# HG changeset patch
# User estrain
# Date 1570063023 14400
# Node ID a51d3bce26129789db2ed5455ddcdffea07cf384
# Parent  613775dc3491426fbb9f5ea5cee7f01beafc917e
Uploaded

diff -r 613775dc3491 -r a51d3bce2612 Initial_Conditions.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Initial_Conditions.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+
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4,12:b:1,5', u'Broughton', u'Kolar', u'II 42:b:1,5', u'Canada', u'Kotte', u'Leopoldville', u'Ohio', u'II 6,7:b:[e,n,x]', u'Koumra', u'II [1],4,[5],12,[27]:b:[e,n,x]', u'Orbe', u'II 6,7:b:z39', u'Elbeuf', u'Vaugirard', u'Sedgwick', u'Wa', u'Melaka', u'Hull', u'Kirkee', u'Dahra', u'Mattenhof', u'II 17:b:e,n,x,z15', u'Wandsworth', u'II 17:b:z6', u'Glasgow', u'48:b:-', u'II 48:b:e,n,x,z15', u'II 48:b:z6', u'Shahalam', u'Riogrande', u'Saugus', u'Johannesburg', u'Duval', u'Benguella', u'II 40:b:-', u'Bignona', u'Paratyphi B', u'II 38:b:1,2', u'IV 50:b:-', u'II 50:b:z6', u'Rochdale', u'Rittersbach', u'Sica', u'Vietnam', u'Gnesta', u'VI 41:b:1,7', u'Abidjan', u'Limete', u'Adime', u'II 16:b:z42', u'II 16:b:z39', u'Malstatt', u'Visby', u'Edinburg', u'Brazzaville', u'Vegesack', u'Sangera', u'II 16:b:e,n,x', u'Hvittingfoss', u'II 41:b:1,5', u'Pharr', u'Eboko', u'Tonev', u'Sanga', u'Kuntair', u'Tucson', u'IIIb (6),14:b:e,n,x', u'Schleissheim (Schleissheim is extremely rare)', u'II 3,10:b:e,n,x', 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+
+remove_list=['Fulica', 'Schleissheim', 'Sendai', 'Blegdam', 'Naestved', 'Rostock', 'Moscow', 'Antarctica', 'Rosenberg', 'Chittagong', 'Bilu', 'Dessau', 'Cannonhill', 'Ilugun']
+
+rename_dict={'Nitra': 'Enteritidis',
+ 'Kiel': 'Dublin',
+ 'Koessen': 'Panama',
+ 'Phaliron': 'Kalumburu',
+ 'Istanbul': 'Hadar',
+ 'Haardt': 'Blockley',
+ 'Ferruch': 'Kottbus',
+ 'Sanga': 'Eboko',
+ 'Pakistan': 'Litchfield',
+ 'Bellevue': 'Lezennes',
+ 'Sunnycove': 'Daarle',
+ 'Noya': 'Akanji',
+ 'Virginia': 'Muenchen',
+ 'Djelfa': 'Skansen',
+ 'Konstanz': 'Gatuni',
+ 'Bardo': 'Newport',
+ 'Houston': 'Panama',
+ 'Martonos': 'Finkenwerder',
+ 'Midway': 'Florida',
+ 'Lindern': 'Charity',
+ 'Bahrenfeld': 'Onderstepoort',
+ 'Schalkwijk': 'Moussoro',
+ 'Amberg': 'Boecker',
+ 'Madelia': 'Carrau',
+ 'Soahanina': 'Sundsvall',
+ 'Stafford': 'Poano',
+ 'Chichiri': 'Uzaramo',
+ 'II 16:g,[m],[s],t:[e,n,x]': 'II 16:g,[m],[s],t:[1,5]',
+ 'Hindmarsh':'Bovismorbificans',
+ 'Yovokome': 'Manhattan'}
+
+ #potential merge for O22 and O23
+ #'Ibadan': 'Mississippi',
+ #'Bracknell': 'Oudwijk',
+ #'Vaertan': 'Ullevi',
+ #'Bahati': 'Durham',
+ #'Wichita': 'Friedenau',
+ #'Diguel': 'Telelkebir',
+ #'II 13,22:l,z28:1,5': 'II 13,23:l,z28:1,5',
+ #'Washington': 'Kintambo',
+ #'II 13,23:m,t:z42': 'II 13,22:m,t:z42:z39',
+ #'Serenli': 'Winslow',
+ #'Farmsen': 'Poona',
+ #'Durance': 'Ivrysurseine',
+ #'Agoueve': 'Cubana',
+ #'II 13,23:z29:1,5': 'II 13,22:z29:1,5',
+ #'II 13,23:z29:e,n,x': 'II 13,22:z29:e,n,x',
+ #'Picpus': 'Mampong',
+ #'Anna': 'Nimes',
+ #'Fanti': 'Leiden',
+ #'Ried': 'Ajiobo',
+
+ #potential O68 list
+ #'Djelfa': 'Skansen',
+ #'Korbol': 'Nagoya',
+ #'Sanga': 'Eboko',
+ #'Konstanz': 'Gatuni',
+ #'Presov': 'Shipley',
+ #'Heistopdenberg': 'Bukuru',
+ #'Tounouma': 'Banalia',
+ #'Gaillac': 'Utah',
+ #'Santiago': 'Belem',
+ #'Virginia': 'Muenchen',
+ #'Yovokome': 'Manhattan',
+ #'Portanigra': 'Dunkwa',
+ #'Bardo': 'Newport',
+ #'Ferruch': 'Kottbus',
+ #'Alminko': 'Nanergou',
+ #'Bargny': 'Takoradi',
+ #'Magherafelt': 'Cyprus',
+ #'Haardt': 'Blockley',
+ #'Pakistan': 'Litchfield',
+ #'Yokoe': 'Bassa',
+ #'Noya': 'Akanji',
+ #'Lamphun': 'Giza',
+ #'Tananarive': 'Brunei',
+ #'Inchpark': 'Alagbon',
+ #'Sunnycove': 'Daarle',
+ #'Sindelfingen': 'Benue',
+ #'Phaliron': 'Kalumburu',
+ #'Bazenheid': 'Zerifin',
+ #'Paris': 'Mapo',
+ #'Istanbul': 'Hadar',
+ #'Chomedey': 'Glostrup',
+ #'Wippra': 'Molade',
+ #'Uno': 'Tamale',
+ #'Kolda': 'Yarm',
+ #'Bellevue': 'Lezennes',
+ #'Albany':'Duesseldorf'
\ No newline at end of file
diff -r 613775dc3491 -r a51d3bce2612 LICENSE
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,339 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 2, June 1991
+ 
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
+ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The licenses for most software are designed to take away your
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+
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+   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+  0. This License applies to any program or other work which contains
+a notice placed by the copyright holder saying it may be distributed
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+Whether that is true depends on what the Program does.
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+
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+convey the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {description}
+    Copyright (C) {year}  {fullname}
+
+    This program is free software; you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation; either version 2 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License along
+    with this program; if not, write to the Free Software Foundation, Inc.,
+    51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+
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+
+    Gnomovision version 69, Copyright (C) year name of author
+    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, the commands you use may
+be called something other than `show w' and `show c'; they could even be
+mouse-clicks or menu items--whatever suits your program.
+
+You should also get your employer (if you work as a programmer) or your
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+
+  Yoyodyne, Inc., hereby disclaims all copyright interest in the program
+  `Gnomovision' (which makes passes at compilers) written by James Hacker.
+
+  {signature of Ty Coon}, 1 April 1989
+  Ty Coon, President of Vice
+
+This General Public License does not permit incorporating your program into
+proprietary programs.  If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.
diff -r 613775dc3491 -r a51d3bce2612 MANIFEST.in
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MANIFEST.in	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,10 @@
+include LICENSE
+include README.md
+include MANIFEST.in
+include version.py
+include setup.py
+include seqsero2_db/antigens.pickle
+include seqsero2_db/H_and_O_and_specific_genes.fasta
+include seqsero2_db/invA_mers_dict
+include seqsero2_db/special.pickle
+include bin/deinterleave_fastq.sh
diff -r 613775dc3491 -r a51d3bce2612 SalmID.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/SalmID.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,386 @@
+#!/usr/bin/env python3
+
+
+import gzip
+import io
+import pickle
+import os
+import sys
+
+from argparse import ArgumentParser
+try:
+    from .version import SalmID_version
+except ImportError:
+    SalmID_version = "version unknown"
+
+
+def reverse_complement(sequence):
+    """return the reverse complement of a nucleotide (including IUPAC ambiguous nuceotide codes)"""
+    complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N', 'M': 'K', 'R': 'Y', 'W': 'W',
+                            'S': 'S', 'Y': 'R', 'K': 'M', 'V': 'B', 'H': 'D', 'D': 'H', 'B': 'V'}
+    return "".join(complement[base] for base in reversed(sequence))
+
+
+def parse_args():
+    "Parse the input arguments, use '-h' for help."
+    parser = ArgumentParser(description='SalmID - rapid Kmer based Salmonella identifier from sequence data')
+    # inputs
+    parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + SalmID_version)
+    parser.add_argument(
+        '-i', '--input_file', type=str, required=False, default='None', metavar='your_fastqgz',
+        help='Single fastq.gz file input, include path to file if file is not in same directory ')
+    parser.add_argument(
+        '-e', '--extension', type=str, required=False, default='.fastq.gz', metavar='file_extension',
+        help='File extension, if specified without "--input_dir", SalmID will attempt to ID all files\n' +
+             ' with this extension in current directory, otherwise files in input directory')
+
+    parser.add_argument(
+        '-d', '--input_dir', type=str, required=False, default='.', metavar='directory',
+        help='Directory which contains data for identification, when not specified files in current directory will be analyzed.')
+    parser.add_argument(
+        '-r', '--report', type=str, required=False, default='percentage', metavar='percentage, coverage or taxonomy',
+        help='Report either percentage ("percentage") of clade specific kmers recovered, average kmer-coverage ("cov"), or '
+             'taxonomy (taxonomic species ID, plus observed mean k-mer coverages and expected coverage).')
+    parser.add_argument(
+        '-m', '--mode', type=str, required=False, default='quick', metavar='quick or thorough',
+        help='Quick [quick] or thorough [thorough] mode')
+    if len(sys.argv) == 1:
+        parser.print_help(sys.stderr)
+        sys.exit(1)
+    return parser.parse_args()
+
+
+def get_av_read_length(file):
+    """Samples the first 100 reads from a fastq file and return the average read length."""
+    i = 1
+    n_reads = 0
+    total_length = 0
+    if file.endswith(".gz"):
+        file_content = io.BufferedReader(gzip.open(file))
+    else:
+        file_content = open(file, "r").readlines()
+    for line in file_content:
+        if i % 4 == 2:
+            total_length += len(line.strip())
+            n_reads += 1
+        i += 1
+        if n_reads == 100:
+            break
+    return total_length / 100
+
+
+def createKmerDict_reads(list_of_strings, kmer):
+    """Count occurence of K-mers in a list of strings
+
+    Args:
+        list_of_strings(list of str): nucleotide sequences as a list of strings
+        kmer(int): length of the K-mer to count
+
+    Returns:
+        dict: dictionary with kmers as keys, counts for each kmer as values"""
+    kmer_table = {}
+    for string in list_of_strings:
+        sequence = string.strip('\n')
+        if len(sequence) >= kmer:
+            for i in range(len(sequence) - kmer + 1):
+                new_mer = sequence[i:i + kmer]
+                new_mer_rc = reverse_complement(new_mer)
+                if new_mer in kmer_table:
+                    kmer_table[new_mer.upper()] += 1
+                else:
+                    kmer_table[new_mer.upper()] = 1
+                if new_mer_rc in kmer_table:
+                    kmer_table[new_mer_rc.upper()] += 1
+                else:
+                    kmer_table[new_mer_rc.upper()] = 1
+    return kmer_table
+
+
+def target_read_kmerizer_multi(file, k, kmerDict_1, kmerDict_2, mode):
+    mean_1 = None
+    mean_2 = None
+    i = 1
+    n_reads_1 = 0
+    n_reads_2 = 0
+    total_coverage_1 = 0
+    total_coverage_2 = 0
+    reads_1 = []
+    reads_2 = []
+    total_reads = 0
+    if file.endswith(".gz"):
+        file_content = io.BufferedReader(gzip.open(file))
+    else:
+        file_content = open(file, "r").readlines()
+    for line in file_content:
+        start = int((len(line) - k) // 2)
+        if i % 4 == 2:
+            total_reads += 1
+            if file.endswith(".gz"):
+                s1 = line[start:k + start].decode()
+                line = line.decode()
+            else:
+                s1 = line[start:k + start]
+            if s1 in kmerDict_1:
+                n_reads_1 += 1
+                total_coverage_1 += len(line)
+                reads_1.append(line)
+            if s1 in kmerDict_2:
+                n_reads_2 += 1
+                total_coverage_2 += len(line)
+                reads_2.append(line)
+        i += 1
+        if mode == 'quick':
+            if total_coverage_2 >= 800000:
+                break
+
+    if len(reads_1) == 0:
+        kmer_Dict1 = {}
+    else:
+        kmer_Dict1 = createKmerDict_reads(reads_1, k)
+        mers_1 = set([key for key in kmer_Dict1])
+        mean_1 = sum([kmer_Dict1[key] for key in kmer_Dict1]) / len(mers_1)
+    if len(reads_2) == 0:
+        kmer_Dict2 = {}
+    else:
+        kmer_Dict2 = createKmerDict_reads(reads_2, k)
+        mers_2 = set([key for key in kmer_Dict2])
+        mean_2 = sum([kmer_Dict2[key] for key in kmer_Dict2]) / len(mers_2)
+    return kmer_Dict1, kmer_Dict2, mean_1, mean_2, total_reads
+
+
+def mean_cov_selected_kmers(iterable, kmer_dict, clade_specific_kmers):
+    '''
+    Given an iterable (list, set, dictrionary) returns mean coverage for the kmers in iterable
+    :param iterable: set, list or dictionary containing kmers
+    :param kmer_dict: dictionary with kmers as keys, kmer-frequency as value
+    :param  clade_specific_kmers: list, dict or set of clade specific kmers
+    :return: mean frequency as float
+    '''
+    if len(iterable) == 0:
+        return 0
+    return sum([kmer_dict[value] for value in iterable]) / len(clade_specific_kmers)
+
+
+def kmer_lists(query_fastq_gz, k,
+               allmers, allmers_rpoB,
+               uniqmers_bongori,
+               uniqmers_I,
+               uniqmers_IIa,
+               uniqmers_IIb,
+               uniqmers_IIIa,
+               uniqmers_IIIb,
+               uniqmers_IV,
+               uniqmers_VI,
+               uniqmers_VII,
+               uniqmers_VIII,
+               uniqmers_bongori_rpoB,
+               uniqmers_S_enterica_rpoB,
+               uniqmers_Escherichia_rpoB,
+               uniqmers_Listeria_ss_rpoB,
+               uniqmers_Lmono_rpoB,
+               mode):
+    dict_invA, dict_rpoB, mean_invA, mean_rpoB, total_reads = target_read_kmerizer_multi(query_fastq_gz, k, allmers,
+                                                                                         allmers_rpoB, mode)
+    target_mers_invA = set([key for key in dict_invA])
+    target_mers_rpoB = set([key for key in dict_rpoB])
+    if target_mers_invA == 0:
+        print('No reads found matching invA, no Salmonella in sample?')
+    else:
+        p_bongori = (len(uniqmers_bongori & target_mers_invA) / len(uniqmers_bongori)) * 100
+        p_I = (len(uniqmers_I & target_mers_invA) / len(uniqmers_I)) * 100
+        p_IIa = (len(uniqmers_IIa & target_mers_invA) / len(uniqmers_IIa)) * 100
+        p_IIb = (len(uniqmers_IIb & target_mers_invA) / len(uniqmers_IIb)) * 100
+        p_IIIa = (len(uniqmers_IIIa & target_mers_invA) / len(uniqmers_IIIa)) * 100
+        p_IIIb = (len(uniqmers_IIIb & target_mers_invA) / len(uniqmers_IIIb)) * 100
+        p_VI = (len(uniqmers_VI & target_mers_invA) / len(uniqmers_VI)) * 100
+        p_IV = (len(uniqmers_IV & target_mers_invA) / len(uniqmers_IV)) * 100
+        p_VII = (len(uniqmers_VII & target_mers_invA) / len(uniqmers_VII)) * 100
+        p_VIII = (len(uniqmers_VIII & target_mers_invA) / len(uniqmers_VIII)) * 100
+        p_bongori_rpoB = (len(uniqmers_bongori_rpoB & target_mers_rpoB) / len(uniqmers_bongori_rpoB)) * 100
+        p_Senterica = (len(uniqmers_S_enterica_rpoB & target_mers_rpoB) / len(uniqmers_S_enterica_rpoB)) * 100
+        p_Escherichia = (len(uniqmers_Escherichia_rpoB & target_mers_rpoB) / len(uniqmers_Escherichia_rpoB)) * 100
+        p_Listeria_ss = (len(uniqmers_Listeria_ss_rpoB & target_mers_rpoB) / len(uniqmers_Listeria_ss_rpoB)) * 100
+        p_Lmono = (len(uniqmers_Lmono_rpoB & target_mers_rpoB) / len(uniqmers_Lmono_rpoB)) * 100
+        bongori_invA_cov = mean_cov_selected_kmers(uniqmers_bongori & target_mers_invA, dict_invA, uniqmers_bongori)
+        I_invA_cov = mean_cov_selected_kmers(uniqmers_I & target_mers_invA, dict_invA, uniqmers_I)
+        IIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIa & target_mers_invA, dict_invA, uniqmers_IIa)
+        IIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIb & target_mers_invA, dict_invA, uniqmers_IIb)
+        IIIa_invA_cov = mean_cov_selected_kmers(uniqmers_IIIa & target_mers_invA, dict_invA, uniqmers_IIIa)
+        IIIb_invA_cov = mean_cov_selected_kmers(uniqmers_IIIb & target_mers_invA, dict_invA, uniqmers_IIIb)
+        IV_invA_cov = mean_cov_selected_kmers(uniqmers_IV & target_mers_invA, dict_invA, uniqmers_IV)
+        VI_invA_cov = mean_cov_selected_kmers(uniqmers_VI & target_mers_invA, dict_invA, uniqmers_VI)
+        VII_invA_cov = mean_cov_selected_kmers(uniqmers_VII & target_mers_invA, dict_invA, uniqmers_VII)
+        VIII_invA_cov = mean_cov_selected_kmers(uniqmers_VIII & target_mers_invA, dict_invA, uniqmers_VIII)
+        S_enterica_rpoB_cov = mean_cov_selected_kmers((uniqmers_S_enterica_rpoB & target_mers_rpoB), dict_rpoB,
+                                                      uniqmers_S_enterica_rpoB)
+        S_bongori_rpoB_cov = mean_cov_selected_kmers((uniqmers_bongori_rpoB & target_mers_rpoB), dict_rpoB,
+                                                     uniqmers_bongori_rpoB)
+        Escherichia_rpoB_cov = mean_cov_selected_kmers((uniqmers_Escherichia_rpoB & target_mers_rpoB), dict_rpoB,
+                                                       uniqmers_Escherichia_rpoB)
+        Listeria_ss_rpoB_cov = mean_cov_selected_kmers((uniqmers_Listeria_ss_rpoB & target_mers_rpoB), dict_rpoB,
+                                                       uniqmers_Listeria_ss_rpoB)
+        Lmono_rpoB_cov = mean_cov_selected_kmers((uniqmers_Lmono_rpoB & target_mers_rpoB), dict_rpoB,
+                                                 uniqmers_Lmono_rpoB)
+        coverages = [Listeria_ss_rpoB_cov, Lmono_rpoB_cov, Escherichia_rpoB_cov, S_bongori_rpoB_cov,
+                     S_enterica_rpoB_cov, bongori_invA_cov, I_invA_cov, IIa_invA_cov, IIb_invA_cov,
+                     IIIa_invA_cov, IIIb_invA_cov, IV_invA_cov, VI_invA_cov, VII_invA_cov, VIII_invA_cov]
+        locus_scores = [p_Listeria_ss, p_Lmono, p_Escherichia, p_bongori_rpoB, p_Senterica, p_bongori,
+                        p_I, p_IIa, p_IIb, p_IIIa, p_IIIb, p_IV, p_VI, p_VII, p_VIII]
+    return locus_scores, coverages, total_reads
+
+
+def report_taxon(locus_covs, average_read_length, number_of_reads):
+    list_taxa = [ 'Listeria ss', 'Listeria monocytogenes', 'Escherichia sp.',  # noqa: E201
+                  'Salmonella bongori (rpoB)', 'Salmonella enterica (rpoB)',
+                  'Salmonella bongori (invA)', 'S. enterica subsp. enterica (invA)',
+                  'S. enterica subsp. salamae (invA: clade a)', 'S. enterica subsp. salamae (invA: clade b)',
+                  'S. enterica subsp. arizonae (invA)', 'S. enterica subsp. diarizonae (invA)',
+                  'S. enterica subsp. houtenae (invA)', 'S. enterica subsp. indica (invA)',
+                  'S. enterica subsp. VII (invA)', 'S. enterica subsp. salamae (invA: clade VIII)' ]  # noqa: E202
+    if sum(locus_covs) < 1:
+        rpoB = ('No rpoB matches!', 0)
+        invA = ('No invA matches!', 0)
+        return rpoB, invA, 0.0
+    else:
+        # given list of scores get taxon
+        if sum(locus_covs[0:5]) > 0:
+            best_rpoB = max(range(len(locus_covs[1:5])), key=lambda x: locus_covs[1:5][x]) + 1
+            all_rpoB = max(range(len(locus_covs[0:5])), key=lambda x: locus_covs[0:5][x])
+            if (locus_covs[best_rpoB] != 0) & (all_rpoB == 0):
+                rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
+            elif (all_rpoB == 0) & (round(sum(locus_covs[1:5]), 1) < 1):
+                rpoB = (list_taxa[0], locus_covs[0])
+            else:
+                rpoB = (list_taxa[best_rpoB], locus_covs[best_rpoB])
+        else:
+            rpoB = ('No rpoB matches!', 0)
+        if sum(locus_covs[5:]) > 0:
+            best_invA = max(range(len(locus_covs[5:])), key=lambda x: locus_covs[5:][x]) + 5
+            invA = (list_taxa[best_invA], locus_covs[best_invA])
+        else:
+            invA = ('No invA matches!', 0)
+        if 'Listeria' in rpoB[0]:
+            return rpoB, invA, (average_read_length * number_of_reads) / 3000000
+        else:
+            return rpoB, invA, (average_read_length * number_of_reads) / 5000000
+
+
+def main():
+    ex_dir = os.path.dirname(os.path.realpath(__file__))
+    args = parse_args()
+    input_file = args.input_file
+    if input_file != 'None':
+        files = [input_file]
+    else:
+        extension = args.extension
+        inputdir = args.input_dir
+        files = [inputdir + '/' + f for f in os.listdir(inputdir) if f.endswith(extension)]
+    report = args.report
+    mode = args.mode
+    f_invA = open(ex_dir + "/invA_mers_dict", "rb")
+    sets_dict_invA = pickle.load(f_invA)
+    f_invA.close()
+    allmers = sets_dict_invA['allmers']
+    uniqmers_I = sets_dict_invA['uniqmers_I']
+    uniqmers_IIa = sets_dict_invA['uniqmers_IIa']
+    uniqmers_IIb = sets_dict_invA['uniqmers_IIb']
+    uniqmers_IIIa = sets_dict_invA['uniqmers_IIIa']
+    uniqmers_IIIb = sets_dict_invA['uniqmers_IIIb']
+    uniqmers_IV = sets_dict_invA['uniqmers_IV']
+    uniqmers_VI = sets_dict_invA['uniqmers_VI']
+    uniqmers_VII = sets_dict_invA['uniqmers_VII']
+    uniqmers_VIII = sets_dict_invA['uniqmers_VIII']
+    uniqmers_bongori = sets_dict_invA['uniqmers_bongori']
+
+    f = open(ex_dir + "/rpoB_mers_dict", "rb")
+    sets_dict = pickle.load(f)
+    f.close()
+
+    allmers_rpoB = sets_dict['allmers']
+    uniqmers_bongori_rpoB = sets_dict['uniqmers_bongori']
+    uniqmers_S_enterica_rpoB = sets_dict['uniqmers_S_enterica']
+    uniqmers_Escherichia_rpoB = sets_dict['uniqmers_Escherichia']
+    uniqmers_Listeria_ss_rpoB = sets_dict['uniqmers_Listeria_ss']
+    uniqmers_Lmono_rpoB = sets_dict['uniqmers_L_mono']
+    # todo: run kmer_lists() once, create list of tuples containing data to be used fro different reports
+    if report == 'taxonomy':
+        print('file\trpoB\tinvA\texpected coverage')
+        for f in files:
+            locus_scores, coverages, reads = kmer_lists(f, 27,
+                                                        allmers, allmers_rpoB,
+                                                        uniqmers_bongori,
+                                                        uniqmers_I,
+                                                        uniqmers_IIa,
+                                                        uniqmers_IIb,
+                                                        uniqmers_IIIa,
+                                                        uniqmers_IIIb,
+                                                        uniqmers_IV,
+                                                        uniqmers_VI,
+                                                        uniqmers_VII,
+                                                        uniqmers_VIII,
+                                                        uniqmers_bongori_rpoB,
+                                                        uniqmers_S_enterica_rpoB,
+                                                        uniqmers_Escherichia_rpoB,
+                                                        uniqmers_Listeria_ss_rpoB,
+                                                        uniqmers_Lmono_rpoB,
+                                                        mode)
+            pretty_covs = [round(cov, 1) for cov in coverages]
+            report = report_taxon(pretty_covs, get_av_read_length(f), reads)
+            print(f.split('/')[-1] + '\t' + report[0][0] + '[' + str(report[0][1]) + ']' + '\t' + report[1][0] +
+                  '[' + str(report[1][1]) + ']' +
+                  '\t' + str(round(report[2], 1)))
+    else:
+        print(
+        'file\tListeria sensu stricto (rpoB)\tL. monocytogenes (rpoB)\tEscherichia spp. (rpoB)\tS. bongori (rpoB)\tS. enterica' +  # noqa: E122
+        '(rpoB)\tS. bongori (invA)\tsubsp. I (invA)\tsubsp. II (clade a: invA)\tsubsp. II' +  # noqa: E122
+        ' (clade b: invA)\tsubsp. IIIa (invA)\tsubsp. IIIb (invA)\tsubsp.IV (invA)\tsubsp. VI (invA)\tsubsp. VII (invA)' +  # noqa: E122
+        '\tsubsp. II (clade VIII : invA)')
+        if report == 'percentage':
+            for f in files:
+                locus_scores, coverages, reads = kmer_lists(f, 27,
+                                                            allmers, allmers_rpoB,
+                                                            uniqmers_bongori,
+                                                            uniqmers_I,
+                                                            uniqmers_IIa,
+                                                            uniqmers_IIb,
+                                                            uniqmers_IIIa,
+                                                            uniqmers_IIIb,
+                                                            uniqmers_IV,
+                                                            uniqmers_VI,
+                                                            uniqmers_VII,
+                                                            uniqmers_VIII,
+                                                            uniqmers_bongori_rpoB,
+                                                            uniqmers_S_enterica_rpoB,
+                                                            uniqmers_Escherichia_rpoB,
+                                                            uniqmers_Listeria_ss_rpoB,
+                                                            uniqmers_Lmono_rpoB,
+                                                            mode)
+                pretty_scores = [str(round(score)) for score in locus_scores]
+                print(f.split('/')[-1] + '\t' + '\t'.join(pretty_scores))
+        else:
+            for f in files:
+                locus_scores, coverages, reads = kmer_lists(f, 27,
+                                                            allmers, allmers_rpoB,
+                                                            uniqmers_bongori,
+                                                            uniqmers_I,
+                                                            uniqmers_IIa,
+                                                            uniqmers_IIb,
+                                                            uniqmers_IIIa,
+                                                            uniqmers_IIIb,
+                                                            uniqmers_IV,
+                                                            uniqmers_VI,
+                                                            uniqmers_VII,
+                                                            uniqmers_VIII,
+                                                            uniqmers_bongori_rpoB,
+                                                            uniqmers_S_enterica_rpoB,
+                                                            uniqmers_Escherichia_rpoB,
+                                                            uniqmers_Listeria_ss_rpoB,
+                                                            uniqmers_Lmono_rpoB,
+                                                            mode)
+                pretty_covs = [str(round(cov, 1)) for cov in coverages]
+                print(f.split('/')[-1] + '\t' + '\t'.join(pretty_covs))
+
+
+if __name__ == '__main__':
+    main()
+
diff -r 613775dc3491 -r a51d3bce2612 SeqSero2_package.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/SeqSero2_package.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,1472 @@
+#!/usr/bin/env python3
+
+import sys
+import time
+import random
+import os
+import subprocess
+import gzip
+import io
+import pickle
+import argparse
+import itertools
+import math
+from distutils.version import LooseVersion
+from distutils.spawn import find_executable
+
+try:
+    from .version import SeqSero2_version
+except Exception: #ImportError
+    from version import SeqSero2_version
+
+### SeqSero Kmer
+def parse_args():
+    "Parse the input arguments, use '-h' for help."
