages]
estrain@0:             report = report_taxon(pretty_covs, get_av_read_length(f), reads)
estrain@0:             print(f.split('/')[-1] + '\t' + report[0][0] + '[' + str(report[0][1]) + ']' + '\t' + report[1][0] +
estrain@0:                   '[' + str(report[1][1]) + ']' +
estrain@0:                   '\t' + str(round(report[2], 1)))
estrain@0:     else:
estrain@0:         print(
estrain@0:         'file\tListeria sensu stricto (rpoB)\tL. monocytogenes (rpoB)\tEscherichia spp. (rpoB)\tS. bongori (rpoB)\tS. enterica' +  # noqa: E122
estrain@0:         '(rpoB)\tS. bongori (invA)\tsubsp. I (invA)\tsubsp. II (clade a: invA)\tsubsp. II' +  # noqa: E122
estrain@0:         ' (clade b: invA)\tsubsp. IIIa (invA)\tsubsp. IIIb (invA)\tsubsp.IV (invA)\tsubsp. VI (invA)\tsubsp. VII (invA)' +  # noqa: E122
estrain@0:         '\tsubsp. II (clade VIII : invA)')
estrain@0:         if report == 'percentage':
estrain@0:             for f in files:
estrain@0:                 locus_scores, coverages, reads = kmer_lists(f, 27,
estrain@0:                                                             allmers, allmers_rpoB,
estrain@0:                                                             uniqmers_bongori,
estrain@0:                                                             uniqmers_I,
estrain@0:                                                             uniqmers_IIa,
estrain@0:                                                             uniqmers_IIb,
estrain@0:                                                             uniqmers_IIIa,
estrain@0:                                                             uniqmers_IIIb,
estrain@0:                                                             uniqmers_IV,
estrain@0:                                                             uniqmers_VI,
estrain@0:                                                             uniqmers_VII,
estrain@0:                                                             uniqmers_VIII,
estrain@0:                                                             uniqmers_bongori_rpoB,
estrain@0:                                                             uniqmers_S_enterica_rpoB,
estrain@0:                                                             uniqmers_Escherichia_rpoB,
estrain@0:                                                             uniqmers_Listeria_ss_rpoB,
estrain@0:                                                             uniqmers_Lmono_rpoB,
estrain@0:                                                             mode)
estrain@0:                 pretty_scores = [str(round(score)) for score in locus_scores]
estrain@0:                 print(f.split('/')[-1] + '\t' + '\t'.join(pretty_scores))
estrain@0:         else:
estrain@0:             for f in files:
estrain@0:                 locus_scores, coverages, reads = kmer_lists(f, 27,
estrain@0:                                                             allmers, allmers_rpoB,
estrain@0:                                                             uniqmers_bongori,
estrain@0:                                                             uniqmers_I,
estrain@0:                                                             uniqmers_IIa,
estrain@0:                                                             uniqmers_IIb,
estrain@0:                                                             uniqmers_IIIa,
estrain@0:                                                             uniqmers_IIIb,
estrain@0:                                                             uniqmers_IV,
estrain@0:                                                             uniqmers_VI,
estrain@0:                                                             uniqmers_VII,
estrain@0:                                                             uniqmers_VIII,
estrain@0:                                                             uniqmers_bongori_rpoB,
estrain@0:                                                             uniqmers_S_enterica_rpoB,
estrain@0:                                                             uniqmers_Escherichia_rpoB,
estrain@0:                                                             uniqmers_Listeria_ss_rpoB,
estrain@0:                                                             uniqmers_Lmono_rpoB,
estrain@0:                                                             mode)
estrain@0:                 pretty_covs = [str(round(cov, 1)) for cov in coverages]
estrain@0:                 print(f.split('/')[-1] + '\t' + '\t'.join(pretty_covs))
estrain@0: 
estrain@0: 
estrain@0: if __name__ == '__main__':
estrain@0:     main()
estrain@0: