├── Results.png ├── Outcomes.png ├── .gitattributes ├── test ├── NZ_CP028167.1.tgz ├── NZ_CP028329.1.tgz ├── NZ_CP028167.1.fna_stdout ├── NZ_CP028329.1.fna_stdout ├── NZ_CP028167.1.faa ├── NZ_CP028329.1.faa └── NZ_CP028167.1.fna ├── macsyfinder.environment.yml ├── copla.ini ├── copla.environment.yml ├── bin ├── download_Copla_databases.sh ├── get_fastani_identity.sh ├── generate_test_output.sh ├── get_ani_identity_job.pl ├── check_conjugation_systems.sh ├── get_ani_identity.pl ├── hmmscan_domtblout_summarize.py ├── post_install_test.sh ├── sHSBM.py └── copla.py ├── macsyfinder.spec-file.txt ├── README.md ├── copla.spec-file.txt └── LICENSE /Results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/santirdnd/COPLA/HEAD/Results.png -------------------------------------------------------------------------------- /Outcomes.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/santirdnd/COPLA/HEAD/Outcomes.png -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /test/NZ_CP028167.1.tgz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/santirdnd/COPLA/HEAD/test/NZ_CP028167.1.tgz -------------------------------------------------------------------------------- /test/NZ_CP028329.1.tgz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/santirdnd/COPLA/HEAD/test/NZ_CP028329.1.tgz -------------------------------------------------------------------------------- /macsyfinder.environment.yml: -------------------------------------------------------------------------------- 1 | name: macsyfinder 2 | channels: 3 | - conda-forge 4 | - bioconda 5 | - defaults 6 | dependencies: 7 | - python=2.7 8 | - blast=2.9.0 9 | - hmmer>=3.2.1 10 | prefix: macsyfinder 11 | -------------------------------------------------------------------------------- /test/NZ_CP028167.1.fna_stdout: -------------------------------------------------------------------------------- 1 | Query is a PTU-X4 plasmid 2 | Query is part of a sHSBM cluster of size 22 3 | Other info: 4 | Size: 33305 5 | MOB: MOBP 6 | MPF: typeT 7 | Repl: IncX4 8 | AMR: MCR-1;MCR-1 9 | -------------------------------------------------------------------------------- /copla.ini: -------------------------------------------------------------------------------- 1 | [Database_paths] 2 | COPLA_DB_DIR databases/Copla_RS84 3 | MOBscan_DB_DIR databases/MOBscan_171004 4 | MacSyFinder_DB_DIR databases/MacSyFinder_190530 5 | PlasmidFinder_DB_DIR databases/PlasmidFinder_190731 6 | CARD_DB_DIR databases/CARD_201015 7 | -------------------------------------------------------------------------------- /test/NZ_CP028329.1.fna_stdout: -------------------------------------------------------------------------------- 1 | Query is a PTU-Lab26 plasmid 2 | Query is part of a sHSBM cluster of size 11 3 | Other info: 4 | Size: 49442 5 | MOB: MOBQ 6 | MPF: - 7 | Repl: - 8 | AMR: - 9 | Query inclusion has caused PTU host range to increase from I to II 10 | -------------------------------------------------------------------------------- /copla.environment.yml: -------------------------------------------------------------------------------- 1 | name: copla 2 | channels: 3 | - conda-forge 4 | - bioconda 5 | - defaults 6 | dependencies: 7 | - python=3 8 | - graph-tool>=2.33 9 | - numpy 10 | - pandas 11 | - blast>=2.9.0 12 | - prodigal>=2.6.3 13 | - hmmer>=3.2.1 14 | - plasmidfinder>=2.1 15 | - ruby 16 | - parallel 17 | prefix: copla 18 | -------------------------------------------------------------------------------- /bin/download_Copla_databases.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | wget https://castillo.dicom.unican.es/zaguan/Copla/Copla_databases_latest.tar 4 | tar -xf Copla_databases_latest.tar 5 | 6 | COPLA_DB_DIR=`grep '^COPLA_DB_DIR' copla.ini | cut -f2` 7 | 8 | find ${COPLA_DB_DIR} -type f -name "*.fna.gz" -print0 | xargs -0 gunzip 9 | 10 | awk -v path=${PWD}/${COPLA_DB_DIR} '{ print path "/" $1 ".fna" }' ${COPLA_DB_DIR}/CoplaDB.lst > ${COPLA_DB_DIR}/CoplaDB.fofn 11 | -------------------------------------------------------------------------------- /bin/get_fastani_identity.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | if [ $# -ne 2 ]; then 4 | echo -e 'Usage: get_ani_identity.sh QUERY.fna.gz REFLIST.lst' 5 | exit 2 6 | fi 7 | 8 | source "$(dirname "${CONDA_EXE%/*}")"/etc/profile.d/conda.sh 9 | conda activate fastani 10 | 11 | THREADS=48 12 | MINFRAC=0.5 13 | FRAGLEN=1500 14 | KMERLEN=16 15 | 16 | QRYFILE=$1 17 | REFLIST=$2 18 | OUTFILE="$1.fastani.tsv" 19 | LOGFILE="$1.fastani.log" 20 | 21 | fastANI -q ${QRYFILE} --rl ${REFLIST} -o ${OUTFILE} --minFraction ${MINFRAC} -t ${THREADS} -k ${KMERLEN} --fragLen ${FRAGLEN} 1>${LOGFILE} 2>&1 22 | -------------------------------------------------------------------------------- /bin/generate_test_output.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | source "$(dirname "${CONDA_EXE%/*}")"/etc/profile.d/conda.sh 4 | conda activate copla 5 | 6 | # NZ_CP028167.1 7 | bin/copla.py test/NZ_CP028167.1.fna \ 8 | databases/Copla_RS84/RS84f_sHSBM.pickle \ 9 | databases/Copla_RS84/CoplaDB.fofn \ 10 | test/NZ_CP028167.1.fna_output \ 11 | -a test/NZ_CP028167.1.faa \ 12 | -t circular \ 13 | -k Bacteria \ 14 | -p Proteobacteria \ 15 | -c Gammaproteobacteria \ 16 | -o Enterobacterales \ 17 | -f Enterobacteriaceae \ 18 | -g Escherichia \ 19 | -s 'Escherichia coli' | \ 20 | tee test/NZ_CP028167.1.fna_stdout 21 | 22 | tar -zcf test/NZ_CP028167.1.new.tgz -C test NZ_CP028167.1.fna_output/ 23 | 24 | # NZ_CP028329.1 25 | bin/copla.py test/NZ_CP028329.1.fna \ 26 | databases/Copla_RS84/RS84f_sHSBM.pickle \ 27 | databases/Copla_RS84/CoplaDB.fofn \ 28 | test/NZ_CP028329.1.fna_output \ 29 | -a test/NZ_CP028329.1.faa \ 30 | -t circular \ 31 | -k Bacteria \ 32 | -p Firmicutes \ 33 | -c Bacilli \ 34 | -o Lactobacillales \ 35 | -f Lactobacillaceae \ 36 | -g Lactobacillus \ 37 | -s 'Lactobacillus sp. D1501' | \ 38 | tee test/NZ_CP028329.1.fna_stdout 39 | 40 | tar -zcf test/NZ_CP028329.1.new.tgz -C test NZ_CP028329.1.fna_output/ 41 | -------------------------------------------------------------------------------- /bin/get_ani_identity_job.pl: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env perl 2 | 3 | BEGIN { 4 | use strict; 5 | die "Old version of strict module\n" unless strict->VERSION >= 1.0; 6 | use warnings; 7 | die "Old version of warnings module\n" unless warnings->VERSION >= 1.1; 8 | use List::Util; 9 | die "Old version of List::Util module\n" unless List::Util->VERSION >= 1.41; 10 | use File::Basename; 11 | die "Old version of File::Basename module\n" unless File::Basename->VERSION >= 2.85; 12 | } 13 | 14 | use strict; 15 | use warnings; 16 | use List::Util qw[min]; 17 | use File::Basename qw[basename fileparse]; 18 | 19 | my $num_args = $#ARGV + 1; 20 | if ($num_args != 3) { 21 | print "\nUsage: get_ani_identity_job.pl qry_path qry_length ref_path\n"; 22 | exit; 23 | } 24 | 25 | my $perc = 0.5; 26 | my $window = 1000; 27 | my $step = 200; 28 | 29 | my $fname_qry = $ARGV[0]; 30 | my $len_qry = $ARGV[1]; 31 | my $fname_ref = $ARGV[2]; 32 | 33 | my (undef, $work_dir) = fileparse($ARGV[0]); 34 | my $fname_tsv = $work_dir . basename($fname_ref) . '.tsv'; 35 | 36 | my $len_ref; 37 | my $len_core; 38 | my $threshold; 39 | 40 | # Get reference sequence length 41 | my $seq = ''; 42 | my $line; 43 | open my $IN, '<', "$fname_ref" or die "Unable to read from $fname_ref: $!\n"; 44 | while ($line = <$IN>) { 45 | chomp($line); 46 | $seq .= $line unless ($line =~ /^>/); 47 | } 48 | close $IN; 49 | $len_ref = length($seq); 50 | 51 | $len_core = $perc * min($len_qry, $len_ref); 52 | if ($len_core <= $window) { 53 | $threshold = 1; 54 | } else { 55 | $threshold = 1 + int(($len_core - $window) / $step); 56 | } 57 | 58 | system("bin/ani.rb -1 $fname_qry -2 $fname_ref -T $fname_tsv -n $threshold -w $window -s $step -d 3 -t 1 -q > /dev/null 2>&1"); 59 | -------------------------------------------------------------------------------- /bin/check_conjugation_systems.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | if [ $# -ne 5 ]; then 4 | echo 'Usage: check_conjugation_systems.sh QUERY.faa OUTPUT.path TYPE TOPOLOGY DATABASE.path' 5 | echo ' TYPE: ordered_replicon or unordered_replicon' 6 | echo ' TOPOLOGY: circular or linear' 7 | exit 2 8 | fi 9 | 10 | source "$(dirname "${CONDA_EXE%/*}")"/etc/profile.d/conda.sh 11 | conda activate macsyfinder 12 | 13 | QRYFILE=$1 14 | OUT_DIR=$2 15 | SEQTYPE=$3 16 | SEQTOPO=$4 17 | DB_PATH=$5 18 | DB_DEFS=${DB_PATH}/definitions/ 19 | DB_PRFS=${DB_PATH}/profiles/ 20 | 21 | THREADS=20 22 | OUTFILE=${OUT_DIR}/'results_tab.tsv' 23 | OUTFILE_REPORT=${OUT_DIR}/'results_tab.report.tsv' 24 | OUTFILE_SUMMARY=${OUT_DIR}/'results_tab.summary.tsv' 25 | 26 | mkdir -p ${OUT_DIR} 27 | for conj_type in typeF typeB typeC typeFATA typeFA typeG typeI typeT; do 28 | macsyfinder ${conj_type} \ 29 | -w ${THREADS} \ 30 | -d ${DB_DEFS} \ 31 | -p ${DB_PRFS} \ 32 | --sequence-db ${QRYFILE} \ 33 | --db-type ${SEQTYPE} \ 34 | --replicon-topology ${SEQTOPO} \ 35 | -o ${OUT_DIR}/${conj_type} 36 | done 37 | 38 | # Plasmids with no MPF will have no result files. Must check if there is at least a file 39 | if compgen -G "${OUT_DIR}/*/macsyfinder.tab" > /dev/null; then 40 | awk 'ORS=NR%2?"\t":"\n"' ${OUT_DIR}/*/macsyfinder.tab | cut -f2- > ${OUTFILE} 41 | fi 42 | if compgen -G "${OUT_DIR}/*/macsyfinder.report" > /dev/null; then 43 | awk 'NR==1||FNR>1' ${OUT_DIR}/*/macsyfinder.report > ${OUTFILE_REPORT} 44 | fi 45 | if compgen -G "${OUT_DIR}/*/macsyfinder.summary" > /dev/null; then 46 | awk 'NR==1||FNR>1' ${OUT_DIR}/*/macsyfinder.summary > ${OUTFILE_SUMMARY} 47 | fi 48 | 49 | rm ${QRYFILE}.* $(dirname ${QRYFILE})/formatdb.err 50 | -------------------------------------------------------------------------------- /bin/get_ani_identity.pl: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env perl 2 | 3 | BEGIN { 4 | use strict; 5 | die "Old version of strict module\n" unless strict->VERSION >= 1.0; 6 | use warnings; 7 | die "Old version of warnings module\n" unless warnings->VERSION >= 1.1; 8 | use List::Util; 9 | die "Old version of List::Util module\n" unless List::Util->VERSION >= 1.41; 10 | use File::Basename; 11 | die "Old version of File::Basename module\n" unless File::Basename->VERSION >= 2.85; 12 | } 13 | 14 | use strict; 15 | use warnings; 16 | use List::Util qw[min]; 17 | use File::Basename qw[basename]; 18 | 19 | my $num_args = $#ARGV + 1; 20 | if ($num_args != 2) { 21 | print "\nUsage: get_ani_identity.pl qry_path ref_fofn\n"; 22 | exit; 23 | } 24 | 25 | my $fname_qry = $ARGV[0]; 26 | my $ref_fofn = $ARGV[1]; 27 | my $work_dir = $fname_qry . '_ani/'; 28 | my $fname_tmp = $work_dir . basename($fname_qry) . '.tmp'; 29 | 30 | my $len_qry; 31 | my $seq = ''; 32 | my $line; 33 | 34 | # Join multifasta contigs into one contig. TODO: Check other strategies (row of Ns, sort by length, ...) 35 | system("mkdir -p $work_dir"); 36 | open my $OUT, '>', "$fname_tmp" or die "Unable to write to $fname_tmp: $!\n"; 37 | open my $IN, '<', "$fname_qry" or die "Unable to read from $fname_qry: $!\n"; 38 | $line = <$IN>; 39 | print $OUT $line; 40 | while ($line = <$IN>) { 41 | chomp($line); 42 | $seq .= $line unless ($line =~ /^>/); 43 | } 44 | print $OUT $seq . "\n"; 45 | close $IN; 46 | close $OUT; 47 | $len_qry = length($seq); 48 | 49 | #my $threads = '70%'; 50 | #my $threads_conf = $work_dir . 'parallel_threads.conf'; 51 | 52 | #system("echo $threads > $threads_conf"); 53 | #system("cat $ref_fofn | parallel -j $threads_conf bin/get_ani_identity_job.pl $fname_tmp $len_qry {}"); 54 | system("cat $ref_fofn | parallel bin/get_ani_identity_job.pl $fname_tmp $len_qry {}"); 55 | 56 | opendir my $DIR, $work_dir or die "Unable to open directory $work_dir: $!\n"; 57 | my @files = grep(/\.tsv$/, readdir($DIR)); 58 | closedir $DIR; 59 | 60 | my $fname_tsv = $fname_qry . '.ani.tsv'; 61 | my $fname_dat; 62 | my $data; 63 | open $OUT, '>', "$fname_tsv" or die "Unable to write to $fname_tsv: $!\n"; 64 | for my $fname (@files) { 65 | $fname_dat = $work_dir . $fname; 66 | open $IN, '<', "$fname_dat" or die "Unable to read from $fname_dat: $!\n"; 67 | $data = <$IN>; 68 | close $IN; 69 | 70 | $line = $fname_qry . "\t" . substr($fname, 0, length($fname) - 4) . "\t" . $data; 71 | print $OUT $line; 72 | } 73 | close $OUT; 74 | 75 | system("rm -r $work_dir"); 76 | -------------------------------------------------------------------------------- /bin/hmmscan_domtblout_summarize.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import os 4 | import sys 5 | import argparse 6 | 7 | parser = argparse.ArgumentParser( 8 | description='Summarize hmmscan domtblout output file') 9 | parser.add_argument('infile', nargs='?', type=argparse.FileType('r'), 10 | default=sys.stdin, 11 | help='hmmscan domtblout file') 12 | parser.add_argument('outfile', nargs='?', type=argparse.FileType('w'), 13 | default=sys.stdout, 14 | help='summarized output') 15 | parser.add_argument('-e', '--Evalue_threshold', type=float, 16 | default=0.01, 17 | help='Evalue threshold') 18 | parser.add_argument('-i', '--iEvalue_threshold', type=float, 19 | default=0.01, 20 | help='iEvalue threshold') 21 | parser.add_argument('-c', '--coverage_threshold', type=float, 22 | default=0.6, 23 | help='coverage threshold') 24 | parser.add_argument('-sc', '--soft_coverage_threshold', type=float, 25 | help='soft coverage threshold') 26 | parser.add_argument('--version', action='version', version='%(prog)s 1.0') 27 | args = parser.parse_args() 28 | 29 | if (args.soft_coverage_threshold == None) or (args.soft_coverage_threshold > args.coverage_threshold): 30 | args.soft_coverage_threshold = args.coverage_threshold 31 | 32 | header = '\t'.join(('Query name', 'Relaxase MOB family', 'Profile HMM', 'Coverage', 'Start', 'End', 'Evalue', 'i-Evalue')) 33 | # args.outfile.write(header + '\n') 34 | 35 | hit_found = False 36 | partial_found = False 37 | prev_line_query_name = '' 38 | for line in args.infile: 39 | if line[0] == '#': 40 | continue 41 | items = line.strip().split() 42 | if len(items) != 23: 43 | print(os.path.basename(sys.argv[0]) + ':', 44 | 'error: bad domtblout format', line, file=sys.stderr) 45 | sys.exit(1) 46 | 47 | line_target_family = items[0].strip().split('_')[1][:4] 48 | line_target_name = items[0] 49 | line_query_name = items[3] 50 | line_evalue = items[6] 51 | line_ievalue = items[12] 52 | line_tlen = int(items[2]) 53 | line_hmm_from = int(items[15]) 54 | line_hmm_to = int(items[16]) 55 | line_hlen = line_hmm_to - line_hmm_from + 1 56 | line_coverage = line_hlen / line_tlen 57 | line_ali_from = items[17] 58 | line_ali_to = items[18] 59 | 60 | if prev_line_query_name != line_query_name: 61 | if hit_found or partial_found: 62 | output = '{0}\t{1}\t{2}\t{3:.2f}\t{4}\t{5}\t{6}\t{7}'.format(hit_query_name, hit_target_family, hit_target_name, hit_coverage, hit_ali_from, hit_ali_to, hit_evalue, hit_ievalue) 63 | args.outfile.write(output + '\n') 64 | hit_found = False 65 | partial_found = False 66 | prev_line_query_name = line_query_name 67 | 68 | if (float(line_ievalue) <= args.iEvalue_threshold) and (float(line_evalue) <= args.Evalue_threshold): 69 | if (line_coverage >= args.coverage_threshold): 70 | if hit_found: 71 | if float(line_ievalue) < float(hit_ievalue): 72 | hit_target_family = line_target_family 73 | hit_target_name = line_target_name 74 | hit_query_name = line_query_name 75 | hit_evalue = line_evalue 76 | hit_ievalue = line_ievalue 77 | hit_coverage = line_coverage 78 | hit_ali_from = line_ali_from 79 | hit_ali_to = line_ali_to 80 | else: 81 | hit_found = True 82 | partial_found = False 83 | 84 | hit_target_family = line_target_family 85 | hit_target_name = line_target_name 86 | hit_query_name = line_query_name 87 | hit_evalue = line_evalue 88 | hit_ievalue = line_ievalue 89 | hit_coverage = line_coverage 90 | hit_ali_from = line_ali_from 91 | hit_ali_to = line_ali_to 92 | elif (line_coverage >= args.soft_coverage_threshold) and not hit_found: 93 | if partial_found: 94 | if float(line_ievalue) < float(hit_ievalue): 95 | hit_target_family = line_target_family 96 | hit_target_name = line_target_name 97 | hit_query_name = line_query_name 98 | hit_evalue = line_evalue 99 | hit_ievalue = line_ievalue 100 | hit_coverage = line_coverage 101 | hit_ali_from = line_ali_from 102 | hit_ali_to = line_ali_to 103 | else: 104 | partial_found = True 105 | 106 | hit_target_family = line_target_family 107 | hit_target_name = line_target_name 108 | hit_query_name = line_query_name 109 | hit_evalue = line_evalue 110 | hit_ievalue = line_ievalue 111 | hit_coverage = line_coverage 112 | hit_ali_from = line_ali_from 113 | hit_ali_to = line_ali_to 114 | 115 | if hit_found or partial_found: 116 | output = '{0}\t{1}\t{2}\t{3:.2f}\t{4}\t{5}\t{6}\t{7}'.format(hit_query_name, hit_target_family, hit_target_name, hit_coverage, hit_ali_from, hit_ali_to, hit_evalue, hit_ievalue) 117 | args.outfile.write(output + '\n') 118 | -------------------------------------------------------------------------------- /macsyfinder.spec-file.txt: -------------------------------------------------------------------------------- 1 | # This file may be used to create an environment using: 2 | # $ conda create --name --file 3 | # platform: linux-64 4 | @EXPLICIT 5 | https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 6 | https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2020.12.5-ha878542_0.tar.bz2 7 | https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.35.1-hea4e1c9_2.tar.bz2 8 | https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-9.3.0-h6de172a_19.tar.bz2 9 | https://conda.anaconda.org/conda-forge/linux-64/libgomp-9.3.0-h2828fa1_19.tar.bz2 10 | https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-1_gnu.tar.bz2 11 | https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-9.3.0-h2828fa1_19.tar.bz2 12 | https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2 13 | https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.17.1-h7f98852_1.tar.bz2 14 | https://conda.anaconda.org/conda-forge/linux-64/expat-2.3.0-h9c3ff4c_0.tar.bz2 15 | https://conda.anaconda.org/bioconda/linux-64/hmmer-3.3.2-h1b792b2_1.tar.bz2 16 | https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2 17 | https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1007.tar.bz2 18 | https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.2-h58526e2_4.tar.bz2 19 | https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1k-h7f98852_0.tar.bz2 20 | https://conda.anaconda.org/conda-forge/linux-64/pcre-8.44-he1b5a44_0.tar.bz2 21 | https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h36c2ea0_1008.tar.bz2 22 | https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h516909a_1010.tar.bz2 23 | https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2 24 | https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.43.0-h812cca2_0.tar.bz2 25 | https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.9.0-ha56f1ee_6.tar.bz2 26 | https://conda.anaconda.org/bioconda/linux-64/perl-app-cpanminus-1.7044-pl526_1.tar.bz2 27 | https://conda.anaconda.org/bioconda/linux-64/perl-base-2.23-pl526_1.tar.bz2 28 | https://conda.anaconda.org/bioconda/linux-64/perl-common-sense-3.74-pl526_2.tar.bz2 29 | https://conda.anaconda.org/bioconda/linux-64/perl-compress-raw-bzip2-2.087-pl526he1b5a44_0.tar.bz2 30 | https://conda.anaconda.org/bioconda/linux-64/perl-compress-raw-zlib-2.087-pl526hc9558a2_0.tar.bz2 31 | https://conda.anaconda.org/bioconda/linux-64/perl-constant-1.33-pl526_1.tar.bz2 32 | https://conda.anaconda.org/bioconda/linux-64/perl-data-dumper-2.173-pl526_0.tar.bz2 33 | https://conda.anaconda.org/bioconda/linux-64/perl-digest-hmac-1.03-pl526_3.tar.bz2 34 | https://conda.anaconda.org/bioconda/linux-64/perl-digest-md5-2.55-pl526_0.tar.bz2 35 | https://conda.anaconda.org/bioconda/linux-64/perl-exporter-5.72-pl526_1.tar.bz2 36 | https://conda.anaconda.org/bioconda/linux-64/perl-exporter-tiny-1.002001-pl526_0.tar.bz2 37 | https://conda.anaconda.org/bioconda/linux-64/perl-extutils-makemaker-7.36-pl526_1.tar.bz2 38 | https://conda.anaconda.org/bioconda/linux-64/perl-html-tagset-3.20-pl526_3.tar.bz2 39 | https://conda.anaconda.org/bioconda/linux-64/perl-io-html-1.001-pl526_2.tar.bz2 40 | https://conda.anaconda.org/bioconda/linux-64/perl-io-zlib-1.10-pl526_2.tar.bz2 41 | https://conda.anaconda.org/bioconda/linux-64/perl-mozilla-ca-20180117-pl526_1.tar.bz2 42 | https://conda.anaconda.org/bioconda/linux-64/perl-parent-0.236-pl526_1.tar.bz2 43 | https://conda.anaconda.org/bioconda/linux-64/perl-scalar-list-utils-1.52-pl526h516909a_0.tar.bz2 44 | https://conda.anaconda.org/bioconda/linux-64/perl-socket-2.027-pl526_1.tar.bz2 45 | https://conda.anaconda.org/bioconda/linux-64/perl-try-tiny-0.30-pl526_1.tar.bz2 46 | https://conda.anaconda.org/conda-forge/linux-64/perl-xml-parser-2.44_01-pl526ha1d75be_1002.tar.bz2 47 | https://conda.anaconda.org/bioconda/linux-64/perl-xml-sax-base-1.09-pl526_0.tar.bz2 48 | https://conda.anaconda.org/bioconda/linux-64/perl-xsloader-0.24-pl526_0.tar.bz2 49 | https://conda.anaconda.org/conda-forge/linux-64/readline-8.0-he28a2e2_2.tar.bz2 50 | https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.10-h21135ba_1.tar.bz2 51 | https://conda.anaconda.org/conda-forge/linux-64/krb5-1.17.2-h926e7f8_0.tar.bz2 52 | https://conda.anaconda.org/bioconda/linux-64/perl-carp-1.38-pl526_3.tar.bz2 53 | https://conda.anaconda.org/bioconda/linux-64/perl-encode-2.88-pl526_1.tar.bz2 54 | https://conda.anaconda.org/bioconda/linux-64/perl-file-path-2.16-pl526_0.tar.bz2 55 | https://conda.anaconda.org/bioconda/linux-64/perl-html-parser-3.72-pl526h6bb024c_5.tar.bz2 56 | https://conda.anaconda.org/bioconda/linux-64/perl-io-compress-2.087-pl526he1b5a44_0.tar.bz2 57 | https://conda.anaconda.org/bioconda/linux-64/perl-list-moreutils-xs-0.428-pl526_0.tar.bz2 58 | https://conda.anaconda.org/bioconda/linux-64/perl-mime-base64-3.15-pl526_1.tar.bz2 59 | https://conda.anaconda.org/bioconda/linux-64/perl-ntlm-1.09-pl526_4.tar.bz2 60 | https://conda.anaconda.org/bioconda/linux-64/perl-storable-3.15-pl526h14c3975_0.tar.bz2 61 | https://conda.anaconda.org/bioconda/linux-64/perl-test-requiresinternet-0.05-pl526_0.tar.bz2 62 | https://conda.anaconda.org/bioconda/linux-64/perl-types-serialiser-1.0-pl526_2.tar.bz2 63 | https://conda.anaconda.org/bioconda/linux-64/perl-xml-namespacesupport-1.12-pl526_0.tar.bz2 64 | https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.35.4-h74cdb3f_0.tar.bz2 65 | https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.76.0-hc4aaa36_0.tar.bz2 66 | https://conda.anaconda.org/bioconda/linux-64/perl-business-isbn-data-20140910.003-pl526_0.tar.bz2 67 | https://conda.anaconda.org/bioconda/linux-64/perl-encode-locale-1.05-pl526_6.tar.bz2 68 | https://conda.anaconda.org/bioconda/linux-64/perl-file-temp-0.2304-pl526_2.tar.bz2 69 | https://conda.anaconda.org/bioconda/linux-64/perl-html-tree-5.07-pl526_1.tar.bz2 70 | https://conda.anaconda.org/bioconda/linux-64/perl-json-xs-2.34-pl526h6bb024c_3.tar.bz2 71 | https://conda.anaconda.org/bioconda/linux-64/perl-list-moreutils-0.428-pl526_1.tar.bz2 72 | https://conda.anaconda.org/bioconda/linux-64/perl-lwp-mediatypes-6.04-pl526_0.tar.bz2 73 | https://conda.anaconda.org/bioconda/linux-64/perl-net-ssleay-1.88-pl526h90d6eec_0.tar.bz2 74 | https://conda.anaconda.org/bioconda/linux-64/perl-pathtools-3.75-pl526h14c3975_1.tar.bz2 75 | https://conda.anaconda.org/bioconda/linux-64/perl-time-local-1.28-pl526_1.tar.bz2 76 | https://conda.anaconda.org/conda-forge/linux-64/python-2.7.15-h5a48372_1011_cpython.tar.bz2 77 | https://conda.anaconda.org/conda-forge/linux-64/curl-7.76.0-h979ede3_0.tar.bz2 78 | https://conda.anaconda.org/bioconda/linux-64/perl-archive-tar-2.32-pl526_0.tar.bz2 79 | https://conda.anaconda.org/bioconda/linux-64/perl-business-isbn-3.004-pl526_0.tar.bz2 80 | https://conda.anaconda.org/bioconda/linux-64/perl-http-date-6.02-pl526_3.tar.bz2 81 | https://conda.anaconda.org/bioconda/linux-64/perl-io-socket-ssl-2.066-pl526_0.tar.bz2 82 | https://conda.anaconda.org/bioconda/linux-64/perl-json-4.02-pl526_0.tar.bz2 83 | https://conda.anaconda.org/bioconda/noarch/perl-xml-sax-1.02-pl526_0.tar.bz2 84 | https://conda.anaconda.org/conda-forge/linux-64/python_abi-2.7-1_cp27mu.tar.bz2 85 | https://conda.anaconda.org/conda-forge/noarch/wheel-0.36.2-pyhd3deb0d_0.tar.bz2 86 | https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.11.28-py27h8c360ce_1.tar.bz2 87 | https://conda.anaconda.org/bioconda/linux-64/perl-file-listing-6.04-pl526_1.tar.bz2 88 | https://conda.anaconda.org/bioconda/linux-64/perl-uri-1.76-pl526_0.tar.bz2 89 | https://conda.anaconda.org/bioconda/linux-64/perl-xml-sax-expat-0.51-pl526_3.tar.bz2 90 | https://conda.anaconda.org/bioconda/linux-64/perl-http-message-6.18-pl526_0.tar.bz2 91 | https://conda.anaconda.org/bioconda/noarch/perl-net-http-6.19-pl526_0.tar.bz2 92 | https://conda.anaconda.org/bioconda/linux-64/perl-www-robotrules-6.02-pl526_3.tar.bz2 93 | https://conda.anaconda.org/bioconda/linux-64/perl-xml-simple-2.25-pl526_1.tar.bz2 94 | https://conda.anaconda.org/conda-forge/linux-64/setuptools-44.0.0-py27_0.tar.bz2 95 | https://conda.anaconda.org/bioconda/linux-64/perl-http-cookies-6.04-pl526_0.tar.bz2 96 | https://conda.anaconda.org/bioconda/linux-64/perl-http-daemon-6.01-pl526_1.tar.bz2 97 | https://conda.anaconda.org/bioconda/linux-64/perl-http-negotiate-6.01-pl526_3.tar.bz2 98 | https://conda.anaconda.org/conda-forge/noarch/pip-20.1.1-pyh9f0ad1d_0.tar.bz2 99 | https://conda.anaconda.org/bioconda/noarch/perl-libwww-perl-6.39-pl526_0.tar.bz2 100 | https://conda.anaconda.org/bioconda/linux-64/perl-lwp-protocol-https-6.07-pl526_4.tar.bz2 101 | https://conda.anaconda.org/bioconda/linux-64/entrez-direct-13.9-pl5262he881be0_2.tar.bz2 102 | https://conda.anaconda.org/bioconda/linux-64/blast-2.9.0-pl526he19e7b1_7.tar.bz2 103 | -------------------------------------------------------------------------------- /test/NZ_CP028167.1.faa: -------------------------------------------------------------------------------- 1 | >WP_049589868.1 phosphoethanolamine--lipid A transferase MCR-1.1 2 | MMQHTSVWYRRSVSPFVLVASVAVFLTATANLTFFDKISQTYPIADNLGFVLTIAVVLFGAMLLITTLLSSYRYVLKPVLILLLIMGAVTSYFTDTYGTVYDTTMLQNALQTDQAETKDLLNAAFIMRIIGLGVLPSLLVAFVKVDYPTWGKGLMRRLGLIVASLALILLPVVAFSSHYASFFRVHKPLRSYVNPIMPIYSVGKLASIEYKKASAPKDTIYHAKDAVQATKPDMRKPRLVVFVVGETARADHVSFNGYERDTFPQLAKIDGVTNFSNVTSCGTSTAYSVPCMFSYLGADEYDVDTAKYQENVLDTLDRLGVSILWRDNNSDSKGVMDKLPKAQFADYKSATNNAICNTNPYNECRDVGMLVGLDDFVAANNGKDMLIMLHQMGNHGPAYFKRYDEKFAKFTPVCEGNELAKCEHQSLINAYDNALLATDDFIAQSIQWLQTHSNAYDVSMLYVSDHGESLGENGVYLHGMPNAFAPKEQRSVPAFFWTDKQTGITPMATDTVLTHDAITPTLLKLFDVTADKVKDRTAFIR 3 | >WP_039003037.1 PAP2 family protein 4 | MPYLSNKRLLAEMSIALVMAIVATLTLEHSQIDLMVADWFYLGMGHWMVAKQAFLPDLLLYSGLKKLLMAMLIYLLVATICRAYHEKKGNAITAKWLVPVTKFRVRELAYLVLTLILVPTVVASLKAYTHVVCPVHLTIFDGTLPYLPMLDSMRNTIPDKCFPAAHASSGFALFAFAFAPSLRRRRGAIIIVVMALGWAMGCYKMIIGDHFLSHTVVSMMLAWAMSAGLAWVFFKKGEQV 5 | >WP_000577043.1 DUF2726 domain-containing protein 6 | MIFNRNKKRDEQHNSAVVLPSAENVFSDFIKNNPALTTNAFTAIGEVDCFTKVPLLSNNESEFLGILSRNIIPDYSVFPKIRLIEFVRPHGSEEAVKDLIREVQNITCDFVLKSYDETVLAVIQFGETATVRQQKKKLIIQRVCAMTGISFFEFKNTVDMMGSEDFCQFLRDDEE 7 | >WP_000997783.1 hypothetical protein 8 | MNDHSTDVANDAVYSDKKKISRFFYLLTPVRYLLAASLYLVVVIPLGILAGFRFLLQFLGGIAVMGLTIAWYMGADVSSYVVLAAWIALTVCGCAEQIFEWWIESRFAFRLFGIGRTPEE 9 | >WP_001353743.1 replication initiation protein 10 | MTTNKTSLSRLTKVRHRNELNSTLSTLPMAAKKVLFLAMCQINSKNEFDDDHIFYVTVADYIKWVQVKPDAAYLALRDGSNILDTTLLKLKHDEILELSSDLGFKFTKSNVPDSMNLSLTVFSTYYRNEGRVGIKFTKEAKRFLCKLIGEEKRYTTQVLLSVVRLTSVNAASLYQLIRKIYSNNSRANSFEMTIDELKDELNLYTIGAGGVKDYKYPDYPAFKRDVLNKSVKEIMKHTEVKNLSFVVSEKIGRKVYKLKFSYTIGYEGDTREDSEFTNMFDKMYPPEN 11 | >WP_001025395.1 J domain-containing protein 12 | MNIQEALNVFGLSGELTEKDIKAAYRKAALKYHPDRNPLGAELMKAVNAAFDVLMANIDKINQFQSTDEHARYNYGDDLEKVLNVLSGLSGLVFEVIGNWVWISGETITHKETLKEIGCKWAAKKKQWFYRPDEHKSYWNREEHTIEEIRAKYGTTGQRRATGWQRVETRA 13 | >WP_001083906.1 hypothetical protein 14 | MNSFFEQYHPVFEVVCRILGNGWRVNKLDDCSSRIKLTSPQFKNYSVHIRMEKDRFSVVGSVDSRSWRSPHHVCTLSRKRNPVDIAADIERKILVNASQEVLQAIEYEKHQVEKKDEILILKGMLSQLVQLESWYGALTGFKAENGLNGKVTEQGDSYDLQIRGLSIDQLVKITGYLKQL 15 | >WP_001230707.1 hypothetical protein 16 | MRKNKGDVTYFLEKEGDNYRLTKRIKARTNVKIGNKTTKITLYDAVLNENELQHIDFTCAGLREDDETPVKNLIKEFMLNETR 17 | >WP_000801440.1 hypothetical protein 18 | MKPDNTPENVKKLRLKAGLTQKECSHIYGVGLRTWQKKEEVNTQNSQSLSLVEFEFLLLLAGEHPEYVLCKRESK 19 | >WP_000650309.1 hypothetical protein 20 | MKCSSVFTSTTNHVFTFERVTLCTIILMHKDTGQQYVVIFTDNNKIRDYKAGIVPQFGELKQSDVDLVLFYRDEYEKYFDSLKDGDECLSFKDFIECLC 21 | >WP_000854259.1 hypothetical protein 22 | MKTLKQAAMQFASDLRNNRCFKAAYNDALLELDREDLNAIVQCTFLFEPKRTLAELDKLIKKIEVEEDYEFCVSLDKGKGLHEVRTFKSEREALDAYRILTLYGYTVTIEKKEGND 23 | >WP_000520549.1 type II toxin-antitoxin system HicA family toxin 24 | MGKTDKLLAKFLNSKKTFEWDELVVLFSSLGYVKKEMQGSRVRFFNAEINHTILMHRPHPESYIKGGTLKAIKQNLKEAGLL 25 | >WP_000681613.1 type II toxin-antitoxin system HicB family antitoxin 26 | MKHLKYKGYLGTVEPDFENNVLYGKLAFIRDLVTYEASTLAELEQEFKTSVDLYLQSCVEDGKEPDTPFKGVFNVRLDPELHRRVAEMAMEEDLSLNAFVNKALEKEVSNHRAGA 27 | >WP_000781813.1 hypothetical protein 28 | MKMIAFRLSDDELKFAEHNAILSGFTSINAFAKHNVLNIETKPVNIPVNNEPAKLVSVRLYPHEIELVKRNAALHGMSMSREIAIRVRQSLLKSEVCLYPDEVKELKKLSTAVDRVGRNIHFIIKGERFCTVNDPDFRKDVIEVIELCKQIDSKLETLTKSVVNRFG 29 | >WP_001329504.1 relaxase/mobilization nuclease domain-containing protein 30 | MGVYVEQEYRIKRAKGTGRDPKSPKLSGRHIHASKSAFNHKVRHGADKKAYTWTPTKEVTFKITGSGKTAAGIKNGIDYITRNGELEAYCYDGKGSEQTGKGEDFNREFTSTLSKGNDYSRTYRGENIDHVKNMVFSPPPEAGVSREDALKATVEFLKETYPNHAFVAVYHDDKEDHPHVHVNIKLRDEETGRRLRLTKSETRKFRNGFHRKLKGMGYDVTATWKKDPERKREIERLQAENPKRLRNVYKVVDFGETSYQNKAGEKRTPFLTYETLKGGKQVTIWGKDLKNHFESEKLQPGTLIKVKKLAPTLVRSPMFNDDGTVAGYRETHRNNWQIENIALERNRERQVHERETRQPEESDVKKQLSRKHEQGHNIGFALEHGFTKDSEEHKKLRIQQERNWKGLGF 31 | >WP_060598542.1 LuxR family transcriptional regulator 32 | MTEHDAICISGLHQIFSDEEHLSEQQKDIILMYAYGYTLNEIADFKGLKPSTVRKYLDSVRAELGGVSLAGIRTLVLIRTNALLVSSLSRISERGNL 33 | >WP_000760375.1 EexN family lipoprotein 34 | MKKYLLFALPFFVVGCSEEVKSVDWWGQHLTEAKQKQAECEKSGSDSQNCKNVKQALFIQSQKDAPVPTFD 35 | >WP_001328551.1 TrbC/VirB2 family protein 36 | MQRKNKVVMAVAGATVASPAFADGFSKAETLLEKVKTGLSGLSLVTVTIACLWVGYKVLFGGSTIRECSPIIIGAIVIASAAEVASMMVN 37 | >WP_000105985.1 VirB4 family type IV secretion/conjugal transfer ATPase 38 | MSTLYKAMTRPAMYVGVPVVPLTVVAGALFLAGVYISKLIWLAIPVAVFVLRMITKQDDHIFNLYFLKLKMLGNSVCNRFFGARAFLSGQYEAVEIDEFVNAMKLNERITTGKYIPYSSHVDKNIVKTKNGDYVATWQLMGINFESISAEMLETIDSQVATLVRSFSGLPVSFYNHSCRASFYDAFTTKSGNKYADIISDCYYGSMKKNKFKGNTLYFTLIYRPDGRVEKLEKRKKSIKEKKDDINIHVKRMNEMINTFSGALDKFTCKLLGMYEENGKVFSSQLSFYNYLLTGKLQKIRVTDSPVYNVLGGVDVFFNHDTGQICRIDGNKFFRSIEIKDFCSETASGVFDVLQYSDADYIITHSYTSMSKSEALSTIKRAEKQLKSTEDDAVTQLQELEKAKNDIVSGDISFGYYHFTLMVMADSIRELDESVSKITADFTDLGIIPALSTMSLPAAYFAQLPAVFHLRPRLSPVSNVNFVELASFHNFYQGKRDKNCWTEAVAILKTPSKQAYYLNLHNSVLFKDETGEKNLANTKVIGTAGSGKTMFLSYLACSLQKYNNPETFADSAKNKKLTCVFLDKDRGAELCIRMLGGEYYTVKSGEPTGWNPFALEATKRNRIFVKQLMEILCTRNGERLSTRERLLISESVDAVMDFPPGEMREYGITRMLEHLMQRDDRDEQENGIILRLSQWANGQAHGWVFDNAKDTFNIQHVNNFGIDGTEFLDDPMVCAPITFYLLYRITQLLDGRRLVIFLDEFWKWLQDEAFSDFVYNKLKTIRKLNGLVIPATQSPDEILKNKISRAVVEVCSTSIYLANPDADYNDYVEGLKLTPEEFNIVKNLDPMSRQFLIKKSSLKKGDGKSFSALATLDLSGLGGYLKILSASADNLEIFESIYHEGMEPDDWVPEYLERAI 39 | >WP_000832283.1 type IV secretion system VirB5 protein 40 | MKRVKTLMLISLLTASFYSSAGIPVAIDASPEWAVEAARWTERLKQWSETAQHYQSQIQAYKDQLATATGIRNIAAFTNELSNLQSELTNIYKQGNSYISDFTSNPEGALSSQAKNLFSKYGAFDMCNTGYERNDNLCKARIVSTAASIEQGNEINKQLSSAMSQIQSLSSRIEASKDIKESQDLANALQAQSLKMQAIKMQYDVWNNKNKADHEMLVTQEQEAFIKQQKEAEPLTFD 41 | >WP_000046889.1 TriE protein 42 | MAQGFFVKYNSTVMDSVDKISSSYQTQFANDIMSLATVSVTLYVLWKGYQILASKTQTPLQDLVWDLSKFAIIIMFITNADGYLTAATDALQGMKDGFSGGVSVWQTLDNLWKSTQNLGAEIYSLDKSTYVKDQGVVGQFLIWTGSLILMAVSVVVFLTADVTMKLLIITAPIFIFCLMFGFIRSMFNNWLQSLFSSILTVLFASLVIRIAMDFQGMILSHAIRAAQTGNVNLVSTGAMGFMAGFLGALLVLIAKGFAVQLAGAGVEGAVQGAAMMGLGAAGMATGKSLMLGGRAGLGFGLGMAGRTGMNSLSGKAGNLMGRGARTAAEWAGEKGYQALAPTGALGMKARRLASLEKARARNAA 43 | >WP_001290765.1 type IV secretion system protein 44 | MSEKIEKKIETAKSFERQLYAENERSKKNAWRVAVAASLLSLCLGAAICVLVPLKEKELAIVAVGEKTGRTELITQVKQEKILQSEALGRYFVNTYITLREGYNYPSLQYDYETVQLYSSNTVKDDYLRLYNSDMAPDKIYHNNGSYVAVEVISNIISDATAPDKLASVRFKKTTRNFTTGQVSVSYWTARVTYRFEPEKSVKSSSRELNPLGFTVTSYQTDREVRGE 45 | >WP_001329500.1 P-type conjugative transfer protein VirB9 46 | MMKKLFIATCLLLPGLVYSAATPLPSGFDARMQTVSYNGANTTVIRSKTGFLTSVVFDEGEAVISAKAGFPAGWEITTDDNVVYINPRPVVQEQEGDEGEKLKKVFQPTEKEWDTNLFVRTTKRIYSLDLILLSEEKQAQPAYVVQFRYPSEIAKKNAEEVRLAKEKQEKLRQKKLISESFEKADAPKNWNYFMRVNEKYDSRRIAPDFAYDNGIFTFLGFNSGKVFPAPFAVRDGQEQTLAFNVETKGKYKIMVIHNVNDKFVLRYGNSVVGVVNKSFGKVLTDQRNTSSPAVERVEVNND 47 | >WP_000131271.1 TrbI/VirB10 family protein 48 | MTDMKKTDDLVDDANQSKLESKPVHDIGDIRNKNNRMRSGFLFGLMAVLVIGIFALKTYKNYFADDQQKASESTGDTSISQVSKIRTGLGQNFDPVENKAIINPGATGSGNTVSDGHKEVPQEGFRKYLSIPVAGQGGSQTGASGSSRTSQTSEPQEERKESKENVPGKSGMKVTAINLDPDLYIEENRLIPCALTTRFVSDVAGRISCVFTEDVWSANHHTKLLEQGTKAFGRYQTGTLNHGQGRMFVMWEQLRTPDNKRIDMVNTAAAGPLGEAGIDGWIDSHFWERFGGSLMLSMVQDVAAAAADNAPGKDRNVDYTENSRQAMAEMAKVALENSINIPPTMYKNQGDIITIMVGEDIDFSDIYELKVK 49 | >WP_000017226.1 P-type DNA transfer ATPase VirB11 50 | MSGVIPVSISNKSLDFYKNQVFQSFLDIEGLTEIAVNRPGEIWTEINGDWTFHNNDSVTFDFCKRFSHTLASFRGDEIGETKPLLSATLESGERVQVVFPPACERNTISITIRKPSTRQITHKEYVANGFYDYVQSGKKHRTYDDELLDLFRARNIAAFMELAVAAGKTIVFAGATGSGKTTYMKSLIDFIPLNTRLITIEDAEEIKFFIHKNYVHLFYPSESGSDSGSIITAAKLIKSCLRMKPDRILLAEIKGGDAWDFVKVAGSGHSGSMTSIHAASAKDAIIQMVTKCYQNRECQNLPFDVLRKIIMDSIDIVVHVGRDGVVRHMSDIYYKGAECENF 51 | >WP_060598543.1 type IV secretory system conjugative DNA transfer family protein 52 | MLVAAWFAGSFIFFVLYFSQIKNLKPLKAVFRSIDAYRMDIFTDSLTLSVVTTDIRVLAFVALGAGFVVSLIIPVAALIKLNEKKENIFGDARFATINDIRESNSFTLDGDEKDGIIVGIKDKKIIRYVGAAFSAMGAGTRAGKGAGIVITNLMKYWWSVIILDPKRECFNITSLIRKVILGHEVYKFDPFSSVTHRFNPLYYVSMGTSEGFNQLENLSLIIYPYKTDGADAGSYLNKTAGGVFKSYAVALWFMIKNDKAGLKTLDIEPVFSMSKINQLFERAEPEHLLSFMNDIRSELRGKDKTLADIGVAGLKAFIEMEDKTKAQLKSNFLNGLSPFANPNVANATDGNDFDLRQVRKKRMTIYFCISGDNARLAEKITNIFFQLAIQVNLEKMPTDDPEIKHDCLFLLDEFPSIGAVDYIKSKSGLIAGYKLKLLIVYQVGSQLEEIYSYAGSKTLLASAPCKIVYSASDVKDARELSEAMGTRTVTIGSKSKSRSRGGTSRSESESLIERPLVTTNELLTLKFSEEILAMKGENPIRCEKAFYYLNEYFFGDFVKVAPELANIFPRKKGKLVMPPQKVFENEIVAKGYLVVKDVPDLDKAA 53 | >WP_015059903.1 hypothetical protein 54 | MMKKLVFLLISILAGCSSPPEPTPVQFEKANEVINPSLPYVPDFHGVIKSDVSGKGWVYEITSLSGVQARTPTFYYALAHADRIVVTTHDAGLWFRIRDLLKMEGATAVVEWRNEKSFLPEQVKIVFIKAQNEVKNDWKK 55 | >WP_001350475.1 putative conjugal transfer protein 56 | MTGKNRVVLPALLALGLISTNANASDPCASVLCLYGKAVGQGGGSECRSAEKDFFNILKKKKGSIRWSKTFDARKAFLNQCSTADPAAISKIMSKFGRVRG 57 | >WP_001198960.1 hypothetical protein 58 | MQYALFDGFERKFLLDALEFGVLKDWKENPVKELPDIDESAHPFHICYGGYLLNPGVSDSDISRKIKDQAGFWLAAIDDTRMDCHSIAYYDIHTLPLISCGHQKIVPFAALIKADECIISKIASYSGFAVTAFLRIKEWDIATNILNREGIFAFNGCERRFRVVSKDNWQHTVSEERAIRCAKRLIQCKG 59 | >WP_001235745.1 type IA DNA topoisomerase 60 | MRLFIAEKPAVANDIVKALGGNFTRHDGWFESDNAIVTNCFGHIIESQPPENYNPEYKAWKVETLPLRLYPVKYQPVESAAKQVKTILELIRRGDVTEIVHAGDPDDEGQLLVDEVLEYAGNTKPVKRVLINDNTLPAVKKALANLKDNRDFKGLYLKALARSVADAVYGFSMTRAYTIPAKARGYQGVLSVGRVQTPVLGLIVNRTRANQNHKSSFYYTMTGVFQRGADVIRANWKPGEFAPLTDRKLLDKAWADGTAASLAGKPATVEAAATDDKKTAAPLPFNLVRLQQYMNKKFKMTAQKTLDITQQLREKYKAITYNRSDCSYLSDEQFSEAPQVIDALKSVFPQSLDIDSARKSKAFNSAKVTAHTAIIPTASVPDVNALSTDERNVYLAIAQHYLVQFMPEKAYQEVSVAIQCGDESFYARARKTTDSGFEAFLGAETTDEGESEDNDDSAFELLCKIRTGETLTTKEVIVNEKKTTPPPLFTEASLLAALVRVADFVTDPTIKKLLKDKDKDKKDEHGGIGTPATRAAILETLKKRNYITLEKGKLIPTDTGYALIDALPGIAVNPDMTALWSEKQAAIENGDLTVEQFINDLYGELTGMISDVDLGEMKIEPAAPAGQFQRLDSPCPSCGKHIVIRPKGYFCTGCEFKIWSEFSGKKITQAQAEKLVKSGKTDLIKGFKKKSGGTYDTVLVLEDKKTGKLGFPARAKK 61 | >WP_000850859.1 hemolysin expression modulator Hha 62 | MKTKQEWLFQLRKCTSRDTLEKVIEINRYKLPLSESEAFYSAADHRRAELVMNKLYDKVPSGVWKYVH 63 | >WP_001293134.1 H-NS histone family protein 64 | MSELTKEDEYGIISRTMMNIRSLRVFAREIDFEQLLEMQEKLNVVIEERREDAEREAAERQELEAKRQQAIEYIISLGLDPESLLAPVTADTVKTRRKAKGGVRKAKYRFKDENGEIREWSGNGKRPLALQKLLDEGHFMEDFLIEKTKPEQAE 65 | >WP_001067855.1 IS6-like element IS26 family transposase 66 | MNPFKGRHFQRDIILWAVRWYCKYGISYRELQEMLAERGVNVDHSTIYRWVQRYAPEMEKRLRWYWRNPSDLCPWHMDETYVKVNGRWAYLYRAVDSRGRTVDFYLSSRRNSKAAYRFLGKILNNVKKWQIPRFINTDKAPAYGRALALLKREGRCPSDVEHRQIKYRNNVIECDHGKLKRIIGATLGFKSMKTAYATIKGIEVMRALRKGQASAFYYGDPLGEMRLVSRVFEM 67 | >WP_000587671.1 P-loop NTPase 68 | MIIVIGGDKGGTGKSHLATNLTVCLSQQGKRTGLVETDLNGSTKKWNKRREQAGLPPVALNEAYGDISAKITKMADVAEILIIDTAGYDSTEFRTALKVADIVIVPIDPLAQVEADSLQTVTKIVRDAQKINPRLAAHVLLYKCQPNTFSEQQELRDSLNSHDYWLKPMKSTVSFLRAFVRAMNQGMGVHELKSNVSGASQAKAQIELLLKELEL 69 | >WP_001086090.1 hypothetical protein 70 | MAKLELPELEQNQEVNATKFVQDAGKRPTENKTKLASFRIPNELLEFIDNESKRLHRPKTKIIKAALLAYQDLDENELINLWFRADRND 71 | -------------------------------------------------------------------------------- /bin/post_install_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | main(){ 4 | check_bash 5 | check_conda 6 | check_perl 7 | check_python3 8 | 9 | check_graph_tool 10 | check_numpy 11 | check_pandas 12 | 13 | check_blastn 14 | check_prodigal 15 | check_hmmscan 16 | check_plasmidfinder 17 | check_macsyfinder 18 | 19 | check_ruby 20 | check_parallel 21 | check_ani_rb 22 | 23 | test_plasmid_NZ_CP028167 24 | test_plasmid_NZ_CP028329 25 | } 26 | 27 | version_check(){ 28 | # Based on https://unix.stackexchange.com/questions/285924/how-to-compare-a-programs-version-in-a-shell-script 29 | VERSION=$1 30 | REQUIRED=$2 31 | 32 | if [ "$(printf '%s\n' "${VERSION}" "${REQUIRED}" | sort -V | head -n1)" = "${REQUIRED}" ]; then 33 | echo " OK - Current version: ${VERSION}" 34 | return 0 35 | else 36 | echo ' Warning! Please verify the installed version' 37 | echo " Current version: ${VERSION}" 38 | echo " Minimum version required: ${REQUIRED}" 39 | return 1 40 | fi 41 | } 42 | 43 | check_bash(){ 44 | EXE='bash' 45 | echo "Checking for ${EXE} ..." 