+    parser = argparse.ArgumentParser(usage='SeqSero2_package.py -t <data_type> -m <mode> -i <input_data> [-d <output_directory>] [-p <number of threads>] [-b <BWA_algorithm>]\n\nDevelopper: Shaokang Zhang (zskzsk@uga.edu), Hendrik C Den-Bakker (Hendrik.DenBakker@uga.edu) and Xiangyu Deng (xdeng@uga.edu)\n\nContact email:seqsero@gmail.com\n\nVersion: v1.0.2')#add "-m <data_type>" in future
+    parser.add_argument("-i",nargs="+",help="<string>: path/to/input_data",type=os.path.abspath)  ### ed_SL_05282019: add 'type=os.path.abspath' to generate absolute path of input data.
+    parser.add_argument("-t",choices=['1','2','3','4','5','6'],help="<int>: '1' for interleaved paired-end reads, '2' for separated paired-end reads, '3' for single reads, '4' for genome assembly, '5' for nanopore fasta, '6' for nanopore fastq")
+    parser.add_argument("-b",choices=['sam','mem'],default="mem",help="<string>: algorithms for bwa mapping for allele mode; 'mem' for mem, 'sam' for samse/sampe; default=mem; optional; for now we only optimized for default 'mem' mode")
+    parser.add_argument("-p",default="1",help="<int>: number of threads for allele mode, if p >4, only 4 threads will be used for assembly since the amount of extracted reads is small, default=1")
+    parser.add_argument("-m",choices=['k','a'],default="a",help="<string>: which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a")
+    parser.add_argument("-d",help="<string>: output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number")
+    parser.add_argument("-c",action="store_true",help="<flag>: if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files")
+    parser.add_argument("--check",action="store_true",help="<flag>: use '--check' flag to check the required dependencies")
+    parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + SeqSero2_version)
+    return parser.parse_args()
+
+### ed_SL_05282019: check paths of dependencies
+check_dependencies = parse_args().check
+dependencies = ['bwa','samtools','blastn','fastq-dump','spades.py','bedtools','SalmID.py']
+if check_dependencies:
+    for item in dependencies:
+        ext_path = find_executable(item)
+        if ext_path is not None:
+            print ("Using "+item+" - "+ext_path)
+        else:
+            print ("ERROR: can not find "+item+" in PATH")
+    sys.exit()
+### end of --check
+
+def reverse_complement(sequence):
+    complement = {
+        'A': 'T',
+        'C': 'G',
+        'G': 'C',
+        'T': 'A',
+        'N': 'N',
+        'M': 'K',
+        'R': 'Y',
+        'W': 'W',
+        'S': 'S',
+        'Y': 'R',
+        'K': 'M',
+        'V': 'B',
+        'H': 'D',
+        'D': 'H',
+        'B': 'V'
+    }
+    return "".join(complement[base] for base in reversed(sequence))
+
+
+def createKmerDict_reads(list_of_strings, kmer):
+    kmer_table = {}
+    for string in list_of_strings:
+        sequence = string.strip('\n')
+        for i in range(len(sequence) - kmer + 1):
+            new_mer = sequence[i:i + kmer].upper()
+            new_mer_rc = reverse_complement(new_mer)
+            if new_mer in kmer_table:
+                kmer_table[new_mer.upper()] += 1
+            else:
+                kmer_table[new_mer.upper()] = 1
+            if new_mer_rc in kmer_table:
+                kmer_table[new_mer_rc.upper()] += 1
+            else:
+                kmer_table[new_mer_rc.upper()] = 1
+    return kmer_table
+
+
+def multifasta_dict(multifasta):
+    multifasta_list = [
+        line.strip() for line in open(multifasta, 'r') if len(line.strip()) > 0
+    ]
+    headers = [i for i in multifasta_list if i[0] == '>']
+    multifasta_dict = {}
+    for h in headers:
+        start = multifasta_list.index(h)
+        for element in multifasta_list[start + 1:]:
+            if element[0] == '>':
+                break
+            else:
+                if h[1:] in multifasta_dict:
+                    multifasta_dict[h[1:]] += element
+                else:
+                    multifasta_dict[h[1:]] = element
+    return multifasta_dict
+
+
+def multifasta_single_string(multifasta):
+    multifasta_list = [
+        line.strip() for line in open(multifasta, 'r')
+        if (len(line.strip()) > 0) and (line.strip()[0] != '>')
+    ]
+    return ''.join(multifasta_list)
+
+
+def chunk_a_long_sequence(long_sequence, chunk_size=60):
+    chunk_list = []
+    steps = len(long_sequence) // 60  #how many chunks
+    for i in range(steps):
+        chunk_list.append(long_sequence[i * chunk_size:(i + 1) * chunk_size])
+    chunk_list.append(long_sequence[steps * chunk_size:len(long_sequence)])
+    return chunk_list
+
+
+def target_multifasta_kmerizer(multifasta, k, kmerDict):
+    forward_length = 300  #if find the target, put forward 300 bases
+    reverse_length = 2200  #if find the target, put backward 2200 bases
+    chunk_size = 60  #it will firstly chunk the single long sequence to multiple smaller sequences, it controls the size of those smaller sequences
+    target_mers = []
+    long_single_string = multifasta_single_string(multifasta)
+    multifasta_list = chunk_a_long_sequence(long_single_string, chunk_size)
+    unit_length = len(multifasta_list[0])
+    forward_lines = int(forward_length / unit_length) + 1
+    reverse_lines = int(forward_length / unit_length) + 1
+    start_num = 0
+    end_num = 0
+    for i in range(len(multifasta_list)):
+        if i not in range(start_num, end_num):  #avoid computational repetition
+            line = multifasta_list[i]
+            start = int((len(line) - k) // 2)
+            s1 = line[start:k + start]
+            if s1 in kmerDict:  #detect it is a potential read or not (use the middle part)
+                if i - forward_lines >= 0:
+                    start_num = i - forward_lines
+                else:
+                    start_num = 0
+                if i + reverse_lines <= len(multifasta_list) - 1:
+                    end_num = i + reverse_lines
+                else:
+                    end_num = len(multifasta_list) - 1
+                target_list = [
+                    x.strip() for x in multifasta_list[start_num:end_num]
+                ]
+                target_line = "".join(target_list)
+                target_mers += [
+                    k1 for k1 in createKmerDict_reads([str(target_line)], k)
+                ]  ##changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length)
+        else:
+            pass
+    return set(target_mers)
+
+
+def target_read_kmerizer(file, k, kmerDict):
+    i = 1
+    n_reads = 0
+    total_coverage = 0
+    target_mers = []
+    if file.endswith(".gz"):
+        file_content = io.BufferedReader(gzip.open(file))
+    else:
+        file_content = open(file, "r").readlines()
+    for line in file_content:
+        start = int((len(line) - k) // 2)
+        if i % 4 == 2:
+            if file.endswith(".gz"):
+                s1 = line[start:k + start].decode()
+                line = line.decode()
+            else:
+                s1 = line[start:k + start]
+            if s1 in kmerDict:  #detect it is a potential read or not (use the middle part)
+                n_reads += 1
+                total_coverage += len(line)
+                target_mers += [
+                    k1 for k1 in createKmerDict_reads([str(line)], k)
+                ]  #changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length)
+        i += 1
+        if total_coverage >= 4000000:
+            break
+    return set(target_mers)
+
+
+def minion_fasta_kmerizer(file, k, kmerDict):
+    i = 1
+    n_reads = 0
+    total_coverage = 0
+    target_mers = {}
+    for line in open(file):
+        if i % 2 == 0:
+            for kmer, rc_kmer in kmers(line.strip().upper(), k):
+                if (kmer in kmerDict) or (rc_kmer in kmerDict):
+                    if kmer in target_mers:
+                        target_mers[kmer] += 1
+                    else:
+                        target_mers[kmer] = 1
+                    if rc_kmer in target_mers:
+                        target_mers[rc_kmer] += 1
+                    else:
+                        target_mers[rc_kmer] = 1
+        i += 1
+    return set([h for h in target_mers])
+
+
+def minion_fastq_kmerizer(file, k, kmerDict):
+    i = 1
+    n_reads = 0
+    total_coverage = 0
+    target_mers = {}
+    for line in open(file):
+        if i % 4 == 2:
+            for kmer, rc_kmer in kmers(line.strip().upper(), k):
+                if (kmer in kmerDict) or (rc_kmer in kmerDict):
+                    if kmer in target_mers:
+                        target_mers[kmer] += 1
+                    else:
+                        target_mers[kmer] = 1
+                    if rc_kmer in target_mers:
+                        target_mers[rc_kmer] += 1
+                    else:
+                        target_mers[rc_kmer] = 1
+        i += 1
+    return set([h for h in target_mers])
+
+
+def multifasta_single_string2(multifasta):
+    single_string = ''
+    with open(multifasta, 'r') as f:
+        for line in f:
+            if line.strip()[0] == '>':
+                pass
+            else:
+                single_string += line.strip()
+    return single_string
+
+
+def kmers(seq, k):
+    rev_comp = reverse_complement(seq)
+    for start in range(1, len(seq) - k + 1):
+        yield seq[start:start + k], rev_comp[-(start + k):-start]
+
+
+def multifasta_to_kmers_dict(multifasta,k_size):#used to create database kmer set
+    multi_seq_dict = multifasta_dict(multifasta)
+    lib_dict = {}
+    for h in multi_seq_dict:
+        lib_dict[h] = set(
+            [k for k in createKmerDict_reads([multi_seq_dict[h]], k_size)])
+    return lib_dict
+
+
+def Combine(b, c):
+    fliC_combinations = []
+    fliC_combinations.append(",".join(c))
+    temp_combinations = []
+    for i in range(len(b)):
+        for x in itertools.combinations(b, i + 1):
+            temp_combinations.append(",".join(x))
+    for x in temp_combinations:
+        temp = []
+        for y in c:
+            temp.append(y)
+        temp.append(x)
+        temp = ",".join(temp)
+        temp = temp.split(",")
+        temp.sort()
+        temp = ",".join(temp)
+        fliC_combinations.append(temp)
+    return fliC_combinations
+
+
+def seqsero_from_formula_to_serotypes(Otype, fliC, fljB, special_gene_list,subspecies):
+    #like test_output_06012017.txt
+    #can add more varialbles like sdf-type, sub-species-type in future (we can conclude it into a special-gene-list)
+    from Initial_Conditions import phase1,phase2,phaseO,sero,subs,remove_list,rename_dict
+    rename_dict_not_anymore=[rename_dict[x] for x in rename_dict]
+    rename_dict_all=rename_dict_not_anymore+list(rename_dict) #used for decide whether to 
+    seronames = []
+    seronames_none_subspecies=[]
+    for i in range(len(phase1)):
+        fliC_combine = []
+        fljB_combine = []
+        if phaseO[i] == Otype: # no VII in KW, but it's there
+            ### for fliC, detect every possible combinations to avoid the effect of "["
+            if phase1[i].count("[") == 0:
+                fliC_combine.append(phase1[i])
+            elif phase1[i].count("[") >= 1:
+                c = []
+                b = []
+                if phase1[i][0] == "[" and phase1[i][-1] == "]" and phase1[i].count(
+                        "[") == 1:
+                    content = phase1[i].replace("[", "").replace("]", "")
+                    fliC_combine.append(content)
+                    fliC_combine.append("-")
+                else:
+                    for x in phase1[i].split(","):
+                        if "[" in x:
+                            b.append(x.replace("[", "").replace("]", ""))
+                        else:
+                            c.append(x)
+                    fliC_combine = Combine(
+                        b, c
+                    )  #Combine will offer every possible combinations of the formula, like f,[g],t: f,t  f,g,t
+            ### end of fliC "[" detect
+            ### for fljB, detect every possible combinations to avoid the effect of "["
+            if phase2[i].count("[") == 0:
+                fljB_combine.append(phase2[i])
+            elif phase2[i].count("[") >= 1:
+                d = []
+                e = []
+                if phase2[i][0] == "[" and phase2[i][-1] == "]" and phase2[i].count(
+                        "[") == 1:
+                    content = phase2[i].replace("[", "").replace("]", "")
+                    fljB_combine.append(content)
+                    fljB_combine.append("-")
+                else:
+                    for x in phase2[i].split(","):
+                        if "[" in x:
+                            d.append(x.replace("[", "").replace("]", ""))
+                        else:
+                            e.append(x)
+                    fljB_combine = Combine(d, e)
+            ### end of fljB "[" detect
+            new_fliC = fliC.split(
+                ","
+            )  #because some antigen like r,[i] not follow alphabetical order, so use this one to judge and can avoid missings
+            new_fliC.sort()
+            new_fliC = ",".join(new_fliC)
+            new_fljB = fljB.split(",")
+            new_fljB.sort()
+            new_fljB = ",".join(new_fljB)
+            if (new_fliC in fliC_combine
+                    or fliC in fliC_combine) and (new_fljB in fljB_combine
+                                                  or fljB in fljB_combine):
+              ######start, remove_list,rename_dict, added on 11/11/2018
+              if sero[i] not in remove_list:
+                temp_sero=sero[i]
+                if temp_sero in rename_dict:
+                  temp_sero=rename_dict[temp_sero] #rename if in the rename list
+                if temp_sero not in seronames:#the new sero may already included, if yes, then not consider
+                  if subs[i] == subspecies:
+                    seronames.append(temp_sero)
+                  seronames_none_subspecies.append(temp_sero)
+                else:
+                  pass
+              else:
+                pass
+              ######end, added on 11/11/2018
+    #analyze seronames
+    subspecies_pointer=""
+    if len(seronames) == 0 and len(seronames_none_subspecies)!=0:
+      seronames=seronames_none_subspecies
+      subspecies_pointer="1"
+    if len(seronames) == 0:
+        seronames = [
+            "N/A (The predicted antigenic profile does not exist in the White-Kauffmann-Le Minor scheme)"
+        ]
+    star = ""
+    star_line = ""
+    if len(seronames) > 1:  #there are two possible predictions for serotypes
+        star = "*"
+        #changed 04072019
+        #star_line = "The predicted serotypes share the same general formula:\t" + Otype + ":" + fliC + ":" + fljB + "\n"
+    if subspecies_pointer=="1" and len(seronames_none_subspecies)!=0:
+      star="*"
+      star_line=" The predicted O and H antigens correspond to serotype '"+(" or ").join(seronames)+"' in the Kauffmann-White scheme. The predicted subspecies by SalmID (github.com/hcdenbakker/SalmID) may not be consistent with subspecies designation in the Kauffmann-White scheme." + star_line
+      #star_line="The formula with this subspieces prediction can't get a serotype in KW manual, and the serotyping prediction was made without considering it."+star_line
+    if  Otype=="":
+      Otype="-"
+    predict_form = Otype + ":" + fliC + ":" + fljB
+    predict_sero = (" or ").join(seronames)
+    ###special test for Enteritidis
+    if predict_form == "9:g,m:-":
+        sdf = "-"
+        for x in special_gene_list:
+            if x.startswith("sdf"):
+                sdf = "+"
+                #star_line="Detected sdf gene, a marker to differentiate Gallinarum and Enteritidis"
+                star_line=" sdf gene detected." # ed_SL_04152019: new output format
+        #predict_form = predict_form + " Sdf prediction:" + sdf
+        predict_form = predict_form #changed 04072019
+        if sdf == "-":
+            star = "*"
+            #star_line="Didn't detected sdf gene, a marker to differentiate Gallinarum and Enteritidis"
+            star_line=" sdf gene not detected." # ed_SL_04152019: new output format
+            #changed in 04072019, for new output
+            #star_line = "Additional characterization is necessary to assign a serotype to this strain.  Commonly circulating strains of serotype Enteritidis are sdf+, although sdf- strains of serotype Enteritidis are known to exist. Serotype Gallinarum is typically sdf- but should be quite rare. Sdf- strains of serotype Enteritidis and serotype Gallinarum can be differentiated by phenotypic profile or genetic criteria.\n"
+            #predict_sero = "Gallinarum/Enteritidis" #04132019, for new output requirement
+            predict_sero = "Gallinarum or Enteritidis" # ed_SL_04152019: new output format
+    ###end of special test for Enteritidis
+    elif predict_form == "4:i:-":
+        predict_sero = "I 4,[5],12:i:-" # ed_SL_09242019: change serotype name
+    elif predict_form == "4:r:-":
+        predict_sero = "4:r:-"
+    elif predict_form == "4:b:-": # ed_SL_09272019: change for new output format
+        predict_sero = "N/A (4:b:-)"
+    #elif predict_form == "8:e,h:1,2": #removed after official merge of newport and bardo
+        #predict_sero = "Newport"
+        #star = "*"
+        #star_line = "Serotype Bardo shares the same antigenic profile with Newport, but Bardo is exceedingly rare."
+    claim = " The serotype(s) is/are the only serotype(s) with the indicated antigenic profile currently recognized in the Kauffmann White Scheme. New serotypes can emerge and the possibility exists that this antigenic profile may emerge in a different subspecies.  Identification of strains to the subspecies level should accompany serotype determination; the same antigenic profile in different subspecies is considered different serotypes.\n"
+    if "N/A" in predict_sero:
+        claim = ""
+    #special test for Typhimurium
+    if "Typhimurium" in predict_sero or predict_form == "4:i:-":
+        normal = 0
+        mutation = 0
+        for x in special_gene_list:
+            if "oafA-O-4_full" in x:
+                normal = float(special_gene_list[x])
+            elif "oafA-O-4_5-" in x:
+                mutation = float(special_gene_list[x])
+        if normal > mutation:
+            pass
+        elif normal < mutation:
+            #predict_sero = predict_sero.strip() + "(O5-)"
+            predict_sero = predict_sero.strip() #diable special sero for new output requirement, 04132019
+            star = "*"
+            #star_line = "Detected the deletion of O5-."
+            star_line = " Detected a deletion that causes O5- variant of Typhimurium." # ed_SL_04152019: new output format
+        else:
+            pass
+    #special test for Paratyphi B
+    if "Paratyphi B" in predict_sero or predict_form == "4:b:-":
+        normal = 0
+        mutation = 0
+        for x in special_gene_list:
+            if "gntR-family-regulatory-protein_dt-positive" in x:
+                normal = float(special_gene_list[x])
+            elif "gntR-family-regulatory-protein_dt-negative" in x:
+                mutation = float(special_gene_list[x])
+        #print(normal,mutation)
+        if normal > mutation:
+            #predict_sero = predict_sero.strip() + "(dt+)" #diable special sero for new output requirement, 04132019
+            predict_sero = predict_sero.strip()+' var. L(+) tartrate+' if "Paratyphi B" in predict_sero else predict_sero.strip() # ed_SL_04152019: new output format
+            star = "*"
+            #star_line = "Didn't detect the SNP for dt- which means this isolate is a Paratyphi B variant L(+) tartrate(+)."
+            star_line = " The SNP that causes d-Tartrate nonfermentating phenotype of Paratyphi B was not detected. " # ed_SL_04152019: new output format
+        elif normal < mutation:
+            #predict_sero = predict_sero.strip() + "(dt-)" #diable special sero for new output requirement, 04132019
+            predict_sero = predict_sero.strip()
+            star = "*"
+            #star_line = "Detected the SNP for dt- which means this isolate is a systemic pathovar of Paratyphi B."
+            star_line = " Detected the SNP for d-Tartrate nonfermenting phenotype of Paratyphi B." # ed_SL_04152019: new output format
+        else:
+            star = "*"
+            #star_line = " Failed to detect the SNP for dt-, can't decide it's a Paratyphi B variant L(+) tartrate(+) or not."
+            star_line = " " ## ed_SL_05152019: do not report this situation.
+    #special test for O13,22 and O13,23
+    if Otype=="13":
+      #ex_dir = os.path.dirname(os.path.realpath(__file__))
+      ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019
+      f = open(ex_dir + '/special.pickle', 'rb')
+      special = pickle.load(f)
+      O22_O23=special['O22_O23']
+      if predict_sero.split(" or ")[0] in O22_O23[-1] and predict_sero.split(" or ")[0] not in rename_dict_all:#if in rename_dict_all, then it means already merged, no need to analyze 
+        O22_score=0
+        O23_score=0
+        for x in special_gene_list:
+            if "O:22" in x:
+                O22_score = O22_score+float(special_gene_list[x])
+            elif "O:23" in x:
+                O23_score = O23_score+float(special_gene_list[x])
+        #print(O22_score,O23_score)
+        for z in O22_O23[0]:
+          if predict_sero.split(" or ")[0] in z:
+            if O22_score > O23_score:
+              star = "*"
+              #star_line = "Detected O22 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019
+              predict_sero = z[0]
+            elif O22_score < O23_score:
+              star = "*"
+              #star_line = "Detected O23 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019
+              predict_sero = z[1]
+            else:
+              star = "*"
+              #star_line = "Fail to detect O22 and O23 differences." #diabled for new output requirement, 04132019
+    if " or " in predict_sero:
+      star_line = star_line + " The predicted serotypes share the same general formula:\t" + Otype + ":" + fliC + ":" + fljB + "\n"
+    #special test for O6,8 
+    #merge_O68_list=["Blockley","Bovismorbificans","Hadar","Litchfield","Manhattan","Muenchen"] #remove 11/11/2018, because already in merge list
+    #for x in merge_O68_list:
+    #  if x in predict_sero:
+    #    predict_sero=x
+    #    star=""
+    #    star_line=""
+    #special test for Montevideo; most of them are monophasic
+    #if "Montevideo" in predict_sero and "1,2,7" in predict_form: #remove 11/11/2018, because already in merge list
+      #star="*"
+      #star_line="Montevideo is almost always monophasic, having an antigen called for the fljB position may be a result of Salmonella-Salmonella contamination."
+    return predict_form, predict_sero, star, star_line, claim
+### End of SeqSero Kmer part
+
+### Begin of SeqSero2 allele prediction and output
+def xml_parse_score_comparision_seqsero(xmlfile):
+  #used to do seqsero xml analysis
+  from Bio.Blast import NCBIXML
+  handle=open(xmlfile)
+  handle=NCBIXML.parse(handle)
+  handle=list(handle)
+  List=[]
+  List_score=[]
+  List_ids=[]
+  List_query_region=[]
+  for i in range(len(handle)):
+    if len(handle[i].alignments)>0:
+      for j in range(len(handle[i].alignments)):
+        score=0
+        ids=0
+        cover_region=set() #fixed problem that repeated calculation leading percentage > 1
+        List.append(handle[i].query.strip()+"___"+handle[i].alignments[j].hit_def)
+        for z in range(len(handle[i].alignments[j].hsps)):
+          hsp=handle[i].alignments[j].hsps[z]
+          temp=set(range(hsp.query_start,hsp.query_end))
+          est_bit=(handle[i].ka_params[0]*hsp.score-math.log(handle[i].ka_params[1]))/(math.log(2))
+          if len(cover_region)==0:
+            cover_region=cover_region|temp
+            fraction=1
+          else:
+            fraction=1-len(cover_region&temp)/float(len(temp))
+            cover_region=cover_region|temp
+          if "last" in handle[i].query or "first" in handle[i].query:
+            #score+=hsp.bits*fraction
+            score+=est_bit*fraction
+            ids+=float(hsp.identities)/handle[i].query_length*fraction
+          else:
+            #score+=hsp.bits*fraction
+            score+=est_bit*fraction
+            ids+=float(hsp.identities)/handle[i].query_length*fraction
+        List_score.append(score)
+        List_ids.append(ids)
+        List_query_region.append(cover_region)
+  temp=zip(List,List_score,List_ids,List_query_region)
+  Final_list=sorted(temp, key=lambda d:d[1], reverse = True)
+  return Final_list
+
+
+def Uniq(L,sort_on_fre="none"): #return the uniq list and the count number
+  Old=L
+  L.sort()
+  L = [L[i] for i in range(len(L)) if L[i] not in L[:i]]
+  count=[]
+  for j in range(len(L)):
+    y=0
+    for x in Old:
+      if L[j]==x:
+        y+=1
+    count.append(y)
+  if sort_on_fre!="none":
+    d=zip(*sorted(zip(count, L)))
+    L=d[1]
+    count=d[0]
+  return (L,count)
+
+def judge_fliC_or_fljB_from_head_tail_for_one_contig(nodes_vs_score_list):
+  #used to predict it's fliC or fljB for one contig, based on tail and head score, but output the score difference,if it is very small, then not reliable, use blast score for whole contig to test
+  #this is mainly used for 
+  a=nodes_vs_score_list
+  fliC_score=0
+  fljB_score=0
+  for z in a:
+    if "fliC" in z[0]:
+      fliC_score+=z[1]
+    elif "fljB" in z[0]:
+      fljB_score+=z[1]
+  if fliC_score>=fljB_score:
+    role="fliC"
+  else:
+    role="fljB"
+  return (role,abs(fliC_score-fljB_score))
+
+def judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(node_name,Final_list,Final_list_passed):
+  #used to predict contig is fliC or fljB, if the differnce score value on above head_and_tail is less than 10 (quite small)
+  #also used when no head or tail got blasted score for the contig
+  role=""
+  for z in Final_list_passed:
+    if node_name in z[0]:
+      role=z[0].split("_")[0]
+      break
+  return role
+
+def fliC_or_fljB_judge_from_head_tail_sequence(nodes_list,tail_head_list,Final_list,Final_list_passed):
+  #nodes_list is the c created by c,d=Uniq(nodes) in below function
+  first_target=""
+  role_list=[]
+  for x in nodes_list:
+    a=[]
+    role=""
+    for y in tail_head_list:
+      if x in y[0]:
+        a.append(y)
+    if len(a)==4:
+      role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
+      if diff<20:
+        role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
+    elif len(a)==3:
+      ###however, if the one with highest score is the fewer one, compare their accumulation score
+      role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
+      if diff<20:
+        role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
+      ###end of above score comparison
+    elif len(a)==2:
+      #must on same node, if not, then decide with unit blast score, blast-score/length_of_special_sequence(30 or 37)
+      temp=[]
+      for z in a:
+        temp.append(z[0].split("_")[0])
+      m,n=Uniq(temp)#should only have one choice, but weird situation might occur too
+      if len(m)==1:
+        pass
+      else:
+        pass
+      role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
+      if diff<20:
+        role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
+        ###need to desgin a algorithm to guess most possible situation for nodes_list, See the situations of test evaluation
+    elif len(a)==1:
+      #that one
+      role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
+      if diff<20:
+        role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
+      #need to evaluate, in future, may set up a cut-off, if not met, then just find Final_list_passed best match,like when "a==0"
+    else:#a==0
+      #use Final_list_passed best match
+      for z in Final_list_passed:
+        if x in z[0]:
+          role=z[0].split("_")[0]
+          break
+    #print x,role,len(a)
+    role_list.append((role,x))
+  if len(role_list)==2:
+    if role_list[0][0]==role_list[1][0]:#this is the most cocmmon error, two antigen were assigned to same phase
+      #just use score to do a final test
+      role_list=[]
+      for x in nodes_list: 
+        role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
+        role_list.append((role,x))
+  return role_list
+
+def decide_contig_roles_for_H_antigen(Final_list,Final_list_passed):
+  #used to decide which contig is FliC and which one is fljB
+  contigs=[]
+  nodes=[]
+  for x in Final_list_passed:
+    if x[0].startswith("fl") and "last" not in x[0] and "first" not in x[0]:
+      nodes.append(x[0].split("___")[1].strip())
+  c,d=Uniq(nodes)#c is node_list
+  #print c
+  tail_head_list=[x for x in Final_list if ("last" in x[0] or "first" in x[0])]
+  roles=fliC_or_fljB_judge_from_head_tail_sequence(c,tail_head_list,Final_list,Final_list_passed)
+  return roles
+
+def decide_O_type_and_get_special_genes(Final_list,Final_list_passed):
+  #decide O based on Final_list
+  O_choice="?"
+  O_list=[]
+  special_genes={}
+  nodes=[]
+  for x in Final_list_passed:
+    if x[0].startswith("O-"):
+      nodes.append(x[0].split("___")[1].strip())
+    elif not x[0].startswith("fl"):
+      special_genes[x[0]]=x[2]#08172018, x[2] changed from x[-1]
+  #print "special_genes:",special_genes
+  c,d=Uniq(nodes)
+  #print "potential O antigen contig",c
+  final_O=[]
+  O_nodes_list=[]
+  for x in c:#c is the list for contigs
+    temp=0
+    for y in Final_list_passed:
+      if x in y[0] and y[0].startswith("O-"):
+        final_O.append(y)
+        break
+  ### O contig has the problem of two genes on same contig, so do additional test
+  potenial_new_gene=""
+  for x in final_O:
+    pointer=0 #for genes merged or not
+    #not consider O-1,3,19_not_in_3,10, too short compared with others
+    if "O-1,3,19_not_in_3,10" not in x[0] and int(x[0].split("__")[1].split("___")[0])*x[2]+850 <= int(x[0].split("length_")[1].split("_")[0]):#gene length << contig length; for now give 300*2 (for secureity can use 400*2) as flank region
+      pointer=x[0].split("___")[1].strip()#store the contig name
+      print(pointer)
+    if pointer!=0:#it has potential merge event
+      for y in Final_list:
+        if pointer in y[0] and y not in final_O and (y[1]>=int(y[0].split("__")[1].split("___")[0])*1.5 or (y[1]>=int(y[0].split("__")[1].split("___")[0])*y[2] and y[1]>=400)):#that's a realtively strict filter now; if passed, it has merge event and add one more to final_O
+          potenial_new_gene=y
+          #print(potenial_new_gene)
+          break
+  if potenial_new_gene!="":
+    print("two differnt genes in same contig, fix it for O antigen")
+    print(potenial_new_gene[:3])
+    pointer=0
+    for y in final_O:
+      if y[0].split("___")[-1]==potenial_new_gene[0].split("___")[-1]:
+        pointer=1
+    if pointer!=0: #changed to consider two genes in same contig
+      final_O.append(potenial_new_gene)
+  ### end of the two genes on same contig test
+  final_O=sorted(final_O,key=lambda x: x[2], reverse=True)#sorted
+  if len(final_O)==0 or (len(final_O)==1 and "O-1,3,19_not_in_3,10" in final_O[0][0]):
+    #print "$$$No Otype, due to no hit"#may need to be changed
+    O_choice="-"
+  else:
+    highest_O_coverage=max([float(x[0].split("_cov_")[-1].split("_")[0]) for x in final_O if "O-1,3,19_not_in_3,10" not in x[0]])
+    O_list=[]
+    O_list_less_contamination=[]
+    for x in final_O:
+      if not "O-1,3,19_not_in_3,10__130" in x[0]:#O-1,3,19_not_in_3,10 is too small, which may affect further analysis; to avoid contamination affect, use 0.15 of highest coverage as cut-off
+        O_list.append(x[0].split("__")[0])
+        O_nodes_list.append(x[0].split("___")[1])
+        if float(x[0].split("_cov_")[-1].split("_")[0])>highest_O_coverage*0.15:
+          O_list_less_contamination.append(x[0].split("__")[0])
+    ### special test for O9,46 and O3,10 family
+    if ("O-9,46_wbaV" in O_list or "O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254" in O_list) and O_list_less_contamination[0].startswith("O-9,"):#not sure should use and float(O9_wbaV)/float(num_1) > 0.1
+      if "O-9,46_wzy" in O_list:#and float(O946_wzy)/float(num_1) > 0.1
+        O_choice="O-9,46"
+        #print "$$$Most possilble Otype:  O-9,46"
+      elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1
+        O_choice="O-9,46,27"
+        #print "$$$Most possilble Otype:  O-9,46,27"
+      else:
+        O_choice="O-9"#next, detect O9 vs O2?
