estrain@0: #!/usr/bin/env python3
estrain@0: 
estrain@0: import sys
estrain@0: import time
estrain@0: import random
estrain@0: import os
estrain@0: import subprocess
estrain@0: import gzip
estrain@0: import io
estrain@0: import pickle
estrain@0: import argparse
estrain@0: import itertools
estrain@0: import math
estrain@0: from distutils.version import LooseVersion
estrain@0: from distutils.spawn import find_executable
estrain@0: 
estrain@0: try:
estrain@0:     from .version import SeqSero2_version
estrain@0: except Exception: #ImportError
estrain@0:     from version import SeqSero2_version
estrain@0: 
estrain@0: ### SeqSero Kmer
estrain@0: def parse_args():
estrain@0:     "Parse the input arguments, use '-h' for help."
estrain@0:     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
estrain@0:     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.
estrain@0:     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")
estrain@0:     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")
estrain@0:     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")
estrain@0:     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")
estrain@0:     parser.add_argument("-d",help="<string>: output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number")
estrain@0:     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")
estrain@0:     parser.add_argument("--check",action="store_true",help="<flag>: use '--check' flag to check the required dependencies")
estrain@0:     parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + SeqSero2_version)
estrain@0:     return parser.parse_args()
estrain@0: 
estrain@0: ### ed_SL_05282019: check paths of dependencies
estrain@0: check_dependencies = parse_args().check
estrain@0: dependencies = ['bwa','samtools','blastn','fastq-dump','spades.py','bedtools','SalmID.py']
estrain@0: if check_dependencies:
estrain@0:     for item in dependencies:
estrain@0:         ext_path = find_executable(item)
estrain@0:         if ext_path is not None:
estrain@0:             print ("Using "+item+" - "+ext_path)
estrain@0:         else:
estrain@0:             print ("ERROR: can not find "+item+" in PATH")
estrain@0:     sys.exit()
estrain@0: ### end of --check
estrain@0: 
estrain@0: def reverse_complement(sequence):
estrain@0:     complement = {
estrain@0:         'A': 'T',
estrain@0:         'C': 'G',
estrain@0:         'G': 'C',
estrain@0:         'T': 'A',
estrain@0:         'N': 'N',
estrain@0:         'M': 'K',
estrain@0:         'R': 'Y',
estrain@0:         'W': 'W',
estrain@0:         'S': 'S',
estrain@0:         'Y': 'R',
estrain@0:         'K': 'M',
estrain@0:         'V': 'B',
estrain@0:         'H': 'D',
estrain@0:         'D': 'H',
estrain@0:         'B': 'V'
estrain@0:     }
estrain@0:     return "".join(complement[base] for base in reversed(sequence))
estrain@0: 
estrain@0: 
estrain@0: def createKmerDict_reads(list_of_strings, kmer):
estrain@0:     kmer_table = {}
estrain@0:     for string in list_of_strings:
estrain@0:         sequence = string.strip('\n')
estrain@0:         for i in range(len(sequence) - kmer + 1):
estrain@0:             new_mer = sequence[i:i + kmer].upper()
estrain@0:             new_mer_rc = reverse_complement(new_mer)
estrain@0:             if new_mer in kmer_table:
estrain@0:                 kmer_table[new_mer.upper()] += 1
estrain@0:             else:
estrain@0:                 kmer_table[new_mer.upper()] = 1
estrain@0:             if new_mer_rc in kmer_table:
estrain@0:                 kmer_table[new_mer_rc.upper()] += 1
estrain@0:             else:
estrain@0:                 kmer_table[new_mer_rc.upper()] = 1
estrain@0:     return kmer_table
estrain@0: 
estrain@0: 
estrain@0: def multifasta_dict(multifasta):
estrain@0:     multifasta_list = [
estrain@0:         line.strip() for line in open(multifasta, 'r') if len(line.strip()) > 0
estrain@0:     ]
estrain@0:     headers = [i for i in multifasta_list if i[0] == '>']
estrain@0:     multifasta_dict = {}
estrain@0:     for h in headers:
estrain@0:         start = multifasta_list.index(h)
estrain@0:         for element in multifasta_list[start + 1:]:
estrain@0:             if element[0] == '>':
estrain@0:                 break
estrain@0:             else:
estrain@0:                 if h[1:] in multifasta_dict:
estrain@0:                     multifasta_dict[h[1:]] += element
estrain@0:                 else:
estrain@0:                     multifasta_dict[h[1:]] = element
estrain@0:     return multifasta_dict
estrain@0: 
estrain@0: 
estrain@0: def multifasta_single_string(multifasta):
estrain@0:     multifasta_list = [
estrain@0:         line.strip() for line in open(multifasta, 'r')
estrain@0:         if (len(line.strip()) > 0) and (line.strip()[0] != '>')
estrain@0:     ]
estrain@0:     return ''.join(multifasta_list)
estrain@0: 
estrain@0: 
estrain@0: def chunk_a_long_sequence(long_sequence, chunk_size=60):
estrain@0:     chunk_list = []
estrain@0:     steps = len(long_sequence) // 60  #how many chunks
estrain@0:     for i in range(steps):
estrain@0:         chunk_list.append(long_sequence[i * chunk_size:(i + 1) * chunk_size])
estrain@0:     chunk_list.append(long_sequence[steps * chunk_size:len(long_sequence)])
estrain@0:     return chunk_list
estrain@0: 
estrain@0: 
estrain@0: def target_multifasta_kmerizer(multifasta, k, kmerDict):
estrain@0:     forward_length = 300  #if find the target, put forward 300 bases
estrain@0:     reverse_length = 2200  #if find the target, put backward 2200 bases
estrain@0:     chunk_size = 60  #it will firstly chunk the single long sequence to multiple smaller sequences, it controls the size of those smaller sequences
estrain@0:     target_mers = []
estrain@0:     long_single_string = multifasta_single_string(multifasta)
estrain@0:     multifasta_list = chunk_a_long_sequence(long_single_string, chunk_size)
estrain@0:     unit_length = len(multifasta_list[0])
estrain@0:     forward_lines = int(forward_length / unit_length) + 1
estrain@0:     reverse_lines = int(forward_length / unit_length) + 1
estrain@0:     start_num = 0
estrain@0:     end_num = 0
estrain@0:     for i in range(len(multifasta_list)):
estrain@0:         if i not in range(start_num, end_num):  #avoid computational repetition
estrain@0:             line = multifasta_list[i]
estrain@0:             start = int((len(line) - k) // 2)
estrain@0:             s1 = line[start:k + start]
estrain@0:             if s1 in kmerDict:  #detect it is a potential read or not (use the middle part)
estrain@0:                 if i - forward_lines >= 0:
estrain@0:                     start_num = i - forward_lines
estrain@0:                 else:
estrain@0:                     start_num = 0
estrain@0:                 if i + reverse_lines <= len(multifasta_list) - 1:
estrain@0:                     end_num = i + reverse_lines
estrain@0:                 else:
estrain@0:                     end_num = len(multifasta_list) - 1
estrain@0:                 target_list = [
estrain@0:                     x.strip() for x in multifasta_list[start_num:end_num]
estrain@0:                 ]
estrain@0:                 target_line = "".join(target_list)
estrain@0:                 target_mers += [
estrain@0:                     k1 for k1 in createKmerDict_reads([str(target_line)], k)
estrain@0:                 ]  ##changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length)
estrain@0:         else:
estrain@0:             pass
estrain@0:     return set(target_mers)
estrain@0: 
estrain@0: 
estrain@0: def target_read_kmerizer(file, k, kmerDict):
estrain@0:     i = 1
estrain@0:     n_reads = 0
estrain@0:     total_coverage = 0
estrain@0:     target_mers = []
estrain@0:     if file.endswith(".gz"):
estrain@0:         file_content = io.BufferedReader(gzip.open(file))
estrain@0:     else:
estrain@0:         file_content = open(file, "r").readlines()
estrain@0:     for line in file_content:
estrain@0:         start = int((len(line) - k) // 2)
estrain@0:         if i % 4 == 2:
estrain@0:             if file.endswith(".gz"):
estrain@0:                 s1 = line[start:k + start].decode()
estrain@0:                 line = line.decode()
estrain@0:             else:
estrain@0:                 s1 = line[start:k + start]
estrain@0:             if s1 in kmerDict:  #detect it is a potential read or not (use the middle part)
estrain@0:                 n_reads += 1
estrain@0:                 total_coverage += len(line)
estrain@0:                 target_mers += [
estrain@0:                     k1 for k1 in createKmerDict_reads([str(line)], k)
estrain@0:                 ]  #changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length)
estrain@0:         i += 1
estrain@0:         if total_coverage >= 4000000:
estrain@0:             break
estrain@0:     return set(target_mers)
estrain@0: 
estrain@0: 
estrain@0: def minion_fasta_kmerizer(file, k, kmerDict):
estrain@0:     i = 1
estrain@0:     n_reads = 0
estrain@0:     total_coverage = 0
estrain@0:     target_mers = {}
estrain@0:     for line in open(file):
estrain@0:         if i % 2 == 0:
estrain@0:             for kmer, rc_kmer in kmers(line.strip().upper(), k):
estrain@0:                 if (kmer in kmerDict) or (rc_kmer in kmerDict):
estrain@0:                     if kmer in target_mers:
estrain@0:                         target_mers[kmer] += 1
estrain@0:                     else:
estrain@0:                         target_mers[kmer] = 1
estrain@0:                     if rc_kmer in target_mers:
estrain@0:                         target_mers[rc_kmer] += 1
estrain@0:                     else:
estrain@0:                         target_mers[rc_kmer] = 1
estrain@0:         i += 1
estrain@0:     return set([h for h in target_mers])
estrain@0: 
estrain@0: 
estrain@0: def minion_fastq_kmerizer(file, k, kmerDict):
estrain@0:     i = 1
estrain@0:     n_reads = 0
estrain@0:     total_coverage = 0
estrain@0:     target_mers = {}
estrain@0:     for line in open(file):
estrain@0:         if i % 4 == 2:
estrain@0:             for kmer, rc_kmer in kmers(line.strip().upper(), k):
estrain@0:                 if (kmer in kmerDict) or (rc_kmer in kmerDict):
estrain@0:                     if kmer in target_mers:
estrain@0:                         target_mers[kmer] += 1
estrain@0:                     else:
estrain@0:                         target_mers[kmer] = 1
estrain@0:                     if rc_kmer in target_mers:
estrain@0:                         target_mers[rc_kmer] += 1
estrain@0:                     else:
estrain@0:                         target_mers[rc_kmer] = 1
estrain@0:         i += 1
estrain@0:     return set([h for h in target_mers])
estrain@0: 
estrain@0: 
estrain@0: def multifasta_single_string2(multifasta):
estrain@0:     single_string = ''
estrain@0:     with open(multifasta, 'r') as f:
estrain@0:         for line in f:
estrain@0:             if line.strip()[0] == '>':
estrain@0:                 pass
estrain@0:             else:
estrain@0:                 single_string += line.strip()
estrain@0:     return single_string
estrain@0: 
estrain@0: 
estrain@0: def kmers(seq, k):
estrain@0:     rev_comp = reverse_complement(seq)
estrain@0:     for start in range(1, len(seq) - k + 1):
estrain@0:         yield seq[start:start + k], rev_comp[-(start + k):-start]
estrain@0: 
estrain@0: 
estrain@0: def multifasta_to_kmers_dict(multifasta,k_size):#used to create database kmer set
estrain@0:     multi_seq_dict = multifasta_dict(multifasta)
estrain@0:     lib_dict = {}
estrain@0:     for h in multi_seq_dict:
estrain@0:         lib_dict[h] = set(
estrain@0:             [k for k in createKmerDict_reads([multi_seq_dict[h]], k_size)])
estrain@0:     return lib_dict
estrain@0: 
estrain@0: 
estrain@0: def Combine(b, c):
estrain@0:     fliC_combinations = []
estrain@0:     fliC_combinations.append(",".join(c))
estrain@0:     temp_combinations = []
estrain@0:     for i in range(len(b)):
estrain@0:         for x in itertools.combinations(b, i + 1):
estrain@0:             temp_combinations.append(",".join(x))
estrain@0:     for x in temp_combinations:
estrain@0:         temp = []
estrain@0:         for y in c:
estrain@0:             temp.append(y)
estrain@0:         temp.append(x)
estrain@0:         temp = ",".join(temp)
estrain@0:         temp = temp.split(",")
estrain@0:         temp.sort()
estrain@0:         temp = ",".join(temp)
estrain@0:         fliC_combinations.append(temp)
estrain@0:     return fliC_combinations
estrain@0: 
estrain@0: 
estrain@0: def seqsero_from_formula_to_serotypes(Otype, fliC, fljB, special_gene_list,subspecies):
estrain@0:     #like test_output_06012017.txt
estrain@0:     #can add more varialbles like sdf-type, sub-species-type in future (we can conclude it into a special-gene-list)
estrain@0:     from Initial_Conditions import phase1,phase2,phaseO,sero,subs,remove_list,rename_dict
estrain@0:     rename_dict_not_anymore=[rename_dict[x] for x in rename_dict]
estrain@0:     rename_dict_all=rename_dict_not_anymore+list(rename_dict) #used for decide whether to 
estrain@0:     seronames = []
estrain@0:     seronames_none_subspecies=[]
estrain@0:     for i in range(len(phase1)):
estrain@0:         fliC_combine = []
estrain@0:         fljB_combine = []
estrain@0:         if phaseO[i] == Otype: # no VII in KW, but it's there
estrain@0:             ### for fliC, detect every possible combinations to avoid the effect of "["
estrain@0:             if phase1[i].count("[") == 0:
estrain@0:                 fliC_combine.append(phase1[i])
estrain@0:             elif phase1[i].count("[") >= 1:
estrain@0:                 c = []
estrain@0:                 b = []
estrain@0:                 if phase1[i][0] == "[" and phase1[i][-1] == "]" and phase1[i].count(
estrain@0:                         "[") == 1:
estrain@0:                     content = phase1[i].replace("[", "").replace("]", "")
estrain@0:                     fliC_combine.append(content)
estrain@0:                     fliC_combine.append("-")
estrain@0:                 else:
estrain@0:                     for x in phase1[i].split(","):
estrain@0:                         if "[" in x:
estrain@0:                             b.append(x.replace("[", "").replace("]", ""))
estrain@0:                         else:
estrain@0:                             c.append(x)
estrain@0:                     fliC_combine = Combine(
estrain@0:                         b, c
estrain@0:                     )  #Combine will offer every possible combinations of the formula, like f,[g],t: f,t  f,g,t
estrain@0:             ### end of fliC "[" detect
estrain@0:             ### for fljB, detect every possible combinations to avoid the effect of "["
estrain@0:             if phase2[i].count("[") == 0:
estrain@0:                 fljB_combine.append(phase2[i])
estrain@0:             elif phase2[i].count("[") >= 1:
estrain@0:                 d = []
estrain@0:                 e = []
estrain@0:                 if phase2[i][0] == "[" and phase2[i][-1] == "]" and phase2[i].count(
estrain@0:                         "[") == 1:
estrain@0:                     content = phase2[i].replace("[", "").replace("]", "")
estrain@0:                     fljB_combine.append(content)
estrain@0:                     fljB_combine.append("-")
estrain@0:                 else:
estrain@0:                     for x in phase2[i].split(","):
estrain@0:                         if "[" in x:
estrain@0:                             d.append(x.replace("[", "").replace("]", ""))
estrain@0:                         else:
estrain@0:                             e.append(x)
estrain@0:                     fljB_combine = Combine(d, e)
estrain@0:             ### end of fljB "[" detect
estrain@0:             new_fliC = fliC.split(
estrain@0:                 ","
estrain@0:             )  #because some antigen like r,[i] not follow alphabetical order, so use this one to judge and can avoid missings
estrain@0:             new_fliC.sort()
estrain@0:             new_fliC = ",".join(new_fliC)
estrain@0:             new_fljB = fljB.split(",")
estrain@0:             new_fljB.sort()
estrain@0:             new_fljB = ",".join(new_fljB)
estrain@0:             if (new_fliC in fliC_combine
estrain@0:                     or fliC in fliC_combine) and (new_fljB in fljB_combine
estrain@0:                                                   or fljB in fljB_combine):
estrain@0:               ######start, remove_list,rename_dict, added on 11/11/2018
estrain@0:               if sero[i] not in remove_list:
estrain@0:                 temp_sero=sero[i]
estrain@0:                 if temp_sero in rename_dict:
estrain@0:                   temp_sero=rename_dict[temp_sero] #rename if in the rename list
estrain@0:                 if temp_sero not in seronames:#the new sero may already included, if yes, then not consider
estrain@0:                   if subs[i] == subspecies:
estrain@0:                     seronames.append(temp_sero)
estrain@0:                   seronames_none_subspecies.append(temp_sero)
estrain@0:                 else:
estrain@0:                   pass
estrain@0:               else:
estrain@0:                 pass
estrain@0:               ######end, added on 11/11/2018
estrain@0:     #analyze seronames
estrain@0:     subspecies_pointer=""
estrain@0:     if len(seronames) == 0 and len(seronames_none_subspecies)!=0:
estrain@0:       seronames=seronames_none_subspecies
estrain@0:       subspecies_pointer="1"
estrain@0:     if len(seronames) == 0:
estrain@0:         seronames = [
estrain@0:             "N/A (The predicted antigenic profile does not exist in the White-Kauffmann-Le Minor scheme)"
estrain@0:         ]
estrain@0:     star = ""
estrain@0:     star_line = ""
estrain@0:     if len(seronames) > 1:  #there are two possible predictions for serotypes
estrain@0:         star = "*"
estrain@0:         #changed 04072019
estrain@0:         #star_line = "The predicted serotypes share the same general formula:\t" + Otype + ":" + fliC + ":" + fljB + "\n"
estrain@0:     if subspecies_pointer=="1" and len(seronames_none_subspecies)!=0:
estrain@0:       star="*"
estrain@0:       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
estrain@0:       #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
estrain@0:     if  Otype=="":
estrain@0:       Otype="-"
estrain@0:     predict_form = Otype + ":" + fliC + ":" + fljB
estrain@0:     predict_sero = (" or ").join(seronames)
estrain@0:     ###special test for Enteritidis
estrain@0:     if predict_form == "9:g,m:-":
estrain@0:         sdf = "-"
estrain@0:         for x in special_gene_list:
estrain@0:             if x.startswith("sdf"):
estrain@0:                 sdf = "+"
estrain@0:                 #star_line="Detected sdf gene, a marker to differentiate Gallinarum and Enteritidis"
estrain@0:                 star_line=" sdf gene detected." # ed_SL_04152019: new output format
estrain@0:         #predict_form = predict_form + " Sdf prediction:" + sdf
estrain@0:         predict_form = predict_form #changed 04072019
estrain@0:         if sdf == "-":
estrain@0:             star = "*"
estrain@0:             #star_line="Didn't detected sdf gene, a marker to differentiate Gallinarum and Enteritidis"
estrain@0:             star_line=" sdf gene not detected." # ed_SL_04152019: new output format
estrain@0:             #changed in 04072019, for new output
estrain@0:             #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"
estrain@0:             #predict_sero = "Gallinarum/Enteritidis" #04132019, for new output requirement
estrain@0:             predict_sero = "Gallinarum or Enteritidis" # ed_SL_04152019: new output format
estrain@0:     ###end of special test for Enteritidis
estrain@0:     elif predict_form == "4:i:-":
estrain@0:         predict_sero = "I 4,[5],12:i:-" # ed_SL_09242019: change serotype name
estrain@0:     elif predict_form == "4:r:-":
estrain@0:         predict_sero = "4:r:-"
estrain@0:     elif predict_form == "4:b:-": # ed_SL_09272019: change for new output format
estrain@0:         predict_sero = "N/A (4:b:-)"
estrain@0:     #elif predict_form == "8:e,h:1,2": #removed after official merge of newport and bardo
estrain@0:         #predict_sero = "Newport"
estrain@0:         #star = "*"
estrain@0:         #star_line = "Serotype Bardo shares the same antigenic profile with Newport, but Bardo is exceedingly rare."
estrain@0:     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"
estrain@0:     if "N/A" in predict_sero:
estrain@0:         claim = ""
estrain@0:     #special test for Typhimurium
estrain@0:     if "Typhimurium" in predict_sero or predict_form == "4:i:-":
estrain@0:         normal = 0
estrain@0:         mutation = 0
estrain@0:         for x in special_gene_list:
estrain@0:             if "oafA-O-4_full" in x:
estrain@0:                 normal = float(special_gene_list[x])
estrain@0:             elif "oafA-O-4_5-" in x:
estrain@0:                 mutation = float(special_gene_list[x])
estrain@0:         if normal > mutation:
estrain@0:             pass
estrain@0:         elif normal < mutation:
estrain@0:             #predict_sero = predict_sero.strip() + "(O5-)"
estrain@0:             predict_sero = predict_sero.strip() #diable special sero for new output requirement, 04132019
estrain@0:             star = "*"
estrain@0:             #star_line = "Detected the deletion of O5-."
estrain@0:             star_line = " Detected a deletion that causes O5- variant of Typhimurium." # ed_SL_04152019: new output format
estrain@0:         else:
estrain@0:             pass
estrain@0:     #special test for Paratyphi B
estrain@0:     if "Paratyphi B" in predict_sero or predict_form == "4:b:-":
estrain@0:         normal = 0
estrain@0:         mutation = 0
estrain@0:         for x in special_gene_list:
estrain@0:             if "gntR-family-regulatory-protein_dt-positive" in x:
estrain@0:                 normal = float(special_gene_list[x])
estrain@0:             elif "gntR-family-regulatory-protein_dt-negative" in x:
estrain@0:                 mutation = float(special_gene_list[x])
estrain@0:         #print(normal,mutation)
estrain@0:         if normal > mutation:
estrain@0:             #predict_sero = predict_sero.strip() + "(dt+)" #diable special sero for new output requirement, 04132019
estrain@0:             predict_sero = predict_sero.strip()+' var. L(+) tartrate+' if "Paratyphi B" in predict_sero else predict_sero.strip() # ed_SL_04152019: new output format
estrain@0:             star = "*"
estrain@0:             #star_line = "Didn't detect the SNP for dt- which means this isolate is a Paratyphi B variant L(+) tartrate(+)."
estrain@0:             star_line = " The SNP that causes d-Tartrate nonfermentating phenotype of Paratyphi B was not detected. " # ed_SL_04152019: new output format
estrain@0:         elif normal < mutation:
estrain@0:             #predict_sero = predict_sero.strip() + "(dt-)" #diable special sero for new output requirement, 04132019
estrain@0:             predict_sero = predict_sero.strip()
estrain@0:             star = "*"
estrain@0:             #star_line = "Detected the SNP for dt- which means this isolate is a systemic pathovar of Paratyphi B."
estrain@0:             star_line = " Detected the SNP for d-Tartrate nonfermenting phenotype of Paratyphi B." # ed_SL_04152019: new output format
estrain@0:         else:
estrain@0:             star = "*"
estrain@0:             #star_line = " Failed to detect the SNP for dt-, can't decide it's a Paratyphi B variant L(+) tartrate(+) or not."
estrain@0:             star_line = " " ## ed_SL_05152019: do not report this situation.
estrain@0:     #special test for O13,22 and O13,23
estrain@0:     if Otype=="13":
estrain@0:       #ex_dir = os.path.dirname(os.path.realpath(__file__))
estrain@0:       ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019
estrain@0:       f = open(ex_dir + '/special.pickle', 'rb')
estrain@0:       special = pickle.load(f)
estrain@0:       O22_O23=special['O22_O23']
estrain@0:       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 
estrain@0:         O22_score=0
estrain@0:         O23_score=0
estrain@0:         for x in special_gene_list:
estrain@0:             if "O:22" in x:
estrain@0:                 O22_score = O22_score+float(special_gene_list[x])
estrain@0:             elif "O:23" in x:
estrain@0:                 O23_score = O23_score+float(special_gene_list[x])
estrain@0:         #print(O22_score,O23_score)
estrain@0:         for z in O22_O23[0]:
estrain@0:           if predict_sero.split(" or ")[0] in z:
estrain@0:             if O22_score > O23_score:
estrain@0:               star = "*"
estrain@0:               #star_line = "Detected O22 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019
estrain@0:               predict_sero = z[0]
estrain@0:             elif O22_score < O23_score:
estrain@0:               star = "*"
estrain@0:               #star_line = "Detected O23 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019
estrain@0:               predict_sero = z[1]
estrain@0:             else:
estrain@0:               star = "*"
estrain@0:               #star_line = "Fail to detect O22 and O23 differences." #diabled for new output requirement, 04132019
estrain@0:     if " or " in predict_sero:
estrain@0:       star_line = star_line + " The predicted serotypes share the same general formula:\t" + Otype + ":" + fliC + ":" + fljB + "\n"
estrain@0:     #special test for O6,8 
estrain@0:     #merge_O68_list=["Blockley","Bovismorbificans","Hadar","Litchfield","Manhattan","Muenchen"] #remove 11/11/2018, because already in merge list
estrain@0:     #for x in merge_O68_list:
estrain@0:     #  if x in predict_sero:
estrain@0:     #    predict_sero=x
estrain@0:     #    star=""
estrain@0:     #    star_line=""
estrain@0:     #special test for Montevideo; most of them are monophasic
estrain@0:     #if "Montevideo" in predict_sero and "1,2,7" in predict_form: #remove 11/11/2018, because already in merge list
estrain@0:       #star="*"
estrain@0:       #star_line="Montevideo is almost always monophasic, having an antigen called for the fljB position may be a result of Salmonella-Salmonella contamination."
estrain@0:     return predict_form, predict_sero, star, star_line, claim
estrain@0: ### End of SeqSero Kmer part
estrain@0: 
estrain@0: ### Begin of SeqSero2 allele prediction and output
estrain@0: def xml_parse_score_comparision_seqsero(xmlfile):
estrain@0:   #used to do seqsero xml analysis
estrain@0:   from Bio.Blast import NCBIXML
estrain@0:   handle=open(xmlfile)
estrain@0:   handle=NCBIXML.parse(handle)
estrain@0:   handle=list(handle)
estrain@0:   List=[]
estrain@0:   List_score=[]
estrain@0:   List_ids=[]
estrain@0:   List_query_region=[]
estrain@0:   for i in range(len(handle)):
estrain@0:     if len(handle[i].alignments)>0:
estrain@0:       for j in range(len(handle[i].alignments)):
estrain@0:         score=0
estrain@0:         ids=0
estrain@0:         cover_region=set() #fixed problem that repeated calculation leading percentage > 1
estrain@0:         List.append(handle[i].query.strip()+"___"+handle[i].alignments[j].hit_def)
estrain@0:         for z in range(len(handle[i].alignments[j].hsps)):
estrain@0:           hsp=handle[i].alignments[j].hsps[z]
estrain@0:           temp=set(range(hsp.query_start,hsp.query_end))
estrain@0:           est_bit=(handle[i].ka_params[0]*hsp.score-math.log(handle[i].ka_params[1]))/(math.log(2))
estrain@0:           if len(cover_region)==0:
estrain@0:             cover_region=cover_region|temp
estrain@0:             fraction=1
estrain@0:           else:
estrain@0:             fraction=1-len(cover_region&temp)/float(len(temp))
estrain@0:             cover_region=cover_region|temp
estrain@0:           if "last" in handle[i].query or "first" in handle[i].query:
estrain@0:             #score+=hsp.bits*fraction
estrain@0:             score+=est_bit*fraction
estrain@0:             ids+=float(hsp.identities)/handle[i].query_length*fraction
estrain@0:           else:
estrain@0:             #score+=hsp.bits*fraction
estrain@0:             score+=est_bit*fraction
estrain@0:             ids+=float(hsp.identities)/handle[i].query_length*fraction
estrain@0:         List_score.append(score)
estrain@0:         List_ids.append(ids)
estrain@0:         List_query_region.append(cover_region)
estrain@0:   temp=zip(List,List_score,List_ids,List_query_region)
estrain@0:   Final_list=sorted(temp, key=lambda d:d[1], reverse = True)
estrain@0:   return Final_list
estrain@0: 
estrain@0: 
estrain@0: def Uniq(L,sort_on_fre="none"): #return the uniq list and the count number
estrain@0:   Old=L
estrain@0:   L.sort()
estrain@0:   L = [L[i] for i in range(len(L)) if L[i] not in L[:i]]
estrain@0:   count=[]
estrain@0:   for j in range(len(L)):
estrain@0:     y=0
estrain@0:     for x in Old:
estrain@0:       if L[j]==x:
estrain@0:         y+=1
estrain@0:     count.append(y)
estrain@0:   if sort_on_fre!="none":
estrain@0:     d=zip(*sorted(zip(count, L)))
estrain@0:     L=d[1]
estrain@0:     count=d[0]
estrain@0:   return (L,count)
estrain@0: 
estrain@0: def judge_fliC_or_fljB_from_head_tail_for_one_contig(nodes_vs_score_list):
estrain@0:   #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
estrain@0:   #this is mainly used for 
estrain@0:   a=nodes_vs_score_list
estrain@0:   fliC_score=0
estrain@0:   fljB_score=0
estrain@0:   for z in a:
estrain@0:     if "fliC" in z[0]:
estrain@0:       fliC_score+=z[1]
estrain@0:     elif "fljB" in z[0]:
estrain@0:       fljB_score+=z[1]
estrain@0:   if fliC_score>=fljB_score:
estrain@0:     role="fliC"
estrain@0:   else:
estrain@0:     role="fljB"
estrain@0:   return (role,abs(fliC_score-fljB_score))
estrain@0: 
estrain@0: def judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(node_name,Final_list,Final_list_passed):
estrain@0:   #used to predict contig is fliC or fljB, if the differnce score value on above head_and_tail is less than 10 (quite small)
estrain@0:   #also used when no head or tail got blasted score for the contig
estrain@0:   role=""
estrain@0:   for z in Final_list_passed:
estrain@0:     if node_name in z[0]:
estrain@0:       role=z[0].split("_")[0]
estrain@0:       break
estrain@0:   return role
estrain@0: 
estrain@0: def fliC_or_fljB_judge_from_head_tail_sequence(nodes_list,tail_head_list,Final_list,Final_list_passed):
estrain@0:   #nodes_list is the c created by c,d=Uniq(nodes) in below function
estrain@0:   first_target=""
estrain@0:   role_list=[]
estrain@0:   for x in nodes_list:
estrain@0:     a=[]
estrain@0:     role=""
estrain@0:     for y in tail_head_list:
estrain@0:       if x in y[0]:
estrain@0:         a.append(y)
estrain@0:     if len(a)==4:
estrain@0:       role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
estrain@0:       if diff<20:
estrain@0:         role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
estrain@0:     elif len(a)==3:
estrain@0:       ###however, if the one with highest score is the fewer one, compare their accumulation score
estrain@0:       role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
estrain@0:       if diff<20:
estrain@0:         role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
estrain@0:       ###end of above score comparison
estrain@0:     elif len(a)==2:
estrain@0:       #must on same node, if not, then decide with unit blast score, blast-score/length_of_special_sequence(30 or 37)
estrain@0:       temp=[]
estrain@0:       for z in a:
estrain@0:         temp.append(z[0].split("_")[0])
estrain@0:       m,n=Uniq(temp)#should only have one choice, but weird situation might occur too
estrain@0:       if len(m)==1:
estrain@0:         pass
estrain@0:       else:
estrain@0:         pass
estrain@0:       role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
estrain@0:       if diff<20:
estrain@0:         role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
estrain@0:         ###need to desgin a algorithm to guess most possible situation for nodes_list, See the situations of test evaluation
estrain@0:     elif len(a)==1:
estrain@0:       #that one
estrain@0:       role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a)
estrain@0:       if diff<20:
estrain@0:         role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
estrain@0:       #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"
estrain@0:     else:#a==0
estrain@0:       #use Final_list_passed best match
estrain@0:       for z in Final_list_passed:
estrain@0:         if x in z[0]:
estrain@0:           role=z[0].split("_")[0]
estrain@0:           break
estrain@0:     #print x,role,len(a)
estrain@0:     role_list.append((role,x))
estrain@0:   if len(role_list)==2:
estrain@0:     if role_list[0][0]==role_list[1][0]:#this is the most cocmmon error, two antigen were assigned to same phase
estrain@0:       #just use score to do a final test
estrain@0:       role_list=[]
estrain@0:       for x in nodes_list: 
estrain@0:         role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed)
estrain@0:         role_list.append((role,x))
estrain@0:   return role_list
estrain@0: 
estrain@0: def decide_contig_roles_for_H_antigen(Final_list,Final_list_passed):
estrain@0:   #used to decide which contig is FliC and which one is fljB
estrain@0:   contigs=[]
estrain@0:   nodes=[]
estrain@0:   for x in Final_list_passed:
estrain@0:     if x[0].startswith("fl") and "last" not in x[0] and "first" not in x[0]:
estrain@0:       nodes.append(x[0].split("___")[1].strip())
estrain@0:   c,d=Uniq(nodes)#c is node_list
estrain@0:   #print c
estrain@0:   tail_head_list=[x for x in Final_list if ("last" in x[0] or "first" in x[0])]
estrain@0:   roles=fliC_or_fljB_judge_from_head_tail_sequence(c,tail_head_list,Final_list,Final_list_passed)
estrain@0:   return roles
estrain@0: 
estrain@0: def decide_O_type_and_get_special_genes(Final_list,Final_list_passed):
estrain@0:   #decide O based on Final_list
estrain@0:   O_choice="?"
estrain@0:   O_list=[]
estrain@0:   special_genes={}
estrain@0:   nodes=[]
estrain@0:   for x in Final_list_passed:
estrain@0:     if x[0].startswith("O-"):
estrain@0:       nodes.append(x[0].split("___")[1].strip())
estrain@0:     elif not x[0].startswith("fl"):
estrain@0:       special_genes[x[0]]=x[2]#08172018, x[2] changed from x[-1]
estrain@0:   #print "special_genes:",special_genes
estrain@0:   c,d=Uniq(nodes)
estrain@0:   #print "potential O antigen contig",c
estrain@0:   final_O=[]
estrain@0:   O_nodes_list=[]
estrain@0:   for x in c:#c is the list for contigs
estrain@0:     temp=0
estrain@0:     for y in Final_list_passed:
estrain@0:       if x in y[0] and y[0].startswith("O-"):
estrain@0:         final_O.append(y)
estrain@0:         break
estrain@0:   ### O contig has the problem of two genes on same contig, so do additional test
estrain@0:   potenial_new_gene=""
estrain@0:   for x in final_O:
estrain@0:     pointer=0 #for genes merged or not
estrain@0:     #not consider O-1,3,19_not_in_3,10, too short compared with others
estrain@0:     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
estrain@0:       pointer=x[0].split("___")[1].strip()#store the contig name
estrain@0:       print(pointer)
estrain@0:     if pointer!=0:#it has potential merge event
estrain@0:       for y in Final_list:
estrain@0:         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
estrain@0:           potenial_new_gene=y
estrain@0:           #print(potenial_new_gene)
estrain@0:           break
estrain@0:   if potenial_new_gene!="":
estrain@0:     print("two differnt genes in same contig, fix it for O antigen")
estrain@0:     print(potenial_new_gene[:3])
estrain@0:     pointer=0
estrain@0:     for y in final_O:
estrain@0:       if y[0].split("___")[-1]==potenial_new_gene[0].split("___")[-1]:
estrain@0:         pointer=1
estrain@0:     if pointer!=0: #changed to consider two genes in same contig
estrain@0:       final_O.append(potenial_new_gene)
estrain@0:   ### end of the two genes on same contig test
estrain@0:   final_O=sorted(final_O,key=lambda x: x[2], reverse=True)#sorted
estrain@0:   if len(final_O)==0 or (len(final_O)==1 and "O-1,3,19_not_in_3,10" in final_O[0][0]):
estrain@0:     #print "$$$No Otype, due to no hit"#may need to be changed
estrain@0:     O_choice="-"
estrain@0:   else:
estrain@0:     highest_O_coverage=max([float(x[0].split("_cov_")[-1].split("_")[0]) for x in final_O if "O-1,3,19_not_in_3,10" not in x[0]])
estrain@0:     O_list=[]
estrain@0:     O_list_less_contamination=[]
estrain@0:     for x in final_O:
estrain@0:       if not "O-1,3,19_not_in_3,10__130" in x[0]:#O-1,3,19_not_in_3,10 is too small, which may affect further analysis; to avoid contamination affect, use 0.15 of highest coverage as cut-off
estrain@0:         O_list.append(x[0].split("__")[0])
estrain@0:         O_nodes_list.append(x[0].split("___")[1])
estrain@0:         if float(x[0].split("_cov_")[-1].split("_")[0])>highest_O_coverage*0.15:
estrain@0:           O_list_less_contamination.append(x[0].split("__")[0])
estrain@0:     ### special test for O9,46 and O3,10 family
estrain@0:     if ("O-9,46_wbaV" in O_list or "O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254" in O_list) and O_list_less_contamination[0].startswith("O-9,"):#not sure should use and float(O9_wbaV)/float(num_1) > 0.1
estrain@0:       if "O-9,46_wzy" in O_list:#and float(O946_wzy)/float(num_1) > 0.1
estrain@0:         O_choice="O-9,46"
estrain@0:         #print "$$$Most possilble Otype:  O-9,46"
estrain@0:       elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1
estrain@0:         O_choice="O-9,46,27"
estrain@0:         #print "$$$Most possilble Otype:  O-9,46,27"
estrain@0:       else:
estrain@0:         O_choice="O-9"#next, detect O9 vs O2?
estrain@0:         O2=0
estrain@0:         O9=0
estrain@0:         for z in special_genes:
estrain@0:           if "tyr-O-9" in z:
estrain@0:             O9=special_genes[z]
estrain@0:           elif "tyr-O-2" in z:
estrain@0:             O2=special_genes[z]
estrain@0:         if O2>O9:
estrain@0:           O_choice="O-2"
estrain@0:         elif O2<O9:
estrain@0:           pass
estrain@0:         else:
estrain@0:           pass
estrain@0:           #print "$$$No suitable one, because can't distinct it's O-9 or O-2, but O-9 has a more possibility."