46 | 47 | EXE_PATH=`which ${EXE}` 48 | if [ -z "${EXE_PATH}" ]; then 49 | echo " Error! ${EXE} is not in your PATH" 50 | else 51 | VERSION=`bash --version | head -n1 | cut -d' ' -f4` 52 | REQUIRED='4.0' 53 | version_check ${VERSION} ${REQUIRED} 54 | fi 55 | } 56 | 57 | check_conda(){ 58 | EXE='conda' 59 | echo "Checking for ${EXE} ..." 60 | 61 | EXE_PATH=`which ${EXE}` 62 | if [ -z "${EXE_PATH}" ]; then 63 | echo " Error! ${EXE} is not in your PATH" 64 | else 65 | VERSION=`conda --version | cut -d' ' -f2` 66 | REQUIRED='4.5' 67 | version_check ${VERSION} ${REQUIRED} 68 | fi 69 | 70 | CONDA_SHELL_INT="$(dirname "${CONDA_EXE%/*}")"/etc/profile.d/conda.sh 71 | if [ ! -e "${CONDA_SHELL_INT}" ]; then 72 | echo " Error! Please check how conda integrates with your shell" 73 | echo " For copla to activate the macsyfinder environment it is assumed that ${CONDA_SHELL_INT} can be sourced" 74 | fi 75 | } 76 | 77 | check_perl(){ 78 | EXE='perl' 79 | echo "Checking for ${EXE} ..." 80 | 81 | EXE_PATH=`which ${EXE}` 82 | if [ -z "${EXE_PATH}" ]; then 83 | echo " Error! ${EXE} is not in your PATH" 84 | else 85 | VERSION=`perl -v | head -n2 | tail -n+2 | cut -d' ' -f9 | tr -d [\(\)]` 86 | REQUIRED='5.22' 87 | version_check ${VERSION} ${REQUIRED} 88 | fi 89 | } 90 | 91 | check_python3(){ 92 | EXE='python3' 93 | echo "Checking for ${EXE} ..." 94 | 95 | EXE_PATH=`which ${EXE}` 96 | if [ -z "${EXE_PATH}" ]; then 97 | echo " Error! ${EXE} is not in your PATH" 98 | else 99 | VERSION=`python3 -V | cut -d' ' -f2` 100 | REQUIRED='3.8' 101 | version_check ${VERSION} ${REQUIRED} 102 | fi 103 | } 104 | 105 | check_graph_tool(){ 106 | PYTHON_MODULE='graph_tool' 107 | echo "Checking for ${PYTHON_MODULE} ..." 108 | 109 | MODULE_LOAD=`python3 -c "import ${PYTHON_MODULE}" 2>&1` 110 | if [ -z "${MODULE_LOAD}" ]; then 111 | VERSION=`python3 -c "import ${PYTHON_MODULE}; print(${PYTHON_MODULE}.__version__)" | cut -d' ' -f1` 112 | REQUIRED='2.33' 113 | version_check ${VERSION} ${REQUIRED} 114 | else 115 | echo " Error! ${PYTHON_MODULE} is not in your PATH" 116 | fi 117 | } 118 | 119 | check_numpy(){ 120 | PYTHON_MODULE='numpy' 121 | echo "Checking for ${PYTHON_MODULE} ..." 122 | 123 | MODULE_LOAD=`python3 -c "import ${PYTHON_MODULE}" 2>&1 ` 124 | if [ -z "${MODULE_LOAD}" ]; then 125 | VERSION=`python3 -c "import ${PYTHON_MODULE}; print(${PYTHON_MODULE}.__version__)"` 126 | REQUIRED='1.19.1' 127 | version_check ${VERSION} ${REQUIRED} 128 | else 129 | echo " Error! ${PYTHON_MODULE} is not in your PATH" 130 | fi 131 | } 132 | 133 | check_pandas(){ 134 | PYTHON_MODULE='pandas' 135 | echo "Checking for ${PYTHON_MODULE} ..." 136 | 137 | MODULE_LOAD=`python3 -c "import ${PYTHON_MODULE}" 2>&1` 138 | if [ -z "${MODULE_LOAD}" ]; then 139 | VERSION=`python3 -c "import ${PYTHON_MODULE}; print(${PYTHON_MODULE}.__version__)"` 140 | REQUIRED='1.1.0' 141 | version_check ${VERSION} ${REQUIRED} 142 | else 143 | echo " Error! ${PYTHON_MODULE} is not in your PATH" 144 | fi 145 | } 146 | 147 | check_blastn(){ 148 | EXE='blastn' 149 | echo "Checking for ${EXE} ..." 150 | 151 | EXE_PATH=`which ${EXE}` 152 | if [ -z "${EXE_PATH}" ]; then 153 | echo " Error! ${EXE} is not in your PATH" 154 | else 155 | VERSION=`blastn -version | head -n1 | cut -d' ' -f2` 156 | REQUIRED='2.9.0' 157 | version_check ${VERSION} ${REQUIRED} 158 | fi 159 | } 160 | 161 | check_prodigal(){ 162 | EXE='prodigal' 163 | echo "Checking for ${EXE} ..." 164 | 165 | EXE_PATH=`which ${EXE}` 166 | if [ -z "${EXE_PATH}" ]; then 167 | echo " Error! ${EXE} is not in your PATH" 168 | else 169 | VERSION=`prodigal -v 2>&1 | head -n2 | tail -n+2 | cut -d' ' -f2 | sed 's/^V//' | sed 's/:$//'` 170 | REQUIRED='2.6.3' 171 | version_check ${VERSION} ${REQUIRED} 172 | fi 173 | } 174 | 175 | check_hmmscan(){ 176 | EXE='hmmscan' 177 | echo "Checking for ${EXE} ..." 178 | 179 | EXE_PATH=`which ${EXE}` 180 | if [ -z "${EXE_PATH}" ]; then 181 | echo " Error! ${EXE} is not in your PATH" 182 | else 183 | VERSION=`hmmscan -h | head -n2 | tail -n+2 | cut -d' ' -f3` 184 | REQUIRED='3.1' 185 | version_check ${VERSION} ${REQUIRED} 186 | fi 187 | } 188 | 189 | check_plasmidfinder(){ 190 | EXE='plasmidfinder.py' 191 | echo "Checking for ${EXE} ..." 192 | 193 | EXE_PATH=`which ${EXE}` 194 | if [ -z "${EXE_PATH}" ]; then 195 | echo " Error! ${EXE} is not in your PATH" 196 | else 197 | echo ' OK' 198 | fi 199 | } 200 | 201 | check_macsyfinder(){ 202 | EXE='macsyfinder' 203 | echo "Checking for ${EXE} ..." 204 | 205 | EXE_CONDA=conda 206 | EXE_CONDA_PATH=`which ${EXE_CONDA}` 207 | if [ -z "${EXE_CONDA_PATH}" ]; then 208 | EXE_PATH=`which ${EXE}` 209 | if [ -z "${EXE_PATH}" ]; then 210 | echo " Error! ${EXE} is not in your PATH" 211 | else 212 | VERSION=`macsyfinder --version 2>&1 | head -n1 | cut -d' ' -f2` 213 | REQUIRED='1.0.5' 214 | version_check ${VERSION} ${REQUIRED} 215 | fi 216 | else 217 | source "$(dirname "${CONDA_EXE%/*}")"/etc/profile.d/conda.sh 218 | conda activate macsyfinder 219 | 220 | EXE_PATH=`which ${EXE}` 221 | if [ -z "${EXE_PATH}" ]; then 222 | echo " Error! ${EXE} is not in your PATH. Please verify the macsyfinder conda environment" 223 | else 224 | VERSION=`macsyfinder --version 2>&1 | head -n1 | cut -d' ' -f2` 225 | REQUIRED='1.0.5' 226 | version_check ${VERSION} ${REQUIRED} 227 | fi 228 | 229 | conda deactivate 230 | conda activate copla 231 | fi 232 | } 233 | 234 | check_ruby(){ 235 | EXE='ruby' 236 | echo "Checking for ${EXE} ..." 