+        O2=0
+        O9=0
+        for z in special_genes:
+          if "tyr-O-9" in z:
+            O9=special_genes[z]
+          elif "tyr-O-2" in z:
+            O2=special_genes[z]
+        if O2>O9:
+          O_choice="O-2"
+        elif O2<O9:
+          pass
+        else:
+          pass
+          #print "$$$No suitable one, because can't distinct it's O-9 or O-2, but O-9 has a more possibility."
+    elif ("O-3,10_wzx" in O_list) and ("O-9,46_wzy" in O_list) and (O_list[0].startswith("O-3,10") or O_list_less_contamination[0].startswith("O-9,46_wzy")):#and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1
+      if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1
+        O_choice="O-3,10"
+        #print "$$$Most possilble Otype:  O-3,10 (contain O-3,10_not_in_1,3,19)"
+      else:
+        O_choice="O-1,3,19"
+        #print "$$$Most possilble Otype:  O-1,3,19 (not contain O-3,10_not_in_1,3,19)"
+    ### end of special test for O9,46 and O3,10 family
+    else:
+      try: 
+        max_score=0
+        for x in final_O:
+          if x[2]>=max_score and float(x[0].split("_cov_")[-1].split("_")[0])>highest_O_coverage*0.15:#use x[2],08172018, the "coverage identity = cover_length * identity"; also meet coverage threshold
+            max_score=x[2]#change from x[-1] to x[2],08172018
+            O_choice=x[0].split("_")[0]
+        if O_choice=="O-1,3,19":
+          O_choice=final_O[1][0].split("_")[0]
+        #print "$$$Most possilble Otype: ",O_choice
+      except:
+        pass
+        #print "$$$No suitable Otype, or failure of mapping (please check the quality of raw reads)"
+  #print "O:",O_choice,O_nodes_list
+  Otypes=[]
+  for x in O_list:
+    if x!="O-1,3,19_not_in_3,10":
+      if "O-9,46_" not in x:
+        Otypes.append(x.split("_")[0])
+      else:
+        Otypes.append(x.split("-from")[0])#O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254
+  #Otypes=[x.split("_")[0] for x in O_list if x!="O-1,3,19_not_in_3,10"]
+  Otypes_uniq,Otypes_fre=Uniq(Otypes)
+  contamination_O=""
+  if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19":
+    if len(Otypes_uniq)>2:
+      contamination_O="potential contamination from O antigen signals"
+  else:
+    if len(Otypes_uniq)>1:
+      if O_choice=="O-4" and len(Otypes_uniq)==2 and "O-9,46,27" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund
+        contamination_O=""
+      elif O_choice=="O-9,46" and len(Otypes_uniq)==2 and "O-9,46_wbaV" in Otypes_uniq and "O-9,46_wzy" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund
+        contamination_O=""
+      else:
+        contamination_O="potential contamination from O antigen signals"
+  return O_choice,O_nodes_list,special_genes,final_O,contamination_O,Otypes_uniq
+### End of SeqSero2 allele prediction and output
+
+def get_input_files(make_dir,input_file,data_type,dirpath):
+  #tell input files from datatype
+  #"<int>: '1'(pair-end reads, interleaved),'2'(pair-end reads, seperated),'3'(single-end reads), '4'(assembly),'5'(nanopore fasta),'6'(nanopore fastq)"
+  for_fq=""
+  rev_fq=""
+  os.chdir(make_dir)
+  if data_type=="1":
+    input_file=input_file[0].split("/")[-1]
+    if input_file.endswith(".sra"):
+      subprocess.check_call("fastq-dump --split-files "+input_file,shell=True)
+      for_fq=input_file.replace(".sra","_1.fastq")
+      rev_fq=input_file.replace(".sra","_2.fastq")
+    else:
+      core_id=input_file.split(".fastq")[0].split(".fq")[0]
+      for_fq=core_id+"_1.fastq"
+      rev_fq=core_id+"_2.fastq"
+      if input_file.endswith(".gz"):
+        subprocess.check_call("gzip -dc "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True)
+      else:
+        subprocess.check_call("cat "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True)
+  elif data_type=="2":
+    for_fq=input_file[0].split("/")[-1]
+    rev_fq=input_file[1].split("/")[-1]
+  elif data_type=="3":
+    input_file=input_file[0].split("/")[-1]
+    if input_file.endswith(".sra"):
+      subprocess.check_call("fastq-dump --split-files "+input_file,shell=True)
+      for_fq=input_file.replace(".sra","_1.fastq")
+    else:
+      for_fq=input_file
+  elif data_type in ["4","5","6"]:
+    for_fq=input_file[0].split("/")[-1]
+  os.chdir("..")
+  return for_fq,rev_fq
+
+def predict_O_and_H_types(Final_list,Final_list_passed,new_fasta):
+  #get O and H types from Final_list from blast parsing; allele mode
+  from Bio import SeqIO
+  fliC_choice="-"
+  fljB_choice="-"
+  fliC_contig="NA"
+  fljB_contig="NA"
+  fliC_region=set([0])
+  fljB_region=set([0,])
+  fliC_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length
+  fljB_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length
+  O_choice="-"#no need to decide O contig for now, should be only one
+  O_choice,O_nodes,special_gene_list,O_nodes_roles,contamination_O,Otypes_uniq=decide_O_type_and_get_special_genes(Final_list,Final_list_passed)#decide the O antigen type and also return special-gene-list for further identification
+  O_choice=O_choice.split("-")[-1].strip()
+  if (O_choice=="1,3,19" and len(O_nodes_roles)==1 and "1,3,19" in O_nodes_roles[0][0]) or O_choice=="":
+    O_choice="-"
+  H_contig_roles=decide_contig_roles_for_H_antigen(Final_list,Final_list_passed)#decide the H antigen contig is fliC or fljB
+  #add alignment locations, used for further selection, 03312019
+  for i in range(len(H_contig_roles)):
+    x=H_contig_roles[i]
+    for y in Final_list_passed:
+      if x[1] in y[0] and y[0].startswith(x[0]):
+        H_contig_roles[i]+=H_contig_roles[i]+(y[-1],)
+        break
+  log_file=open("SeqSero_log.txt","a")
+  extract_file=open("Extracted_antigen_alleles.fasta","a")
+  handle_fasta=list(SeqIO.parse(new_fasta,"fasta"))
+
+  #print("O_contigs:")
+  log_file.write("O_contigs:\n")
+  extract_file.write("#Sequences with antigen signals (if the micro-assembled contig only covers the flanking region, it will not be used for contamination analysis)\n")
+  extract_file.write("#O_contigs:\n")
+  for x in O_nodes_roles:
+    if "O-1,3,19_not_in_3,10" not in x[0]:#O-1,3,19_not_in_3,10 is just a small size marker
+      #print(x[0].split("___")[-1],x[0].split("__")[0],"blast score:",x[1],"identity%:",str(round(x[2]*100,2))+"%",str(min(x[-1]))+" to "+str(max(x[-1])))
+      log_file.write(x[0].split("___")[-1]+" "+x[0].split("__")[0]+"; "+"blast score: "+str(x[1])+" identity%: "+str(round(x[2]*100,2))+"%; alignment from "+str(min(x[-1]))+" to "+str(max(x[-1]))+" of antigen\n")
+      title=">"+x[0].split("___")[-1]+" "+x[0].split("__")[0]+"; "+"blast score: "+str(x[1])+" identity%: "+str(round(x[2]*100,2))+"%; alignment from "+str(min(x[-1]))+" to "+str(max(x[-1]))+" of antigen\n"      
+      seqs=""
+      for z in handle_fasta:
+        if x[0].split("___")[-1]==z.description:
+          seqs=str(z.seq)
+      extract_file.write(title+seqs+"\n")
+  if len(H_contig_roles)!=0:
+    highest_H_coverage=max([float(x[1].split("_cov_")[-1].split("_")[0]) for x in H_contig_roles]) #less than highest*0.1 would be regarded as contamination and noises, they will still be considered in contamination detection and logs, but not used as final serotype output
+  else:
+    highest_H_coverage=0
+  for x in H_contig_roles:
+    #if multiple choices, temporately select the one with longest length for now, will revise in further change
+    if "fliC" == x[0] and len(x[-1])>=fliC_length and x[1] not in O_nodes and float(x[1].split("_cov_")[-1].split("_")[0])>highest_H_coverage*0.13:#remember to avoid the effect of O-type contig, so should not in O_node list
+      fliC_contig=x[1]
+      fliC_length=len(x[-1])
+    elif "fljB" == x[0] and len(x[-1])>=fljB_length and x[1] not in O_nodes and float(x[1].split("_cov_")[-1].split("_")[0])>highest_H_coverage*0.13:
+      fljB_contig=x[1]
+      fljB_length=len(x[-1])
+  for x in Final_list_passed:
+    if fliC_choice=="-" and "fliC_" in x[0] and fliC_contig in x[0]:
+      fliC_choice=x[0].split("_")[1]
+    elif fljB_choice=="-" and "fljB_" in x[0] and fljB_contig in x[0]:
+      fljB_choice=x[0].split("_")[1]
+    elif fliC_choice!="-" and fljB_choice!="-":
+      break
+  #now remove contigs not in middle core part
+  first_allele="NA"
+  first_allele_percentage=0
+  for x in Final_list:
+    if x[0].startswith("fliC") or x[0].startswith("fljB"):
+      first_allele=x[0].split("__")[0] #used to filter those un-middle contigs
+      first_allele_percentage=x[2]
+      break 
+  additional_contigs=[]
+  for x in Final_list:
+    if first_allele in x[0]:
+      if (fliC_contig == x[0].split("___")[-1]): 
+        fliC_region=x[3]
+      elif fljB_contig!="NA" and (fljB_contig == x[0].split("___")[-1]):
+        fljB_region=x[3]
+      else:
+        if x[1]*1.1>int(x[0].split("___")[1].split("_")[3]):#loose threshold by multiplying 1.1
+          additional_contigs.append(x)
+        #else:
+          #print x[:3]
+  #we can just use the fljB region (or fliC depends on size), no matter set() or contain a large locations (without middle part); however, if none of them is fully assembled, use 500 and 1200 as conservative cut-off
+  if first_allele_percentage>0.9:
+    if len(fliC_region)>len(fljB_region) and (max(fljB_region)-min(fljB_region))>1000:
+      target_region=fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region)))) #fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region))))
+    elif len(fliC_region)<len(fljB_region) and (max(fliC_region)-min(fliC_region))>1000:
+      target_region=fliC_region|(fljB_region-set(range(min(fliC_region),max(fliC_region))))  #fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region))))
+    else:
+      target_region=set()#doesn't do anything
+  else:
+    target_region=set()#doesn't do anything
+  #print(target_region)
+  #print(additional_contigs)
+  target_region2=set(list(range(0,525))+list(range(1200,1700)))#I found to use 500 to 1200 as special region would be best
+  target_region=target_region2|target_region
+  for x in additional_contigs:
+    removal=0
+    contig_length=int(x[0].split("___")[1].split("length_")[-1].split("_")[0])
+    if fljB_contig not in x[0] and fliC_contig not in x[0] and len(target_region&x[3])/float(len(x[3]))>0.65 and contig_length*0.5<len(x[3])<contig_length*1.5: #consider length and alignment length for now, but very loose,0.5 and 1.5 as cut-off
+      removal=1
+    else:
+      if first_allele_percentage > 0.9 and float(x[0].split("__")[1].split("___")[0])*x[2]/len(x[-1])>0.96:#if high similiarity with middle part of first allele (first allele >0.9, already cover middle part)
+        removal=1
+      else:
+        pass
+    if removal==1:
+      for y in H_contig_roles:
+        if y[1] in x[0]:
+          H_contig_roles.remove(y)
+    else:
+      pass
+      #print(x[:3],contig_length,len(target_region&x[3])/float(len(x[3])),contig_length*0.5,len(x[3]),contig_length*1.5)
+  #end of removing none-middle contigs
+  #print("H_contigs:")
+  log_file.write("H_contigs:\n")
+  extract_file.write("#H_contigs:\n")
+  H_contig_stat=[]
+  H1_cont_stat={}
+  H2_cont_stat={}
+  for i in range(len(H_contig_roles)):
+    x=H_contig_roles[i]
+    a=0
+    for y in Final_list_passed:
+      if x[1] in y[0] and y[0].startswith(x[0]):
+        if "first" in y[0] or "last" in y[0]: #this is the final filter to decide it's fliC or fljB, if can't pass, then can't decide
+          for y in Final_list_passed: #it's impossible to has the "first" and "last" allele as prediction, so re-do it
+            if x[1] in y[0]:#it's very possible to be third phase allele, so no need to make it must be fliC or fljB
+              #print(x[1],"can't_decide_fliC_or_fljB",y[0].split("_")[1],"blast_score:",y[1],"identity%:",str(round(y[2]*100,2))+"%",str(min(y[-1]))+" to "+str(max(y[-1])))
+              log_file.write(x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n")
+              H_contig_roles[i]="can't decide fliC or fljB, may be third phase"
+              title=">"+x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antiten\n"
+              seqs=""
+              for z in handle_fasta:
+                if x[1]==z.description:
+                  seqs=str(z.seq)
+              extract_file.write(title+seqs+"\n")
+              break
+        else:
+          #print(x[1],x[0],y[0].split("_")[1],"blast_score:",y[1],"identity%:",str(round(y[2]*100,2))+"%",str(min(y[-1]))+" to "+str(max(y[-1])))
+          log_file.write(x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n")
+          title=">"+x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n"
+          seqs=""
+          for z in handle_fasta:
+            if x[1]==z.description:
+              seqs=str(z.seq)
+          extract_file.write(title+seqs+"\n")
+        if x[0]=="fliC":
+          if y[0].split("_")[1] not in H1_cont_stat:
+            H1_cont_stat[y[0].split("_")[1]]=y[2]
+          else:
+            H1_cont_stat[y[0].split("_")[1]]+=y[2]
+        if x[0]=="fljB":
+          if y[0].split("_")[1] not in H2_cont_stat:
+            H2_cont_stat[y[0].split("_")[1]]=y[2]
+          else:
+            H2_cont_stat[y[0].split("_")[1]]+=y[2]
+        break
+  #detect contaminations
+  #print(H1_cont_stat)
+  #print(H2_cont_stat)
+  H1_cont_stat_list=[x for x in H1_cont_stat if H1_cont_stat[x]>0.2]
+  H2_cont_stat_list=[x for x in H2_cont_stat if H2_cont_stat[x]>0.2]
+  contamination_H=""
+  if len(H1_cont_stat_list)>1 or len(H2_cont_stat_list)>1:
+    contamination_H="potential contamination from H antigen signals"
+  elif len(H2_cont_stat_list)==1 and fljB_contig=="NA":
+    contamination_H="potential contamination from H antigen signals, uncommon weak fljB signals detected"
+  #get additional antigens
+  """
+    if ("O-9,46_wbaV" in O_list or "O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254" in O_list) and O_list_less_contamination[0].startswith("O-9,"):#not sure should use and float(O9_wbaV)/float(num_1) > 0.1
+      if "O-9,46_wzy" in O_list:#and float(O946_wzy)/float(num_1) > 0.1
+        O_choice="O-9,46"
+        #print "$$$Most possilble Otype:  O-9,46"