estrain@0:     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
estrain@0:       if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1
estrain@0:         O_choice="O-3,10"
estrain@0:         #print "$$$Most possilble Otype:  O-3,10 (contain O-3,10_not_in_1,3,19)"
estrain@0:       else:
estrain@0:         O_choice="O-1,3,19"
estrain@0:         #print "$$$Most possilble Otype:  O-1,3,19 (not contain O-3,10_not_in_1,3,19)"
estrain@0:     ### end of special test for O9,46 and O3,10 family
estrain@0:     else:
estrain@0:       try: 
estrain@0:         max_score=0
estrain@0:         for x in final_O:
estrain@0:           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
estrain@0:             max_score=x[2]#change from x[-1] to x[2],08172018
estrain@0:             O_choice=x[0].split("_")[0]
estrain@0:         if O_choice=="O-1,3,19":
estrain@0:           O_choice=final_O[1][0].split("_")[0]
estrain@0:         #print "$$$Most possilble Otype: ",O_choice
estrain@0:       except:
estrain@0:         pass
estrain@0:         #print "$$$No suitable Otype, or failure of mapping (please check the quality of raw reads)"
estrain@0:   #print "O:",O_choice,O_nodes_list
estrain@0:   Otypes=[]
estrain@0:   for x in O_list:
estrain@0:     if x!="O-1,3,19_not_in_3,10":
estrain@0:       if "O-9,46_" not in x:
estrain@0:         Otypes.append(x.split("_")[0])
estrain@0:       else:
estrain@0:         Otypes.append(x.split("-from")[0])#O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254
estrain@0:   #Otypes=[x.split("_")[0] for x in O_list if x!="O-1,3,19_not_in_3,10"]
estrain@0:   Otypes_uniq,Otypes_fre=Uniq(Otypes)
estrain@0:   contamination_O=""
estrain@0:   if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19":
estrain@0:     if len(Otypes_uniq)>2:
estrain@0:       contamination_O="potential contamination from O antigen signals"
estrain@0:   else:
estrain@0:     if len(Otypes_uniq)>1:
estrain@0:       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
estrain@0:         contamination_O=""
estrain@0:       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
estrain@0:         contamination_O=""
estrain@0:       else:
estrain@0:         contamination_O="potential contamination from O antigen signals"
estrain@0:   return O_choice,O_nodes_list,special_genes,final_O,contamination_O,Otypes_uniq
estrain@0: ### End of SeqSero2 allele prediction and output
estrain@0: 
estrain@0: def get_input_files(make_dir,input_file,data_type,dirpath):
estrain@0:   #tell input files from datatype
estrain@0:   #"<int>: '1'(pair-end reads, interleaved),'2'(pair-end reads, seperated),'3'(single-end reads), '4'(assembly),'5'(nanopore fasta),'6'(nanopore fastq)"
estrain@0:   for_fq=""
estrain@0:   rev_fq=""
estrain@0:   os.chdir(make_dir)
estrain@0:   if data_type=="1":
estrain@0:     input_file=input_file[0].split("/")[-1]
estrain@0:     if input_file.endswith(".sra"):
estrain@0:       subprocess.check_call("fastq-dump --split-files "+input_file,shell=True)
estrain@0:       for_fq=input_file.replace(".sra","_1.fastq")
estrain@0:       rev_fq=input_file.replace(".sra","_2.fastq")
estrain@0:     else:
estrain@0:       core_id=input_file.split(".fastq")[0].split(".fq")[0]
estrain@0:       for_fq=core_id+"_1.fastq"
estrain@0:       rev_fq=core_id+"_2.fastq"
estrain@0:       if input_file.endswith(".gz"):
estrain@0:         subprocess.check_call("gzip -dc "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True)
estrain@0:       else:
estrain@0:         subprocess.check_call("cat "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True)
estrain@0:   elif data_type=="2":
estrain@0:     for_fq=input_file[0].split("/")[-1]
estrain@0:     rev_fq=input_file[1].split("/")[-1]
estrain@0:   elif data_type=="3":
estrain@0:     input_file=input_file[0].split("/")[-1]
estrain@0:     if input_file.endswith(".sra"):
estrain@0:       subprocess.check_call("fastq-dump --split-files "+input_file,shell=True)
estrain@0:       for_fq=input_file.replace(".sra","_1.fastq")
estrain@0:     else:
estrain@0:       for_fq=input_file
estrain@0:   elif data_type in ["4","5","6"]:
estrain@0:     for_fq=input_file[0].split("/")[-1]
estrain@0:   os.chdir("..")
estrain@0:   return for_fq,rev_fq
estrain@0: 
estrain@0: def predict_O_and_H_types(Final_list,Final_list_passed,new_fasta):
estrain@0:   #get O and H types from Final_list from blast parsing; allele mode
estrain@0:   from Bio import SeqIO
estrain@0:   fliC_choice="-"
estrain@0:   fljB_choice="-"
estrain@0:   fliC_contig="NA"
estrain@0:   fljB_contig="NA"
estrain@0:   fliC_region=set([0])
estrain@0:   fljB_region=set([0,])
estrain@0:   fliC_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length
estrain@0:   fljB_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length
estrain@0:   O_choice="-"#no need to decide O contig for now, should be only one
estrain@0:   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
estrain@0:   O_choice=O_choice.split("-")[-1].strip()
estrain@0:   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=="":
estrain@0:     O_choice="-"
estrain@0:   H_contig_roles=decide_contig_roles_for_H_antigen(Final_list,Final_list_passed)#decide the H antigen contig is fliC or fljB
estrain@0:   #add alignment locations, used for further selection, 03312019
estrain@0:   for i in range(len(H_contig_roles)):
estrain@0:     x=H_contig_roles[i]
estrain@0:     for y in Final_list_passed:
estrain@0:       if x[1] in y[0] and y[0].startswith(x[0]):
estrain@0:         H_contig_roles[i]+=H_contig_roles[i]+(y[-1],)
estrain@0:         break
estrain@0:   log_file=open("SeqSero_log.txt","a")
estrain@0:   extract_file=open("Extracted_antigen_alleles.fasta","a")
estrain@0:   handle_fasta=list(SeqIO.parse(new_fasta,"fasta"))
estrain@0: 
estrain@0:   #print("O_contigs:")
estrain@0:   log_file.write("O_contigs:\n")
estrain@0:   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")
estrain@0:   extract_file.write("#O_contigs:\n")
estrain@0:   for x in O_nodes_roles:
estrain@0:     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
estrain@0:       #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])))
estrain@0:       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")
estrain@0:       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"      
estrain@0:       seqs=""
estrain@0:       for z in handle_fasta:
estrain@0:         if x[0].split("___")[-1]==z.description:
estrain@0:           seqs=str(z.seq)
estrain@0:       extract_file.write(title+seqs+"\n")
estrain@0:   if len(H_contig_roles)!=0:
estrain@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
estrain@0:   else:
estrain@0:     highest_H_coverage=0
estrain@0:   for x in H_contig_roles:
estrain@0:     #if multiple choices, temporately select the one with longest length for now, will revise in further change
estrain@0:     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
estrain@0:       fliC_contig=x[1]
estrain@0:       fliC_length=len(x[-1])
estrain@0:     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:
estrain@0:       fljB_contig=x[1]
estrain@0:       fljB_length=len(x[-1])
estrain@0:   for x in Final_list_passed:
estrain@0:     if fliC_choice=="-" and "fliC_" in x[0] and fliC_contig in x[0]:
estrain@0:       fliC_choice=x[0].split("_")[1]
estrain@0:     elif fljB_choice=="-" and "fljB_" in x[0] and fljB_contig in x[0]:
estrain@0:       fljB_choice=x[0].split("_")[1]
estrain@0:     elif fliC_choice!="-" and fljB_choice!="-":
estrain@0:       break
estrain@0:   #now remove contigs not in middle core part
estrain@0:   first_allele="NA"
estrain@0:   first_allele_percentage=0
estrain@0:   for x in Final_list:
estrain@0:     if x[0].startswith("fliC") or x[0].startswith("fljB"):
estrain@0:       first_allele=x[0].split("__")[0] #used to filter those un-middle contigs
estrain@0:       first_allele_percentage=x[2]
estrain@0:       break 
estrain@0:   additional_contigs=[]
estrain@0:   for x in Final_list:
estrain@0:     if first_allele in x[0]:
estrain@0:       if (fliC_contig == x[0].split("___")[-1]): 
estrain@0:         fliC_region=x[3]
estrain@0:       elif fljB_contig!="NA" and (fljB_contig == x[0].split("___")[-1]):
estrain@0:         fljB_region=x[3]
estrain@0:       else:
estrain@0:         if x[1]*1.1>int(x[0].split("___")[1].split("_")[3]):#loose threshold by multiplying 1.1
estrain@0:           additional_contigs.append(x)
estrain@0:         #else:
estrain@0:           #print x[:3]
estrain@0:   #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
estrain@0:   if first_allele_percentage>0.9:
estrain@0:     if len(fliC_region)>len(fljB_region) and (max(fljB_region)-min(fljB_region))>1000:
estrain@0:       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))))
estrain@0:     elif len(fliC_region)<len(fljB_region) and (max(fliC_region)-min(fliC_region))>1000:
estrain@0:       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))))
estrain@0:     else:
estrain@0:       target_region=set()#doesn't do anything
estrain@0:   else:
estrain@0:     target_region=set()#doesn't do anything
estrain@0:   #print(target_region)
estrain@0:   #print(additional_contigs)
estrain@0:   target_region2=set(list(range(0,525))+list(range(1200,1700)))#I found to use 500 to 1200 as special region would be best
estrain@0:   target_region=target_region2|target_region
estrain@0:   for x in additional_contigs:
estrain@0:     removal=0
estrain@0:     contig_length=int(x[0].split("___")[1].split("length_")[-1].split("_")[0])
estrain@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
estrain@0:       removal=1
estrain@0:     else:
estrain@0:       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)
estrain@0:         removal=1
estrain@0:       else:
estrain@0:         pass
estrain@0:     if removal==1:
estrain@0:       for y in H_contig_roles:
estrain@0:         if y[1] in x[0]:
estrain@0:           H_contig_roles.remove(y)
estrain@0:     else:
estrain@0:       pass
estrain@0:       #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)
estrain@0:   #end of removing none-middle contigs
estrain@0:   #print("H_contigs:")
estrain@0:   log_file.write("H_contigs:\n")
estrain@0:   extract_file.write("#H_contigs:\n")
estrain@0:   H_contig_stat=[]
estrain@0:   H1_cont_stat={}
estrain@0:   H2_cont_stat={}
estrain@0:   for i in range(len(H_contig_roles)):
estrain@0:     x=H_contig_roles[i]
estrain@0:     a=0
estrain@0:     for y in Final_list_passed:
estrain@0:       if x[1] in y[0] and y[0].startswith(x[0]):
estrain@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