+      elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1
+        O_choice="O-9,46,27"
+        #print "$$$Most possilble Otype:  O-9,46,27"
+    elif ("O-3,10_wzx" in O_list) and ("O-9,46_wzy" in O_list) and (O_list[0].startswith("O-3,10") or O_list_less_contamination[0].startswith("O-9,46_wzy")):#and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1
+      if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1
+        O_choice="O-3,10"
+        #print "$$$Most possilble Otype:  O-3,10 (contain O-3,10_not_in_1,3,19)"
+      else:
+        O_choice="O-1,3,19"
+        #print "$$$Most possilble Otype:  O-1,3,19 (not contain O-3,10_not_in_1,3,19)"
+    ### end of special test for O9,46 and O3,10 family
+
+    if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19":
+    if len(Otypes_uniq)>2:
+      contamination_O="potential contamination from O antigen signals"
+  else:
+    if len(Otypes_uniq)>1:
+      if O_choice=="O-4" and len(Otypes_uniq)==2 and "O-9,46,27" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund
+        contamination_O=""
+      elif O_choice=="O-9,46" and len(Otypes_uniq)==2 and "O-9,46_wbaV" in Otypes_uniq and "O-9,46_wzy" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund
+        contamination_O=""
+  """
+  additonal_antigents=[]
+  #print(contamination_O)
+  #print(contamination_H)
+  log_file.write(contamination_O+"\n")
+  log_file.write(contamination_H+"\n")
+  log_file.close()
+  return O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H,Otypes_uniq,H1_cont_stat_list,H2_cont_stat_list
+
+def get_input_K(input_file,lib_dict,data_type,k_size):
+  #kmer mode; get input_Ks from dict and data_type
+  kmers = []
+  for h in lib_dict:
+      kmers += lib_dict[h]
+  if data_type == '4':
+      input_Ks = target_multifasta_kmerizer(input_file, k_size, set(kmers))
+  elif data_type == '1' or data_type == '2' or data_type == '3':#set it for now, will change later
+      input_Ks = target_read_kmerizer(input_file, k_size, set(kmers))
+  elif data_type == '5':#minion_2d_fasta
+      input_Ks = minion_fasta_kmerizer(input_file, k_size, set(kmers))
+  if data_type == '6':#minion_2d_fastq
+      input_Ks = minion_fastq_kmerizer(input_file, k_size, set(kmers))
+  return input_Ks
+
+def get_kmer_dict(lib_dict,input_Ks):
+  #kmer mode; get predicted types
+  O_dict = {}
+  H_dict = {}
+  Special_dict = {}
+  for h in lib_dict:
+      score = (len(lib_dict[h] & input_Ks) / len(lib_dict[h])) * 100
+      if score > 1:  # Arbitrary cut-off for similarity score very low but seems necessary to detect O-3,10 in some cases
+          if h.startswith('O-') and score > 25:
+              O_dict[h] = score
+          if h.startswith('fl') and score > 40:
+              H_dict[h] = score
+          if (h[:2] != 'fl') and (h[:2] != 'O-'):
+              Special_dict[h] = score
+  return O_dict,H_dict,Special_dict
+
+def call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir):
+  log_file=open("SeqSero_log.txt","a")
+  log_file.write("O_scores:\n")
+  #call O:
+  highest_O = '-'
+  if len(O_dict) == 0:
+      pass
+  else:
+      for x in O_dict:
+          log_file.write(x+"\t"+str(O_dict[x])+"\n")
+      if ('O-9,46_wbaV__1002' in O_dict and O_dict['O-9,46_wbaV__1002']>70) or ("O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002" in O_dict and O_dict['O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002']>70):  # not sure should use and float(O9_wbaV)/float(num_1) > 0.1
+          if 'O-9,46_wzy__1191' in O_dict:  # and float(O946_wzy)/float(num_1) > 0.1
+              highest_O = "O-9,46"
+          elif "O-9,46,27_partial_wzy__1019" in O_dict:  # and float(O94627)/float(num_1) > 0.1
+              highest_O = "O-9,46,27"
+          else:
+              highest_O = "O-9"  # next, detect O9 vs O2?
+              O2 = 0
+              O9 = 0
+              for z in Special_dict:
+                  if "tyr-O-9" in z:
+                      O9 = float(Special_dict[z])
+                  if "tyr-O-2" in z:
+                      O2 = float(Special_dict[z])
+              if O2 > O9:
+                  highest_O = "O-2"
+      elif ("O-3,10_wzx__1539" in O_dict) and (
+              "O-9,46_wzy__1191" in O_dict
+      ):  # and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1
+          if "O-3,10_not_in_1,3,19__1519" in O_dict:  # and float(O310_no_1319)/float(num_1) > 0.1
+              highest_O = "O-3,10"
+          else:
+              highest_O = "O-1,3,19"
+      ### end of special test for O9,46 and O3,10 family
+      else:
+          try:
+              max_score = 0
+              for x in O_dict:
+                  if float(O_dict[x]) >= max_score:
+                      max_score = float(O_dict[x])
+                      highest_O = x.split("_")[0]
+              if highest_O == "O-1,3,19":
+                  highest_O = '-'
+                  max_score = 0
+                  for x in O_dict:
+                      if x == 'O-1,3,19_not_in_3,10__130':
+                          pass
+                      else:
+                          if float(O_dict[x]) >= max_score:
+                              max_score = float(O_dict[x])
+                              highest_O = x.split("_")[0]
+          except:
+              pass
+  #call_fliC:
+  if len(H_dict)!=0:
+    highest_H_score_both_BC=H_dict[max(H_dict.keys(), key=(lambda k: H_dict[k]))] #used to detect whether fljB existed or not
+  else:
+    highest_H_score_both_BC=0
+  highest_fliC = '-'
+  highest_fliC_raw = '-'
+  highest_Score = 0
+  log_file.write("\nH_scores:\n")
+  for s in H_dict:
+      log_file.write(s+"\t"+str(H_dict[s])+"\n")
+      if s.startswith('fliC'):
+          if float(H_dict[s]) > highest_Score:
+              highest_fliC = s.split('_')[1]
+              highest_fliC_raw = s
+              highest_Score = float(H_dict[s])
+  #call_fljB
+  highest_fljB = '-'
+  highest_fljB_raw = '-'
+  highest_Score = 0
+  for s in H_dict:
+      if s.startswith('fljB'):
+          if float(H_dict[s]) > highest_Score and float(H_dict[s]) > highest_H_score_both_BC * 0.65: #fljB is special, so use highest_H_score_both_BC to give a general estimate of coverage, currently 0.65 seems pretty good; the reason use a high (0.65) is some fliC and fljB shared with each other
+              highest_fljB = s.split('_')[1]
+              highest_fljB_raw = s
+              highest_Score = float(H_dict[s])
+  log_file.write("\nSpecial_scores:\n")
+  for s in Special_dict:
+    log_file.write(s+"\t"+str(Special_dict[s])+"\n")
+  log_file.close()
+  return highest_O,highest_fliC,highest_fljB
+
+def get_temp_file_names(for_fq,rev_fq):
+  #seqsero2 -a; get temp file names
+  sam=for_fq+".sam"
+  bam=for_fq+".bam"
+  sorted_bam=for_fq+"_sorted.bam"
+  mapped_fq1=for_fq+"_mapped.fq"
+  mapped_fq2=rev_fq+"_mapped.fq"
+  combined_fq=for_fq+"_combined.fq"
+  for_sai=for_fq+".sai"
+  rev_sai=rev_fq+".sai"
+  return sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai
+
+def map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode):
+  #seqsero2 -a; do mapping and sort
+  print("building database...")
+  subprocess.check_call("bwa index "+database+ " 2>> data_log.txt",shell=True)
+  print("mapping...")
+  if mapping_mode=="mem":
+    subprocess.check_call("bwa mem -k 17 -t "+threads+" "+database+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True)
+  elif mapping_mode=="sam":
+    if fnameB!="":
+      subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True)
+      subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameB+" > "+rev_sai+ " 2>> data_log.txt",shell=True)
+      subprocess.check_call("bwa sampe "+database+" "+for_sai+" "+ rev_sai+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True)
+    else:
+      subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True)
+      subprocess.check_call("bwa samse "+database+" "+for_sai+" "+for_fq+" > "+sam)
+  subprocess.check_call("samtools view -@ "+threads+" -F 4 -Sh "+sam+" > "+bam,shell=True)
+  ### check the version of samtools then use differnt commands
+  samtools_version=subprocess.Popen(["samtools"],stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+  out, err = samtools_version.communicate()
+  version = str(err).split("ersion:")[1].strip().split(" ")[0].strip()
+  print("check samtools version:",version)
+  ### end of samtools version check and its analysis
+  if LooseVersion(version)<=LooseVersion("1.2"):
+    subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" "+fnameA+"_sorted",shell=True)
+  else:
+    subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" >"+sorted_bam,shell=True)
+
+def extract_mapped_reads_and_do_assembly_and_blast(current_time,sorted_bam,combined_fq,mapped_fq1,mapped_fq2,threads,fnameA,fnameB,database,mapping_mode):
+  #seqsero2 -a; extract, assembly and blast
+  subprocess.check_call("bamToFastq -i "+sorted_bam+" -fq "+combined_fq,shell=True)
+  #print("fnameA:",fnameA)
+  #print("fnameB:",fnameB)
+  if fnameB!="":
+    subprocess.check_call("bamToFastq -i "+sorted_bam+" -fq "+mapped_fq1+" -fq2 "+mapped_fq2 + " 2>> data_log.txt",shell=True)#2> /dev/null if want no output
+  else:
+    pass
+  outdir=current_time+"_temp"
+  print("assembling...")
+  if int(threads)>4:
+    t="4"
+  else:
+    t=threads
+  if os.path.getsize(combined_fq)>100 and (fnameB=="" or os.path.getsize(mapped_fq1)>100):#if not, then it's "-:-:-"
+    if fnameB!="":
+      subprocess.check_call("spades.py --careful --pe1-s "+combined_fq+" --pe1-1 "+mapped_fq1+" --pe1-2 "+mapped_fq2+" -t "+t+" -o "+outdir+ " >> data_log.txt 2>&1",shell=True)
+    else:
+      subprocess.check_call("spades.py --careful --pe1-s "+combined_fq+" -t "+t+" -o "+outdir+ " >> data_log.txt 2>&1",shell=True)
+    #new_fasta=fnameA+"_"+database+"_"+mapping_mode+".fasta"
+    new_fasta=fnameA+"_"+database.split('/')[-1]+"_"+mapping_mode+".fasta" # ed_SL_09152019: change path to databse for packaging
+    subprocess.check_call("mv "+outdir+"/contigs.fasta "+new_fasta+ " 2> /dev/null",shell=True)
+    #os.system("mv "+outdir+"/scaffolds.fasta "+new_fasta+ " 2> /dev/null") contigs.fasta
+    subprocess.check_call("rm -rf "+outdir+ " 2> /dev/null",shell=True)
+    print("blasting...","\n")
+    xmlfile="blasted_output.xml"#fnameA+"-extracted_vs_"+database+"_"+mapping_mode+".xml"
+    subprocess.check_call('makeblastdb -in '+new_fasta+' -out '+new_fasta+'_db '+'-dbtype nucl >> data_log.txt 2>&1',shell=True) #temp.txt is to forbid the blast result interrupt the output of our program###1/27/2015
+    subprocess.check_call("blastn -query "+database+" -db "+new_fasta+"_db -out "+xmlfile+" -outfmt 5 >> data_log.txt 2>&1",shell=True)###1/27/2015; 08272018, remove "-word_size 10"
+  else:
+    xmlfile="NA"
+  return xmlfile,new_fasta
+
+def judge_subspecies(fnameA,dirpath):
+  #seqsero2 -a; judge subspecies on just forward raw reads fastq
+  salmID_output=subprocess.Popen("python " + dirpath + "/SalmID.py -i "+fnameA,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+  out, err = salmID_output.communicate()
+  out=out.decode("utf-8")
+  file=open("data_log.txt","a")
+  file.write(out)
+  file.close()
+  salm_species_scores=out.split("\n")[1].split("\t")[6:]
+  salm_species_results=out.split("\n")[0].split("\t")[6:]
+  max_score=0
+  max_score_index=1 #default is 1, means "I"
+  for i in range(len(salm_species_scores)):
+    if max_score<float(salm_species_scores[i]):
+      max_score=float(salm_species_scores[i])
+      max_score_index=i
+  prediction=salm_species_results[max_score_index].split(".")[1].strip().split(" ")[0]
+  if float(out.split("\n")[1].split("\t")[4]) > float(out.split("\n")[1].split("\t")[5]): #bongori and enterica compare
+    prediction="bongori" #if not, the prediction would always be enterica, since they are located in the later part
+  if max_score<10:
+    prediction="-"
+  return prediction
+
+def judge_subspecies_Kmer(Special_dict):
+  #seqsero2 -k;
+  max_score=0
+  prediction="-" #default should be I
+  for x in Special_dict:
+    if "mer" in x:
+      if max_score<float(Special_dict[x]):
+        max_score=float(Special_dict[x])
+        prediction=x.split("_")[-1].strip()
+      if x.split("_")[-1].strip()=="bongori" and float(Special_dict[x])>95:#if bongori already, then no need to test enterica
+        prediction="bongori"
+        break
+  return prediction
+
+def main():
+  #combine SeqSeroK and SeqSero2, also with SalmID
+  args = parse_args()
+  input_file = args.i
+  data_type = args.t
+  analysis_mode = args.m
+  mapping_mode=args.b
+  threads=args.p
+  make_dir=args.d
+  clean_mode=args.c
+  k_size=27 #will change for bug fixing
+  #database="H_and_O_and_specific_genes.fasta"
+  dirpath = os.path.abspath(os.path.dirname(os.path.realpath(__file__)))
+  ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019: add ex_dir for packaging
+  database=ex_dir+"/H_and_O_and_specific_genes.fasta" # ed_SL_09152019: change path to database for packaging
+  note="Note:"
+  NA_note=" This predicted serotype is not in the Kauffman-White scheme." # ed_SL_09272019: add for new output format
+  if len(sys.argv)==1:
+    subprocess.check_call(dirpath+"/SeqSero2_package.py -h",shell=True)#change name of python file
+  else:
+    request_id = time.strftime("%m_%d_%Y_%H_%M_%S", time.localtime())
+    request_id += str(random.randint(1, 10000000))
+    if make_dir is None:
+      make_dir="SeqSero_result_"+request_id
+    if os.path.isdir(make_dir):
+      pass
+    else:
+      subprocess.check_call(["mkdir",make_dir])
+    #subprocess.check_call("cp "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True)
+    #subprocess.check_call("ln -sr "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True)
+    subprocess.check_call("ln -f -s "+database+" "+" ".join(input_file)+" "+make_dir,shell=True) # ed_SL_09152019: change path to database for packaging 
+    #subprocess.check_call("ln -f -s "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True) ### ed_SL_05282019: use -f option to force the replacement of links, remove -r and use absolute path instead to avoid link issue (use 'type=os.path.abspath' in -i argument).