estrain@0:           for y in Final_list_passed: #it's impossible to has the "first" and "last" allele as prediction, so re-do it
estrain@0:             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
estrain@0:               #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])))
estrain@0:               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")
estrain@0:               H_contig_roles[i]="can't decide fliC or fljB, may be third phase"
estrain@0:               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"
estrain@0:               seqs=""
estrain@0:               for z in handle_fasta:
estrain@0:                 if x[1]==z.description:
estrain@0:                   seqs=str(z.seq)
estrain@0:               extract_file.write(title+seqs+"\n")
estrain@0:               break
estrain@0:         else:
estrain@0:           #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])))
estrain@0:           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")
estrain@0:           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"
estrain@0:           seqs=""
estrain@0:           for z in handle_fasta:
estrain@0:             if x[1]==z.description:
estrain@0:               seqs=str(z.seq)
estrain@0:           extract_file.write(title+seqs+"\n")
estrain@0:         if x[0]=="fliC":
estrain@0:           if y[0].split("_")[1] not in H1_cont_stat:
estrain@0:             H1_cont_stat[y[0].split("_")[1]]=y[2]
estrain@0:           else:
estrain@0:             H1_cont_stat[y[0].split("_")[1]]+=y[2]
estrain@0:         if x[0]=="fljB":
estrain@0:           if y[0].split("_")[1] not in H2_cont_stat:
estrain@0:             H2_cont_stat[y[0].split("_")[1]]=y[2]
estrain@0:           else:
estrain@0:             H2_cont_stat[y[0].split("_")[1]]+=y[2]
estrain@0:         break
estrain@0:   #detect contaminations
estrain@0:   #print(H1_cont_stat)
estrain@0:   #print(H2_cont_stat)
estrain@0:   H1_cont_stat_list=[x for x in H1_cont_stat if H1_cont_stat[x]>0.2]
estrain@0:   H2_cont_stat_list=[x for x in H2_cont_stat if H2_cont_stat[x]>0.2]
estrain@0:   contamination_H=""
estrain@0:   if len(H1_cont_stat_list)>1 or len(H2_cont_stat_list)>1:
estrain@0:     contamination_H="potential contamination from H antigen signals"
estrain@0:   elif len(H2_cont_stat_list)==1 and fljB_contig=="NA":
estrain@0:     contamination_H="potential contamination from H antigen signals, uncommon weak fljB signals detected"
estrain@0:   #get additional antigens
estrain@0:   """
estrain@0:     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
estrain@0:       if "O-9,46_wzy" in O_list:#and float(O946_wzy)/float(num_1) > 0.1
estrain@0:         O_choice="O-9,46"
estrain@0:         #print "$$$Most possilble Otype:  O-9,46"
estrain@0:       elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1
estrain@0:         O_choice="O-9,46,27"
estrain@0:         #print "$$$Most possilble Otype:  O-9,46,27"
estrain@0:     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
estrain@0:       if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1
estrain@0:         O_choice="O-3,10"
estrain@0:         #print "$$$Most possilble Otype:  O-3,10 (contain O-3,10_not_in_1,3,19)"
estrain@0:       else:
estrain@0:         O_choice="O-1,3,19"
estrain@0:         #print "$$$Most possilble Otype:  O-1,3,19 (not contain O-3,10_not_in_1,3,19)"
estrain@0:     ### end of special test for O9,46 and O3,10 family
estrain@0: 
estrain@0:     if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19":
estrain@0:     if len(Otypes_uniq)>2:
estrain@0:       contamination_O="potential contamination from O antigen signals"
estrain@0:   else:
estrain@0:     if len(Otypes_uniq)>1:
estrain@0:       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
estrain@0:         contamination_O=""
estrain@0:       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
estrain@0:         contamination_O=""
estrain@0:   """
estrain@0:   additonal_antigents=[]
estrain@0:   #print(contamination_O)
estrain@0:   #print(contamination_H)
estrain@0:   log_file.write(contamination_O+"\n")
estrain@0:   log_file.write(contamination_H+"\n")
estrain@0:   log_file.close()
estrain@0:   return O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H,Otypes_uniq,H1_cont_stat_list,H2_cont_stat_list
estrain@0: 
estrain@0: def get_input_K(input_file,lib_dict,data_type,k_size):
estrain@0:   #kmer mode; get input_Ks from dict and data_type
estrain@0:   kmers = []
estrain@0:   for h in lib_dict:
estrain@0:       kmers += lib_dict[h]
estrain@0:   if data_type == '4':
estrain@0:       input_Ks = target_multifasta_kmerizer(input_file, k_size, set(kmers))
estrain@0:   elif data_type == '1' or data_type == '2' or data_type == '3':#set it for now, will change later
estrain@0:       input_Ks = target_read_kmerizer(input_file, k_size, set(kmers))
estrain@0:   elif data_type == '5':#minion_2d_fasta
estrain@0:       input_Ks = minion_fasta_kmerizer(input_file, k_size, set(kmers))
estrain@0:   if data_type == '6':#minion_2d_fastq
estrain@0:       input_Ks = minion_fastq_kmerizer(input_file, k_size, set(kmers))
estrain@0:   return input_Ks
estrain@0: 
estrain@0: def get_kmer_dict(lib_dict,input_Ks):
estrain@0:   #kmer mode; get predicted types
estrain@0:   O_dict = {}
estrain@0:   H_dict = {}
estrain@0:   Special_dict = {}
estrain@0:   for h in lib_dict:
estrain@0:       score = (len(lib_dict[h] & input_Ks) / len(lib_dict[h])) * 100
estrain@0:       if score > 1:  # Arbitrary cut-off for similarity score very low but seems necessary to detect O-3,10 in some cases
estrain@0:           if h.startswith('O-') and score > 25:
estrain@0:               O_dict[h] = score
estrain@0:           if h.startswith('fl') and score > 40:
estrain@0:               H_dict[h] = score
estrain@0:           if (h[:2] != 'fl') and (h[:2] != 'O-'):
estrain@0:               Special_dict[h] = score
estrain@0:   return O_dict,H_dict,Special_dict
estrain@0: 
estrain@0: def call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir):
estrain@0:   log_file=open("SeqSero_log.txt","a")
estrain@0:   log_file.write("O_scores:\n")
estrain@0:   #call O:
estrain@0:   highest_O = '-'
estrain@0:   if len(O_dict) == 0:
estrain@0:       pass
estrain@0:   else:
estrain@0:       for x in O_dict:
estrain@0:           log_file.write(x+"\t"+str(O_dict[x])+"\n")
estrain@0:       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
estrain@0:           if 'O-9,46_wzy__1191' in O_dict:  # and float(O946_wzy)/float(num_1) > 0.1
estrain@0:               highest_O = "O-9,46"
estrain@0:           elif "O-9,46,27_partial_wzy__1019" in O_dict:  # and float(O94627)/float(num_1) > 0.1
estrain@0:               highest_O = "O-9,46,27"
estrain@0:           else:
estrain@0:               highest_O = "O-9"  # next, detect O9 vs O2?
estrain@0:               O2 = 0
estrain@0:               O9 = 0
estrain@0:               for z in Special_dict:
estrain@0:                   if "tyr-O-9" in z:
estrain@0:                       O9 = float(Special_dict[z])
estrain@0:                   if "tyr-O-2" in z:
estrain@0:                       O2 = float(Special_dict[z])
estrain@0:               if O2 > O9:
estrain@0:                   highest_O = "O-2"
estrain@0:       elif ("O-3,10_wzx__1539" in O_dict) and (
estrain@0:               "O-9,46_wzy__1191" in O_dict
estrain@0:       ):  # and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1
estrain@0:           if "O-3,10_not_in_1,3,19__1519" in O_dict:  # and float(O310_no_1319)/float(num_1) > 0.1
estrain@0:               highest_O = "O-3,10"
estrain@0:           else:
estrain@0:               highest_O = "O-1,3,19"
estrain@0:       ### end of special test for O9,46 and O3,10 family
estrain@0:       else:
estrain@0:           try:
estrain@0:               max_score = 0
estrain@0:               for x in O_dict:
estrain@0:                   if float(O_dict[x]) >= max_score:
estrain@0:                       max_score = float(O_dict[x])
estrain@0:                       highest_O = x.split("_")[0]
estrain@0:               if highest_O == "O-1,3,19":
estrain@0:                   highest_O = '-'
estrain@0:                   max_score = 0
estrain@0:                   for x in O_dict:
estrain@0:                       if x == 'O-1,3,19_not_in_3,10__130':
estrain@0:                           pass
estrain@0:                       else:
estrain@0:                           if float(O_dict[x]) >= max_score:
estrain@0:                               max_score = float(O_dict[x])
estrain@0:                               highest_O = x.split("_")[0]
estrain@0:           except:
estrain@0:               pass
estrain@0:   #call_fliC:
estrain@0:   if len(H_dict)!=0:
estrain@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
estrain@0:   else:
estrain@0:     highest_H_score_both_BC=0
estrain@0:   highest_fliC = '-'
estrain@0:   highest_fliC_raw = '-'
estrain@0:   highest_Score = 0
estrain@0:   log_file.write("\nH_scores:\n")
estrain@0:   for s in H_dict:
estrain@0:       log_file.write(s+"\t"+str(H_dict[s])+"\n")
estrain@0:       if s.startswith('fliC'):
estrain@0:           if float(H_dict[s]) > highest_Score:
estrain@0:               highest_fliC = s.split('_')[1]
estrain@0:               highest_fliC_raw = s
estrain@0:               highest_Score = float(H_dict[s])
estrain@0:   #call_fljB
estrain@0:   highest_fljB = '-'
estrain@0:   highest_fljB_raw = '-'
estrain@0:   highest_Score = 0
estrain@0:   for s in H_dict:
estrain@0:       if s.startswith('fljB'):
estrain@0:           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
estrain@0:               highest_fljB = s.split('_')[1]
estrain@0:               highest_fljB_raw = s
estrain@0:               highest_Score = float(H_dict[s])
estrain@0:   log_file.write("\nSpecial_scores:\n")
estrain@0:   for s in Special_dict:
estrain@0:     log_file.write(s+"\t"+str(Special_dict[s])+"\n")
estrain@0:   log_file.close()
estrain@0:   return highest_O,highest_fliC,highest_fljB
estrain@0: 
estrain@0: def get_temp_file_names(for_fq,rev_fq):
estrain@0:   #seqsero2 -a; get temp file names
estrain@0:   sam=for_fq+".sam"
estrain@0:   bam=for_fq+".bam"
estrain@0:   sorted_bam=for_fq+"_sorted.bam"
estrain@0:   mapped_fq1=for_fq+"_mapped.fq"
estrain@0:   mapped_fq2=rev_fq+"_mapped.fq"
estrain@0:   combined_fq=for_fq+"_combined.fq"
estrain@0:   for_sai=for_fq+".sai"
estrain@0:   rev_sai=rev_fq+".sai"
estrain@0:   return sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai
estrain@0: 
estrain@0: def map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode):
estrain@0:   #seqsero2 -a; do mapping and sort
estrain@0:   print("building database...")
estrain@0:   subprocess.check_call("bwa index "+database+ " 2>> data_log.txt",shell=True)
estrain@0:   print("mapping...")
estrain@0:   if mapping_mode=="mem":
estrain@0:     subprocess.check_call("bwa mem -k 17 -t "+threads+" "+database+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True)
estrain@0:   elif mapping_mode=="sam":
estrain@0:     if fnameB!="":
estrain@0:       subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True)
estrain@0:       subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameB+" > "+rev_sai+ " 2>> data_log.txt",shell=True)
estrain@0:       subprocess.check_call("bwa sampe "+database+" "+for_sai+" "+ rev_sai+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True)
estrain@0:     else:
estrain@0:       subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True)
estrain@0:       subprocess.check_call("bwa samse "+database+" "+for_sai+" "+for_fq+" > "+sam)
estrain@0:   subprocess.check_call("samtools view -@ "+threads+" -F 4 -Sh "+sam+" > "+bam,shell=True)
estrain@0:   ### check the version of samtools then use differnt commands
estrain@0:   samtools_version=subprocess.Popen(["samtools"],stdout=subprocess.PIPE,stderr=subprocess.PIPE)
estrain@0:   out, err = samtools_version.communicate()
estrain@0:   version = str(err).split("ersion:")[1].strip().split(" ")[0].strip()
estrain@0:   print("check samtools version:",version)
estrain@0:   ### end of samtools version check and its analysis
estrain@0:   if LooseVersion(version)<=LooseVersion("1.2"):
estrain@0:     subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" "+fnameA+"_sorted",shell=True)
estrain@0:   else:
estrain@0:     subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" >"+sorted_bam,shell=True)
estrain@0: 
estrain@0: 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):
estrain@0:   #seqsero2 -a; extract, assembly and blast
estrain@0:   subprocess.check_call("bamToFastq -i "+sorted_bam+" -fq "+combined_fq,shell=True)
estrain@0:   #print("fnameA:",fnameA)
estrain@0:   #print("fnameB:",fnameB)
estrain@0:   if fnameB!="":
estrain@0:     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
estrain@0:   else:
estrain@0:     pass
estrain@0:   outdir=current_time+"_temp"
estrain@0:   print("assembling...")
estrain@0:   if int(threads)>4:
estrain@0:     t="4"
estrain@0:   else:
estrain@0:     t=threads
estrain@0:   if os.path.getsize(combined_fq)>100 and (fnameB=="" or os.path.getsize(mapped_fq1)>100):#if not, then it's "-:-:-"
estrain@0:     if fnameB!="":
estrain@0:       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)
estrain@0:     else:
estrain@0:       subprocess.check_call("spades.py --careful --pe1-s "+combined_fq+" -t "+t+" -o "+outdir+ " >> data_log.txt 2>&1",shell=True)
estrain@0:     #new_fasta=fnameA+"_"+database+"_"+mapping_mode+".fasta"
estrain@0:     new_fasta=fnameA+"_"+database.split('/')[-1]+"_"+mapping_mode+".fasta" # ed_SL_09152019: change path to databse for packaging
estrain@0:     subprocess.check_call("mv "+outdir+"/contigs.fasta "+new_fasta+ " 2> /dev/null",shell=True)
estrain@0:     #os.system("mv "+outdir+"/scaffolds.fasta "+new_fasta+ " 2> /dev/null") contigs.fasta
estrain@0:     subprocess.check_call("rm -rf "+outdir+ " 2> /dev/null",shell=True)
estrain@0:     print("blasting...","\n")
estrain@0:     xmlfile="blasted_output.xml"#fnameA+"-extracted_vs_"+database+"_"+mapping_mode+".xml"
estrain@0:     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
estrain@0:     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"
estrain@0:   else:
estrain@0:     xmlfile="NA"
estrain@0:   return xmlfile,new_fasta
estrain@0: 
estrain@0: def judge_subspecies(fnameA,dirpath):
estrain@0:   #seqsero2 -a; judge subspecies on just forward raw reads fastq
estrain@0:   salmID_output=subprocess.Popen("python " + dirpath + "/SalmID.py -i "+fnameA,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
estrain@0:   out, err = salmID_output.communicate()
estrain@0:   out=out.decode("utf-8")
estrain@0:   file=open("data_log.txt","a")
estrain@0:   file.write(out)
estrain@0:   file.close()
estrain@0:   salm_species_scores=out.split("\n")[1].split("\t")[6:]
estrain@0:   salm_species_results=out.split("\n")[0].split("\t")[6:]
estrain@0:   max_score=0
estrain@0:   max_score_index=1 #default is 1, means "I"
estrain@0:   for i in range(len(salm_species_scores)):
estrain@0:     if max_score<float(salm_species_scores[i]):
estrain@0:       max_score=float(salm_species_scores[i])
estrain@0:       max_score_index=i
estrain@0:   prediction=salm_species_results[max_score_index].split(".")[1].strip().split(" ")[0]
estrain@0:   if float(out.split("\n")[1].split("\t")[4]) > float(out.split("\n")[1].split("\t")[5]): #bongori and enterica compare
estrain@0:     prediction="bongori" #if not, the prediction would always be enterica, since they are located in the later part
estrain@0:   if max_score<10:
estrain@0:     prediction="-"
estrain@0:   return prediction
estrain@0: 
estrain@0: def judge_subspecies_Kmer(Special_dict):
estrain@0:   #seqsero2 -k;
estrain@0:   max_score=0
estrain@0:   prediction="-" #default should be I
estrain@0:   for x in Special_dict:
estrain@0:     if "mer" in x:
estrain@0:       if max_score<float(Special_dict[x]):
estrain@0:         max_score=float(Special_dict[x])
estrain@0:         prediction=x.split("_")[-1].strip()
estrain@0:       if x.split("_")[-1].strip()=="bongori" and float(Special_dict[x])>95:#if bongori already, then no need to test enterica
estrain@0:         prediction="bongori"
estrain@0:         break
estrain@0:   return prediction
estrain@0: 
estrain@0: def main():
estrain@0:   #combine SeqSeroK and SeqSero2, also with SalmID
estrain@0:   args = parse_args()
estrain@0:   input_file = args.i
estrain@0:   data_type = args.t
estrain@0:   analysis_mode = args.m
estrain@0:   mapping_mode=args.b
estrain@0:   threads=args.p
estrain@0:   make_dir=args.d
estrain@0:   clean_mode=args.c
estrain@0:   k_size=27 #will change for bug fixing
estrain@0:   #database="H_and_O_and_specific_genes.fasta"
estrain@0:   dirpath = os.path.abspath(os.path.dirname(os.path.realpath(__file__)))
estrain@0:   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
estrain@4:   database=dirpath+"/seqsero2_db/H_and_O_and_specific_genes.fasta" # ed_SL_09152019: change path to database for packaging
estrain@0:   note="Note:"
estrain@0:   NA_note=" This predicted serotype is not in the Kauffman-White scheme." # ed_SL_09272019: add for new output format
estrain@0:   if len(sys.argv)==1:
estrain@0:     subprocess.check_call(dirpath+"/SeqSero2_package.py -h",shell=True)#change name of python file
estrain@0:   else:
estrain@0:     request_id = time.strftime("%m_%d_%Y_%H_%M_%S", time.localtime())
estrain@0:     request_id += str(random.randint(1, 10000000))
estrain@0:     if make_dir is None:
estrain@0:       make_dir="SeqSero_result_"+request_id
estrain@0:     if os.path.isdir(make_dir):
estrain@0:       pass
estrain@0:     else:
estrain@0:       subprocess.check_call(["mkdir",make_dir])
estrain@0:     #subprocess.check_call("cp "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True)
estrain@0:     #subprocess.check_call("ln -sr "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True)
estrain@0:     subprocess.check_call("ln -f -s "+database+" "+" ".join(input_file)+" "+make_dir,shell=True) # ed_SL_09152019: change path to database for packaging 
estrain@0:     #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).