+  ############################begin the real analysis 
+    if analysis_mode=="a":
+      if data_type in ["1","2","3"]:#use allele mode
+        for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath)
+        os.chdir(make_dir)
+        ###add a function to tell input files
+        fnameA=for_fq.split("/")[-1]
+        fnameB=rev_fq.split("/")[-1]
+        current_time=time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
+        sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai=get_temp_file_names(fnameA,fnameB) #get temp files id
+        map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode) #do mapping and sort
+        xmlfile,new_fasta=extract_mapped_reads_and_do_assembly_and_blast(current_time,sorted_bam,combined_fq,mapped_fq1,mapped_fq2,threads,fnameA,fnameB,database,mapping_mode) #extract the mapped reads and do micro assembly and blast
+        if xmlfile=="NA":
+          O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H=("-","-","-",[],"","")
+        else:
+          Final_list=xml_parse_score_comparision_seqsero(xmlfile) #analyze xml and get parsed results
+          file=open("data_log.txt","a")
+          for x in Final_list:
+            file.write("\t".join(str(y) for y in x)+"\n")
+          file.close()
+          Final_list_passed=[x for x in Final_list if float(x[0].split("_cov_")[1].split("_")[0])>=0.9 and (x[1]>=int(x[0].split("__")[1]) or x[1]>=int(x[0].split("___")[1].split("_")[3]) or x[1]>1000)]
+          O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H,Otypes_uniq,H1_cont_stat_list,H2_cont_stat_list=predict_O_and_H_types(Final_list,Final_list_passed,new_fasta) #predict O, fliC and fljB
+        subspecies=judge_subspecies(fnameA,dirpath) #predict subspecies
+        ###output
+        predict_form,predict_sero,star,star_line,claim=seqsero_from_formula_to_serotypes(O_choice,fliC_choice,fljB_choice,special_gene_list,subspecies)
+        claim="" #04132019, disable claim for new report requirement
+        contamination_report=""
+        H_list=["fliC_"+x for x in H1_cont_stat_list if len(x)>0]+["fljB_"+x for x in H2_cont_stat_list if len(x)>0]
+        if contamination_O!="" and contamination_H=="":
+          contamination_report="#Potential inter-serotype contamination detected from O antigen signals. All O-antigens detected:"+"\t".join(Otypes_uniq)+"."
+        elif contamination_O=="" and contamination_H!="":
+          contamination_report="#Potential inter-serotype contamination detected or potential thrid H phase from H antigen signals. All H-antigens detected:"+"\t".join(H_list)+"."
+        elif contamination_O!="" and contamination_H!="":
+          contamination_report="#Potential inter-serotype contamination detected from both O and H antigen signals.All O-antigens detected:"+"\t".join(Otypes_uniq)+". All H-antigens detected:"+"\t".join(H_list)+"."
+        if contamination_report!="":
+          #contamination_report="potential inter-serotype contamination detected (please refer below antigen signal report for details)." #above contamination_reports are for back-up and bug fixing #web-based mode need to be re-used, 04132019
+          contamination_report=" Co-existence of multiple serotypes detected, indicating potential inter-serotype contamination. See 'Extracted_antigen_alleles.fasta' for detected serotype determinant alleles."
+        #claim="\n"+open("Extracted_antigen_alleles.fasta","r").read()#used to store H and O antigen sequeences #04132019, need to change if using web-version
+        ## ed_SL_09272019: change for new output format
+        #if contamination_report+star_line+claim=="": #0413, new output style
+        #  note=""
+        #else:
+        #  note="Note:"
+        if clean_mode:
+          subprocess.check_call("rm -rf ../"+make_dir,shell=True)
+          make_dir="none-output-directory due to '-c' flag"
+        else:
+          new_file=open("SeqSero_result.txt","w")
+          if O_choice=="":
+            O_choice="-"
+          if "N/A" not in predict_sero:
+            new_file.write("Output_directory:\t"+make_dir+"\n"+
+                           "Input files:\t"+"\t".join(input_file)+"\n"+
+                           "O antigen prediction:\t"+O_choice+"\n"+
+                           "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
+                           "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
+                           "Predicted subspecies:\t"+subspecies+"\n"+
+                           "Predicted antigenic profile:\t"+predict_form+"\n"+
+                           "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
+                           note+contamination_report+star_line+claim+"\n")#+##
+          else:
+            #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n"
+            star_line="" #04132019, for new output requirement, diable star_line if "NA" in output
+            new_file.write("Output_directory:\t"+make_dir+"\n"+
+                           "Input files:\t"+"\t".join(input_file)+"\n"+
+                           "O antigen prediction:\t"+O_choice+"\n"+
+                           "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
+                           "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
+                           "Predicted subspecies:\t"+subspecies+"\n"+
+                           "Predicted antigenic profile:\t"+predict_form+"\n"+
+                           "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
+                           note+NA_note+contamination_report+star_line+claim+"\n")#+##
+          new_file.close()
+          print("\n")
+          #subprocess.check_call("cat Seqsero_result.txt",shell=True)
+          #subprocess.call("rm H_and_O_and_specific_genes.fasta* *.sra *.bam *.sam *.fastq *.gz *.fq temp.txt *.xml "+fnameA+"*_db* 2> /dev/null",shell=True)
+          subprocess.call("rm H_and_O_and_specific_genes.fasta* *.sra *.bam *.sam *.fastq *.gz *.fq temp.txt "+fnameA+"*_db* 2> /dev/null",shell=True)
+        if "N/A" not in predict_sero:
+          #print("Output_directory:"+make_dir+"\nInput files:\t"+for_fq+" "+rev_fq+"\n"+"O antigen prediction:\t"+O_choice+"\n"+"H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+"H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+"Predicted antigenic profile:\t"+predict_form+"\n"+"Predicted subspecies:\t"+subspecies+"\n"+"Predicted serotype(s):\t"+predict_sero+star+"\nNote:"+contamination_report+star+star_line+claim+"\n")#+##
+          print("Output_directory:\t"+make_dir+"\n"+
+                "Input files:\t"+"\t".join(input_file)+"\n"+
+                "O antigen prediction:\t"+O_choice+"\n"+
+                "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
+                "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
+                "Predicted subspecies:\t"+subspecies+"\n"+
+                "Predicted antigenic profile:\t"+predict_form+"\n"+
+                "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
+                note+contamination_report+star_line+claim+"\n")#+##
+        else:
+          print("Output_directory:\t"+make_dir+"\n"+
+                "Input files:\t"+"\t".join(input_file)+"\n"+
+                "O antigen prediction:\t"+O_choice+"\n"+
+                "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
+                "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
+                "Predicted subspecies:\t"+subspecies+"\n"+
+                "Predicted antigenic profile:\t"+predict_form+"\n"+
+                "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
+                note+NA_note+contamination_report+star_line+claim+"\n")
+      else:
+        print("Allele modes only support raw reads datatype, i.e. '-t 1 or 2 or 3'; please use '-m k'")
+    elif analysis_mode=="k":
+      #ex_dir = os.path.dirname(os.path.realpath(__file__))
+      ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019: change ex_dir for packaging
+      #output_mode = args.mode
+      for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath)
+      input_file = for_fq #-k will just use forward because not all reads were used
+      os.chdir(make_dir)
+      f = open(ex_dir + '/antigens.pickle', 'rb')
+      lib_dict = pickle.load(f)
+      f.close
+      input_Ks=get_input_K(input_file,lib_dict,data_type,k_size)
+      O_dict,H_dict,Special_dict=get_kmer_dict(lib_dict,input_Ks)
+      highest_O,highest_fliC,highest_fljB=call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir)
+      subspecies=judge_subspecies_Kmer(Special_dict)
+      if subspecies=="IIb" or subspecies=="IIa":
+        subspecies="II"
+      predict_form,predict_sero,star,star_line,claim = seqsero_from_formula_to_serotypes(
+          highest_O.split('-')[1], highest_fliC, highest_fljB, Special_dict,subspecies)
+      claim="" #no claim any more based on new output requirement
+      ## ed_SL_09272019: change for new output format
+      #if star_line+claim=="": #0413, new output style
+      #  note=""
+      #else:
+      #  note="Note:"
+      if clean_mode:
+        subprocess.check_call("rm -rf ../"+make_dir,shell=True)
+        make_dir="none-output-directory due to '-c' flag"
+        ### ed_SL_05282019, fix the assignment issue of variable 'O_choice' using "-m k -c"
+        if highest_O.split('-')[-1]=="":
+          O_choice="-"
+        else:
+          O_choice=highest_O.split('-')[-1]
+        ###
+      else:
+        if highest_O.split('-')[-1]=="":
+          O_choice="-"
+        else:
+          O_choice=highest_O.split('-')[-1]
+        #print("Output_directory:"+make_dir+"\tInput_file:"+input_file+"\tPredicted subpecies:"+subspecies + '\tPredicted antigenic profile:' + predict_form + '\tPredicted serotype(s):' + predict_sero)
+        new_file=open("SeqSero_result.txt","w")
+        #new_file.write("Output_directory:"+make_dir+"\nInput files:\t"+input_file+"\n"+"O antigen prediction:\t"+O_choice+"\n"+"H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+"H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+"Predicted antigenic profile:\t"+predict_form+"\n"+"Predicted subspecies:\t"+subspecies+"\n"+"Predicted serotype(s):\t"+predict_sero+star+"\n"+star+star_line+claim+"\n")#+##
+        if "N/A" not in predict_sero:
+          new_file.write("Output_directory:\t"+make_dir+"\n"+
+                         "Input files:\t"+input_file+"\n"+
+                         "O antigen prediction:\t"+O_choice+"\n"+
+                         "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
+                         "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
+                         "Predicted subspecies:\t"+subspecies+"\n"+
+                         "Predicted antigenic profile:\t"+predict_form+"\n"+
+                         "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
+                         note+star_line+claim+"\n")#+##
+        else:
+          #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n"
+          star_line = "" #changed for new output requirement, 04132019
+          new_file.write("Output_directory:\t"+make_dir+"\n"+
+                         "Input files:\t"+input_file+"\n"+
+                         "O antigen prediction:\t"+O_choice+"\n"+
+                         "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
+                         "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
+                         "Predicted subspecies:\t"+subspecies+"\n"+
+                         "Predicted antigenic profile:\t"+predict_form+"\n"+
+                         "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
+                         note+NA_note+star_line+claim+"\n")#+##
+        new_file.close()
+        subprocess.call("rm *.fasta* *.fastq *.gz *.fq temp.txt *.sra 2> /dev/null",shell=True)
+      if "N/A" not in predict_sero:
+        print("Output_directory:\t"+make_dir+"\n"+
+              "Input files:\t"+input_file+"\n"+
+              "O antigen prediction:\t"+O_choice+"\n"+
+              "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
+              "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
+              "Predicted subspecies:\t"+subspecies+"\n"+
+              "Predicted antigenic profile:\t"+predict_form+"\n"+
+              "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
+              note+star_line+claim+"\n")#+##
+      else:
+        print("Output_directory:\t"+make_dir+"\n"+
+              "Input files:\t"+input_file+"\n"+
+              "O antigen prediction:\t"+O_choice+"\n"+
+              "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
+              "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
+              "Predicted subspecies:\t"+subspecies+"\n"+
+              "Predicted antigenic profile:\t"+predict_form+"\n"+
+              "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
+              note+NA_note+star_line+claim+"\n")#+##
+
+if __name__ == '__main__':
+  main()
diff -r 613775dc3491 -r a51d3bce2612 SeqSero2_update_kmer_database.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/SeqSero2_update_kmer_database.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+
+import argparse
+import os,subprocess
+import pickle
+
+### SeqSero Kmer
+def parse_args():
+    "Parse the input arguments, use '-h' for help."