estrain@0:   ############################begin the real analysis 
estrain@0:     if analysis_mode=="a":
estrain@0:       if data_type in ["1","2","3"]:#use allele mode
estrain@0:         for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath)
estrain@0:         os.chdir(make_dir)
estrain@0:         ###add a function to tell input files
estrain@0:         fnameA=for_fq.split("/")[-1]
estrain@0:         fnameB=rev_fq.split("/")[-1]
estrain@0:         current_time=time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
estrain@0:         sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai=get_temp_file_names(fnameA,fnameB) #get temp files id
estrain@0:         map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode) #do mapping and sort
estrain@0:         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
estrain@0:         if xmlfile=="NA":
estrain@0:           O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H=("-","-","-",[],"","")
estrain@0:         else:
estrain@0:           Final_list=xml_parse_score_comparision_seqsero(xmlfile) #analyze xml and get parsed results
estrain@0:           file=open("data_log.txt","a")
estrain@0:           for x in Final_list:
estrain@0:             file.write("\t".join(str(y) for y in x)+"\n")
estrain@0:           file.close()
estrain@0:           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)]
estrain@0:           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
estrain@0:         subspecies=judge_subspecies(fnameA,dirpath) #predict subspecies
estrain@0:         ###output
estrain@0:         predict_form,predict_sero,star,star_line,claim=seqsero_from_formula_to_serotypes(O_choice,fliC_choice,fljB_choice,special_gene_list,subspecies)
estrain@0:         claim="" #04132019, disable claim for new report requirement
estrain@0:         contamination_report=""
estrain@0:         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]
estrain@0:         if contamination_O!="" and contamination_H=="":
estrain@0:           contamination_report="#Potential inter-serotype contamination detected from O antigen signals. All O-antigens detected:"+"\t".join(Otypes_uniq)+"."
estrain@0:         elif contamination_O=="" and contamination_H!="":
estrain@0:           contamination_report="#Potential inter-serotype contamination detected or potential thrid H phase from H antigen signals. All H-antigens detected:"+"\t".join(H_list)+"."
estrain@0:         elif contamination_O!="" and contamination_H!="":
estrain@0:           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)+"."
estrain@0:         if contamination_report!="":
estrain@0:           #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
estrain@0:           contamination_report=" Co-existence of multiple serotypes detected, indicating potential inter-serotype contamination. See 'Extracted_antigen_alleles.fasta' for detected serotype determinant alleles."
estrain@0:         #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
estrain@0:         ## ed_SL_09272019: change for new output format
estrain@0:         #if contamination_report+star_line+claim=="": #0413, new output style
estrain@0:         #  note=""
estrain@0:         #else:
estrain@0:         #  note="Note:"
estrain@0:         if clean_mode:
estrain@0:           subprocess.check_call("rm -rf ../"+make_dir,shell=True)
estrain@0:           make_dir="none-output-directory due to '-c' flag"
estrain@0:         else:
estrain@0:           new_file=open("SeqSero_result.txt","w")
estrain@0:           if O_choice=="":
estrain@0:             O_choice="-"
estrain@0:           if "N/A" not in predict_sero:
estrain@0:             new_file.write("Output_directory:\t"+make_dir+"\n"+
estrain@0:                            "Input files:\t"+"\t".join(input_file)+"\n"+
estrain@0:                            "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                            "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
estrain@0:                            "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
estrain@0:                            "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                            "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                            "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
estrain@0:                            note+contamination_report+star_line+claim+"\n")#+##
estrain@0:           else:
estrain@0:             #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n"
estrain@0:             star_line="" #04132019, for new output requirement, diable star_line if "NA" in output
estrain@0:             new_file.write("Output_directory:\t"+make_dir+"\n"+
estrain@0:                            "Input files:\t"+"\t".join(input_file)+"\n"+
estrain@0:                            "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                            "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
estrain@0:                            "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
estrain@0:                            "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                            "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                            "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
estrain@0:                            note+NA_note+contamination_report+star_line+claim+"\n")#+##
estrain@0:           new_file.close()
estrain@0:           print("\n")
estrain@0:           #subprocess.check_call("cat Seqsero_result.txt",shell=True)
estrain@0:           #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)
estrain@0:           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)
estrain@0:         if "N/A" not in predict_sero:
estrain@0:           #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")#+##
estrain@0:           print("Output_directory:\t"+make_dir+"\n"+
estrain@0:                 "Input files:\t"+"\t".join(input_file)+"\n"+
estrain@0:                 "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
estrain@0:                 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
estrain@0:                 "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                 "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                 "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
estrain@0:                 note+contamination_report+star_line+claim+"\n")#+##
estrain@0:         else:
estrain@0:           print("Output_directory:\t"+make_dir+"\n"+
estrain@0:                 "Input files:\t"+"\t".join(input_file)+"\n"+
estrain@0:                 "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+
estrain@0:                 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+
estrain@0:                 "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                 "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                 "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
estrain@0:                 note+NA_note+contamination_report+star_line+claim+"\n")
estrain@0:       else:
estrain@0:         print("Allele modes only support raw reads datatype, i.e. '-t 1 or 2 or 3'; please use '-m k'")
estrain@0:     elif analysis_mode=="k":
estrain@0:       #ex_dir = os.path.dirname(os.path.realpath(__file__))
estrain@0:       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
estrain@0:       #output_mode = args.mode
estrain@0:       for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath)
estrain@0:       input_file = for_fq #-k will just use forward because not all reads were used
estrain@0:       os.chdir(make_dir)
estrain@0:       f = open(ex_dir + '/antigens.pickle', 'rb')
estrain@0:       lib_dict = pickle.load(f)
estrain@0:       f.close
estrain@0:       input_Ks=get_input_K(input_file,lib_dict,data_type,k_size)
estrain@0:       O_dict,H_dict,Special_dict=get_kmer_dict(lib_dict,input_Ks)
estrain@0:       highest_O,highest_fliC,highest_fljB=call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir)
estrain@0:       subspecies=judge_subspecies_Kmer(Special_dict)
estrain@0:       if subspecies=="IIb" or subspecies=="IIa":
estrain@0:         subspecies="II"
estrain@0:       predict_form,predict_sero,star,star_line,claim = seqsero_from_formula_to_serotypes(
estrain@0:           highest_O.split('-')[1], highest_fliC, highest_fljB, Special_dict,subspecies)
estrain@0:       claim="" #no claim any more based on new output requirement
estrain@0:       ## ed_SL_09272019: change for new output format
estrain@0:       #if star_line+claim=="": #0413, new output style
estrain@0:       #  note=""
estrain@0:       #else:
estrain@0:       #  note="Note:"
estrain@0:       if clean_mode:
estrain@0:         subprocess.check_call("rm -rf ../"+make_dir,shell=True)
estrain@0:         make_dir="none-output-directory due to '-c' flag"
estrain@0:         ### ed_SL_05282019, fix the assignment issue of variable 'O_choice' using "-m k -c"
estrain@0:         if highest_O.split('-')[-1]=="":
estrain@0:           O_choice="-"
estrain@0:         else:
estrain@0:           O_choice=highest_O.split('-')[-1]
estrain@0:         ###
estrain@0:       else:
estrain@0:         if highest_O.split('-')[-1]=="":
estrain@0:           O_choice="-"
estrain@0:         else:
estrain@0:           O_choice=highest_O.split('-')[-1]
estrain@0:         #print("Output_directory:"+make_dir+"\tInput_file:"+input_file+"\tPredicted subpecies:"+subspecies + '\tPredicted antigenic profile:' + predict_form + '\tPredicted serotype(s):' + predict_sero)
estrain@0:         new_file=open("SeqSero_result.txt","w")
estrain@0:         #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")#+##
estrain@0:         if "N/A" not in predict_sero:
estrain@0:           new_file.write("Output_directory:\t"+make_dir+"\n"+
estrain@0:                          "Input files:\t"+input_file+"\n"+
estrain@0:                          "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                          "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
estrain@0:                          "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
estrain@0:                          "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                          "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                          "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
estrain@0:                          note+star_line+claim+"\n")#+##
estrain@0:         else:
estrain@0:           #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n"
estrain@0:           star_line = "" #changed for new output requirement, 04132019
estrain@0:           new_file.write("Output_directory:\t"+make_dir+"\n"+
estrain@0:                          "Input files:\t"+input_file+"\n"+
estrain@0:                          "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:                          "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
estrain@0:                          "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
estrain@0:                          "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:                          "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:                          "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
estrain@0:                          note+NA_note+star_line+claim+"\n")#+##
estrain@0:         new_file.close()
estrain@0:         subprocess.call("rm *.fasta* *.fastq *.gz *.fq temp.txt *.sra 2> /dev/null",shell=True)
estrain@0:       if "N/A" not in predict_sero:
estrain@0:         print("Output_directory:\t"+make_dir+"\n"+
estrain@0:               "Input files:\t"+input_file+"\n"+
estrain@0:               "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:               "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
estrain@0:               "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
estrain@0:               "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:               "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:               "Predicted serotype:\t"+predict_sero+"\n"+ # ed_SL_04152019: change serotype(s) to serotype
estrain@0:               note+star_line+claim+"\n")#+##
estrain@0:       else:
estrain@0:         print("Output_directory:\t"+make_dir+"\n"+
estrain@0:               "Input files:\t"+input_file+"\n"+
estrain@0:               "O antigen prediction:\t"+O_choice+"\n"+
estrain@0:               "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+
estrain@0:               "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+
estrain@0:               "Predicted subspecies:\t"+subspecies+"\n"+
estrain@0:               "Predicted antigenic profile:\t"+predict_form+"\n"+
estrain@0:               "Predicted serotype:\t"+predict_form+"\n"+ # ed_SL_09242019: add serotype output for "N/A" prediction
estrain@0:               note+NA_note+star_line+claim+"\n")#+##
estrain@0: 
estrain@0: if __name__ == '__main__':
estrain@0:   main()