+    parser = argparse.ArgumentParser(usage='Just type "SeqSero2_update_kmer_database.py", it will update kmer database automatically')
+    return parser.parse_args()
+
+def reverse_complement(sequence):
+    complement = {
+        'A': 'T',
+        'C': 'G',
+        'G': 'C',
+        'T': 'A',
+        'N': 'N',
+        'M': 'K',
+        'R': 'Y',
+        'W': 'W',
+        'S': 'S',
+        'Y': 'R',
+        'K': 'M',
+        'V': 'B',
+        'H': 'D',
+        'D': 'H',
+        'B': 'V'
+    }
+    return "".join(complement[base] for base in reversed(sequence))
+
+def multifasta_dict(multifasta):
+    multifasta_list = [
+        line.strip() for line in open(multifasta, 'r') if len(line.strip()) > 0
+    ]
+    headers = [i for i in multifasta_list if i[0] == '>']
+    multifasta_dict = {}
+    for h in headers:
+        start = multifasta_list.index(h)
+        for element in multifasta_list[start + 1:]:
+            if element[0] == '>':
+                break
+            else:
+                if h[1:] in multifasta_dict:
+                    multifasta_dict[h[1:]] += element
+                else:
+                    multifasta_dict[h[1:]] = element
+    return multifasta_dict
+
+def createKmerDict_reads(list_of_strings, kmer):
+    kmer_table = {}
+    for string in list_of_strings:
+        sequence = string.strip('\n')
+        for i in range(len(sequence) - kmer + 1):
+            new_mer = sequence[i:i + kmer].upper()
+            new_mer_rc = reverse_complement(new_mer)
+            if new_mer in kmer_table:
+                kmer_table[new_mer.upper()] += 1
+            else:
+                kmer_table[new_mer.upper()] = 1
+            if new_mer_rc in kmer_table:
+                kmer_table[new_mer_rc.upper()] += 1
+            else:
+                kmer_table[new_mer_rc.upper()] = 1
+    return kmer_table
+
+def multifasta_to_kmers_dict(multifasta):
+    multi_seq_dict = multifasta_dict(multifasta)
+    lib_dict = {}
+    for h in multi_seq_dict:
+        lib_dict[h] = set(
+            [k for k in createKmerDict_reads([multi_seq_dict[h]], 27)])
+    return lib_dict
+
+def get_salmid_invA_database(ex_dir):
+  # read invA kmer and return it
+  a = open(ex_dir + '/invA_mers_dict', 'rb')
+  invA_dict = pickle.load(a)
+  try:
+    del invA_dict['version']
+  except:
+    pass
+  return invA_dict
+
+def get_salmid_rpoB_database(ex_dir):
+  # read invA kmer and return it
+  a = open(ex_dir + '/rpoB_mers_dict', 'rb')
+  rpoB_dict = pickle.load(a)
+  try:
+    del rpoB_dict['version']
+  except:
+    pass
+  return rpoB_dict
+
+def main():
+  args = parse_args()
+  ex_dir = os.path.dirname(os.path.realpath(__file__))
+  lib_dict = multifasta_to_kmers_dict(ex_dir + '/H_and_O_and_specific_genes.fasta')
+  invA_dict=get_salmid_invA_database(ex_dir)
+  #rpoB_dict=get_salmid_rpoB_database(ex_dir)
+  lib_dict_new = lib_dict.copy()
+  #print(len(lib_dict_new))
+  lib_dict_new.update(invA_dict)
+  #print(len(lib_dict_new))
+  #lib_dict_new.update(rpoB_dict)
+  #print(len(lib_dict_new))
+  f = open(ex_dir + '/antigens.pickle', "wb")
+  pickle.dump(lib_dict_new, f)
+  f.close()
+
+if __name__ == '__main__':
+  main()
diff -r 613775dc3491 -r a51d3bce2612 __pycache__/Initial_Conditions.cpython-34.pyc
Binary file __pycache__/Initial_Conditions.cpython-34.pyc has changed
diff -r 613775dc3491 -r a51d3bce2612 __pycache__/version.cpython-34.pyc
Binary file __pycache__/version.cpython-34.pyc has changed
diff -r 613775dc3491 -r a51d3bce2612 deinterleave_fastq.sh
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deinterleave_fastq.sh	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,30 @@
+#!/bin/bash
+# Usage: deinterleave_fastq.sh < interleaved.fastq f.fastq r.fastq [compress]
+# 
+# Deinterleaves a FASTQ file of paired reads into two FASTQ
+# files specified on the command line. Optionally GZip compresses the output
+# FASTQ files using pigz if the 3rd command line argument is the word "compress"
+# 
+# Can deinterleave 100 million paired reads (200 million total
+# reads; a 43Gbyte file), in memory (/dev/shm), in 4m15s (255s)
+# 
+# Latest code: https://gist.github.com/3521724
+# Also see my interleaving script: https://gist.github.com/4544979
+# 
+# Inspired by Torsten Seemann's blog post:
+# http://thegenomefactory.blogspot.com.au/2012/05/cool-use-of-unix-paste-with-ngs.html
+
+# Set up some defaults
+GZIP_OUTPUT=0
+PIGZ_COMPRESSION_THREADS=10
+
+# If the third argument is the word "compress" then we'll compress the output using pigz
+if [[ $3 == "compress" ]]; then
+  GZIP_OUTPUT=1
+fi
+
+if [[ ${GZIP_OUTPUT} == 0 ]]; then
+  paste - - - - - - - -  | tee >(cut -f 1-4 | tr "\t" "\n" > $1) | cut -f 5-8 | tr "\t" "\n" > $2
+else
+  paste - - - - - - - -  | tee >(cut -f 1-4 | tr "\t" "\n" | pigz --best --processes ${PIGZ_COMPRESSION_THREADS} > $1) | cut -f 5-8 | tr "\t" "\n" | pigz --best --processes ${PIGZ_COMPRESSION_THREADS} > $2
+fi
diff -r 613775dc3491 -r a51d3bce2612 rpoB_mers_dict
Binary file rpoB_mers_dict has changed
diff -r 613775dc3491 -r a51d3bce2612 seqsero2.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/seqsero2.xml	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,196 @@
+<tool id="seqsero2v102" name="SeqSero2 v1.0.2" version="1.0.2">
+    <description>Salmonella serotype prediction</description>
+    <requirements>
+      <requirement type="package" version="3.6">python</requirement>
+      <requirement type="package" version="1.74">biopython</requirement>
+      <requirement type="package" version="2.7.1">blast</requirement>
+      <requirement type="package" version="1.9">samtools</requirement>
+      <requirement type="package" version="2.9.1">sra-tools</requirement>
+      <requirement type="package" version="0.7.17">bwa</requirement>
+      <requirement type="package" version="3.13.1">spades</requirement>
+      <requirement type="package" version="2.27.1">bedtools</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+      echo "SeqSero2 v1.0.2" ;
+      mkdir ./output;
+
+      #if $reads.reads_select == 'history'
+        #set $name = $reads.forward.name.split('.')[0].replace(' ','_')
+        #set $forward = $reads.forward
+        #set $reverse = $reads.reverse
+        #set $infile = $name + "_1.fastq " +  $name + "_2.fastq" 
+        #set $tval = 2
+        #if $reverse.is_of_type('fastq.gz', 'fastqsanger.gz')
+          gunzip -c $reverse > reverse.fastq;
+          #set $reverse = './reverse.fastq'
+          gunzip -c $forward > forward.fastq;
+          #set $forward = './forward.fastq'
+        #end if
+        ln -s $forward ${name}_1.fastq;
+        ln -s $reverse ${name}_2.fastq;
+      #else if $reads.reads_select == 'collection'
+        #set $name = $reads.coll.name.replace(' ', '_')
+        #set $forward = $reads.coll.forward
+        #set $reverse = $reads.coll.reverse
+        #set $infile = $name + "_1.fastq " +  $name + "_2.fastq" 
+        #set $tval = 2
+        #if $reverse.is_of_type('fastq.gz', 'fastqsanger.gz')
+          gunzip -c $reverse > reverse.fastq;
+          #set $reverse = './reverse.fastq'
+          gunzip -c $forward > forward.fastq;
+          #set $forward = './forward.fastq'
+        #end if
+        ln -s $forward ${name}_1.fastq;
+        ln -s $reverse ${name}_2.fastq;
+      #else 
+        #set $name = $reads.assembly.name.replace(' ', '_')
+        #set $ga = $reads.assembly
+        #set $infile = $name + ".fasta" 
+        ln -s $ga ${name}.fasta;
+        #set $tval = 4
+        #set $mode='k'
+      #end if
+      echo $name ;
+      echo "-=-=-=-=-" ;
+      touch output/SeqSero_log.txt ;
+      python $__tool_directory__/SeqSero2_package.py
+        -p \${GALAXY_SLOTS:-4}
+        -t $tval 
+        -m $mode
+        -d ./output
+      #if $mode == 'a':
+        -b $maptype 
+      #end if
+        -i $infile &&
+        echo "-=-=-=-=-" &&
+        cat output/SeqSero_log.txt &&
+        echo "-=-=-=-=-" &&
+        ls -lah ./output
+    ]]></command>
+    <inputs>
+        
+        <conditional name="reads">
+            <param name="reads_select" type="select" label="Genome assembly,paired-end collection, or two datasets from your history">
+                <option value="collection">Paired collection from your history</option>
+                <option value="history">Two FASTQ datasets from your history</option>
+                <option value="genome">Genome Assembly</option>
+            </param>
+            <when value="collection">
+                <param label="Paired reads" name="coll" type="data_collection" format="fastq,fastqsanger,fastq.gz,fastqsanger.gz" collection_type="paired" />
+            </when>
+            <when value="history">
+                <param label="Forward reads" type="data" name="forward" format="fastq,fastqsanger,fastq.gz,fastqsanger.gz" />
+                <param label="Reverse reads" type="data" name="reverse" format="fastq,fastqsanger,fastq.gz,fastqsanger.gz" />
+            </when>
+            <when value="genome">
+                <param label="Genome assembly" name="assembly" type="data" format="fasta"/>
+            </when>
+        </conditional>
+     
+          <!-- <param name="fastq1" type="data" format="fastq" label="FASTQ paired end read 1" />
+          <param name="fastq2" type="data" format="fastq" label="FASTQ paired end read 2" /> -->
+          <!-- <param name="numofthr" type="select" label="Number of threads">
+          <option value="1">1</option>
+          <option value="2">2</option>
+          <option value="3">3</option>
+          <option value="4">4</option> -->
+        <!-- </param> -->
+        
+        <param label="Analysis mode" type="select" name="mode">
+         <option value="a">allele mode</option>
+         <option value="k">k-mer mode</option>
+        </param>
+
+        <param name="maptype" type="select" label="Algorithms for BWA mapping">
+          <option value="mem">mem</option>
+          <option value="sam">sam</option>
+        </param>
+
+    
+
+    </inputs>
+    <outputs>
+      <data format="tabular" label="SeqSero Results" name="results" from_work_dir="output/SeqSero_result.txt"/>
+    </outputs>
+    <tests>
+       <!-- <test>
+         <param name="reads_select" value="history" />
+         <param name="forward" value="forward.fastq.gz" ftype="fastqsanger.gz" />
+         <param name="reverse" value="reverse.fastq.gz" ftype="fastqsanger.gz" />
+         <output name="results" file="Seqsero_result.tsv" />
+       </test>
+       <test>
+        <param name="reads_select" value="collection" />
+        <param name="coll">
+            <collection type="paired">
+                <element name="forward" value="forward.fastq.gz" ftype="fastqsanger.gz" />
+                <element name="reverse" value="reverse.fastq.gz" ftype="fastqsanger.gz" />
+            </collection>
+        </param>
+        <output name="results" file="Seqsero_result.tsv" />
+       </test> -->
+        <test>
+         <param name="mode" value="k" />
+         <param name="reads_select" value="history" />
+         <param name="forward" value="forward_25k.fastq.gz" ftype="fastqsanger" />
+         <param name="reverse" value="reverse_25k.fastq.gz" ftype="fastqsanger" />
+         <output name="results" file="Seqsero_result_25k.tsv" />
+       </test>
+       <test>
+        <param name="mode" value="k" />
+        <param name="reads_select" value="collection" />
+        <param name="coll">
+            <collection type="paired">
+                <element name="forward" value="forward_25k.fastq.gz" ftype="fastqsanger.gz" />
+                <element name="reverse" value="reverse_25k.fastq.gz" ftype="fastqsanger.gz" />
+            </collection>
+        </param>
+        <output name="results" file="Seqsero_result_25k_coll.tsv" />
+       </test>
+        <test>
+         <param name="mode" value="a" />
+         <param name="reads_select" value="history" />
+         <param name="forward" value="forward_250k.fastq.gz" ftype="fastqsanger" />
+         <param name="reverse" value="reverse_250k.fastq.gz" ftype="fastqsanger" />
+         <assert_stdout>
+          <has_text text="predicted antigenic profile does not exist" />
+         </assert_stdout>
+       </test>
+       <!-- <test>
+        <param name="mode" value="a" />
+        <param name="reads_select" value="collection" />
+        <param name="coll">
+            <collection type="paired">
+                <element name="forward" value="forward_25k.fastq.gz" ftype="fastqsanger.gz" />
+                <element name="reverse" value="reverse_25k.fastq.gz" ftype="fastqsanger.gz" />
+            </collection>
+        </param>
+        <output name="results" file="Seqsero_result_allele.tsv" />
+       </test> -->
+    </tests>
+    <help><![CDATA[
+    
+**Usage: SeqSero2.py** 
+
+**Algorithms for BWA mapping**
+
+'mem' for mem, 'sam' for samse/sampe; default=mem; optional; for now SeqSero2 is only optimized for "mem" mode
+   
+    ]]></help>
+    <citations>
+       <citation type="bibtex">
+        @misc{zhang_yin_jones_zhang_deathrage_dinsmore_fitzgeral_fields_deng_2015,
+        title={Salmonella serotype determination utilizing high-throughput genome sequencing data.},
+        journal={J Clin Microbiol}, publisher={ASM},
+        author={Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.},
+        year={2015}, month={Max},
+        url={http://http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15}},
+       }</citation>
+       <citation type="bibtex">
+        @misc{cfsan_biostatistics_group_2017,
+        title={CFSAN Biostatistics Group fork of SeqSero2},
+        url={https://github.com/CFSAN-Biostatistics/SeqSero2.git}},
+       </citation>
+    </citations>
+
+</tool>
diff -r 613775dc3491 -r a51d3bce2612 setup.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/setup.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,30 @@
+import os, sys
+from distutils.core import setup
+from setuptools import find_packages
+
+def readme():
+    with open('README.md') as f:
+        return f.read()
+
+setup(name='SeqSero2',
+    version=open("version.py").readlines()[-1].split()[-1].strip("\"'"),
+    description='Salmonella serotyping',
+    long_description=readme(),
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'License :: OSI Approved :: GNU General Public License v2 (GPLv2)',
+        'Programming Language :: Python :: 3',
+        'Topic :: Text Processing :: Linguistic',
+        ],
+    keywords='Salmonella serotyping bioinformatics WGS',
+    url='https://github.com/denglab/SeqSero2/',
+    author='Shaokang Zhang, Hendrik C Den-Bakker and Xiangyu Deng',
+    author_email='zskzsk@uga.edu, Hendrik.DenBakker@uga.edu, xdeng@uga.edu',
+    license='GPLv2',
+    scripts=["bin/deinterleave_fastq.sh","bin/Initial_Conditions.py","bin/SeqSero2_package.py","bin/SeqSero2_update_kmer_database.py"],
+    packages=[""],
+    include_package_data = True,
+    install_requires=['biopython==1.73'],
+    data_files=[("seqsero2_db",["seqsero2_db/antigens.pickle","seqsero2_db/H_and_O_and_specific_genes.fasta","seqsero2_db/invA_mers_dict","seqsero2_db/special.pickle"])],
+    zip_safe=False,
+)
diff -r 613775dc3491 -r a51d3bce2612 version.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/version.py	Wed Oct 02 20:37:03 2019 -0400
@@ -0,0 +1,1 @@
+SeqSero2_version = '1.0.2'