237 | 238 | EXE_PATH=`which ${EXE}` 239 | if [ -z "${EXE_PATH}" ]; then 240 | echo " Error! ${EXE} is not in your PATH" 241 | else 242 | VERSION=`ruby -v | cut -d' ' -f2` 243 | REQUIRED='2.3' 244 | version_check ${VERSION} ${REQUIRED} 245 | fi 246 | } 247 | 248 | check_parallel(){ 249 | EXE='parallel' 250 | echo "Checking for ${EXE} ..." 251 | 252 | EXE_PATH=`which ${EXE}` 253 | if [ -z "${EXE_PATH}" ]; then 254 | echo " Error! ${EXE} is not in your PATH" 255 | else 256 | VERSION=`parallel --version | head -n1 | cut -d' ' -f3` 257 | REQUIRED='20161222' 258 | version_check ${VERSION} ${REQUIRED} 259 | fi 260 | } 261 | 262 | check_ani_rb(){ 263 | EXE='ani.rb' 264 | echo "Checking for ${EXE} ..." 265 | 266 | EXE_PATH='bin/${EXE}' 267 | if [ -z "${EXE_PATH}" ]; then 268 | echo " Error! ${EXE} is not in your PATH" 269 | else 270 | echo ' OK' 271 | fi 272 | } 273 | 274 | test_plasmid_NZ_CP028167(){ 275 | SEQ_FNA='test/NZ_CP028167.1.fna' 276 | SEQ_FAA='test/NZ_CP028167.1.faa' 277 | 278 | COPLA_DB=`grep '^COPLA_DB_DIR' copla.ini | cut -f2` 279 | OUTPUT_DIR=${SEQ_FNA}_output 280 | OUTPUT=${SEQ_FNA}_stdout 281 | TMP_FILE=${SEQ_FNA}_tmp 282 | 283 | echo 'Checking COPLA with plasmid NZ_CP028167.1 ...' 284 | echo '==========' 285 | bin/copla.py ${SEQ_FNA} ${COPLA_DB}/RS84f_sHSBM.pickle ${COPLA_DB}/CoplaDB.fofn ${OUTPUT_DIR} \ 286 | -a ${SEQ_FAA} -t circular -k Bacteria -p Proteobacteria -c Gammaproteobacteria -o Enterobacterales \ 287 | -f Enterobacteriaceae -g Escherichia -s 'Escherichia coli' | tee ${TMP_FILE} 288 | echo '==========' 289 | DIFF_OUT=`diff ${OUTPUT} ${TMP_FILE}` 290 | if [ -z "${DIFF_OUT}" ]; then 291 | echo ' OK - Output for plasmid NZ_CP028167.1 is as expected' 292 | else 293 | echo ' Error! Output for plasmid NZ_CP028167.1 differs. Please verify COPLA installation' 294 | fi 295 | rm ${TMP_FILE} 296 | } 297 | 298 | test_plasmid_NZ_CP028329(){ 299 | SEQ_FNA='test/NZ_CP028329.1.fna' 300 | 301 | COPLA_DB=`grep '^COPLA_DB_DIR' copla.ini | cut -f2` 302 | OUTPUT_DIR=${SEQ_FNA}_output 303 | OUTPUT=${SEQ_FNA}_stdout 304 | TMP_FILE=${SEQ_FNA}_tmp 305 | 306 | echo 'Checking COPLA with plasmid NZ_CP028329.1 ...' 307 | echo '==========' 308 | bin/copla.py ${SEQ_FNA} ${COPLA_DB}/RS84f_sHSBM.pickle ${COPLA_DB}/CoplaDB.fofn ${OUTPUT_DIR} \ 309 | -t circular -k Bacteria -p Firmicutes -c Bacilli -o Lactobacillales -f Lactobacillaceae \ 310 | -g Lactobacillus -s 'Lactobacillus sp. D1501' | tee ${TMP_FILE} 311 | echo '==========' 312 | DIFF_OUT=`diff ${OUTPUT} ${TMP_FILE}` 313 | if [ -z "${DIFF_OUT}" ]; then 314 | echo ' OK - Output for plasmid NZ_CP028329.1 is as expected' 315 | else 316 | echo ' Error! Output for plasmid NZ_CP028329.1 differs. Please verify COPLA installation' 317 | fi 318 | rm ${TMP_FILE} 319 | } 320 | 321 | main 322 | -------------------------------------------------------------------------------- /test/NZ_CP028329.1.faa: -------------------------------------------------------------------------------- 1 | >WP_013356270.1 ParA family protein 2 | MDIPDVVKAIQSRDEALTIVIGNQKGGVGKTTNTYLIAYTLAKMGIHTLVADLDPQANATKTLMLTKSQQEDTVYSIKKTLMVGVQEKDLTDLPVKIMDNLDLIPSYIDFQDFTKYLYQNTNNEYEETHLLEPLFNPLKKKYDVILLDVPPFSIEITRNAVIFSDFALISLQTHDDSLSGAEEYVNTLSKLQQEYQLDIEVIGILPMLHDARNGVDQTIIQSAKDEFGEENVFTNIVTQMARIKRFPINGITDKDRFDKRVLDKYQQVTDELLSRIGLFIDDKEAK 3 | >WP_016526703.1 hypothetical protein 4 | MPSKKPNILQRRVTGAVKPSEEYHATDNATKKENKSSSMPKNQKKSLKVSAETYKDMKVLKTIEHVSFDYEIIQLLIDQYYKDMTEEQQRRYTVLRENM 5 | >WP_013356271.1 IS30-like element ISLpl1 family transposase 6 | MSSITYSERIKIETFCELGLSNIQMGVRLNRSPSTISYELSRCQPYQAELAQTDAEYKRSRCGRKTKLSDELKQKILNHLRLSWSPGMIAHEFKLATKSIYNWLNQGRIGFSLNDLPEHGVRQRRNVDQRSKYNQSLGRSIEQRPMMINQRKRIGDFELDTVVGPRGHSKAVLLTLIDRKSRFLWAYRLKDRTTATVNEALTKFLTTFNGPVHSFTVDRGTEFSGLVSLESQYGIKTYYCHAYTPAERGSNERFNRNLRYFYPKGTRFEHISAQDLTTTLLQINQRPLKILDWQTPYQVILTNLSKNSD 7 | >WP_072533463.1 IS30 family transposase 8 | MTYTHLTIDELATIYSFCKLGKKAYLVAPALHRSAETVYRIYRFLDAGGTIIDYQCHYRKHKQHCGRKPIQLRPDELAYIQAKTQAGWQPDTIINRQERAFSCGVRTLYRLFKRGVLGLSTKDLPMHGKRHPNGYIEHRGKAGQLGRDLKNRYQDYPNFNQEFGHLEGDTVQGKNHQGAVTTLVERQTKVAIVLNSHTKSAHDVNRSLAGWLSKLPRHLFKSITFDNGKEFAGWRTIANQFDLNIYFAAVGAPNQRGLNENTNGLLRKDGLRHNLIMDQLSDGFVQAVASRRNHIPRKSLGYQTPLEAFISQITDEQLKNF 9 | >WP_013356274.1 glycine/betaine ABC transporter 10 | MISLKKRILRYLQMATVIICLTAIVSACAKPAEYDSHKKLGPQINYTITGIEAGAGVMANTQTLLSKYKLKQANWQIMPSSTAAMISTLSKAVKNKQPIVVTGWQPHWMVAKYSLKFLKDPKHVYGNGESMRTITRKGLEKDNPGATKLLKNFHWTISMSNPVMLNINGGMNKQKAVNQFIKNHPKQVKAWTAGVPNGHGKKIKLVYTPYDYEIATTTLVTTLLKQKGYKATMQQLDVGVMWNSIANGSSDASVTAELPVTHGLYAKKYKGKYVQLRKNLKGAQTGLAVPKYMKNINTVDDLKNK 11 | >WP_013356275.1 proline/glycine betaine ABC transporter permease 12 | MNIGQIPLADWINSGVDWLSQFTGFFNSVTIFFQVIIDGIQWAFDFLPQWVFILVVLALTYWVKRGQKKISFMVFEVLGLLLIWNLGYWRDMTQTLTLVLSSSLIAIVVGIPLGIWMAKSSRAEIVIKPILDFMQTLPAFVYLIPAVSFFGIGMVPGVLASVIFATPPTVRMTNLGIRQVPSDLIEVADSFGSTSWQKLIKLQLPLAKTTIMSGINQTMMLGLSMVVIASMIGALGLGTQVYFAVGRNDAGAGFAAGIAVVIVAIILDRITQSFNKTSK 13 | >WP_027822877.1 glycine betaine/L-proline ABC transporter ATP-binding protein 14 | MVEKVKVKNVTKIFGKQISLAKKLLREGKSKAEILSQTGCTVGVNQASFAVNEGELFVIMGLSGSGKSTIIRMINRLINSTDGDIEIDEQGVMGLSKEELRHLRQDKIGMVFQNFALFPHKSVLQNAAYGLELKKFPLDVRNRKAHEALTLVGLTGYDDQYPDQLSGGMQQRVGLARALANDAEILLMDEAFSALDPLYRKEMQDLLLQIQEKMHKTIIFIGHDLNEALKLGDRIMIMRDGHIEQIDNPEDILTHPANDYVERFIEGVDRTKVLTASSVMTQAQVVNIGKAGPRVALRRMRANDISSIYVVDNDNKFVGFADAHDVSDLIKKGSEDLKSVLRTDVPKTSVDMPINALINDISKAAIPYVVLDDDDHLLGIILRSSVLAAIAGEEVSA 15 | >WP_072533464.1 IS30 family transposase 16 | MFNDQIKQFEVSGMSYKHLTIKEREILIFLRTKGLSIRAVALRLGRNPSTISRELKRCAGNYSPSKADNDYHQKRQNCHKKRLLDSHPQLRRQIVHYILDLHWSPEQITALFNKEHQWCVSYNTIYRHIYQHNLGEKYSSRGDTGIQRHLRHKHRTRHSKNTRRHREVQTDYISIHERPGFINQRQRIGDWEIDTVIGRTGHSILLTVVDRLSRLTLIKKVVQKDLQEINKGLVELLGAIPKEFVHLDEISERLGVTVYWPDPYSPEQRGTNENTNGLIREYFPKRTDIDNYTEQDVEHCQKQLNQRPRKVLNYETPYEVFFDKPLHLV 17 | >WP_041161898.1 LysM peptidoglycan-binding domain-containing protein 18 | MKIKNLVLSSTAALALFAISTTVANADTYTVKAGDTVSAIAQAHNTSVSAIEKANKLANVNLIFIGDKLEVNGTTTTTTTSAATSAAPQSATSQATSSAASTTSTTSTQTTQQTTTTQSSAQTSQTQAQPSQASQTQSSQTQTSKPAAQTTTQTSSSTSNYSNNGSDSAAKAWIAGKESGGSYSARNGQYIGKYQLSASYLNGDYSAANQERVANSYVASRYGSWSNAKSHWLANGWY 19 | >WP_072533465.1 putative holin-like toxin 20 | MRPWEHGIIFRKVSQSMSVFQALSLMLLFGTFLIALLSYIDKHHK 21 | >WP_016526665.1 ClC family H(+)/Cl(-) exchange transporter 22 | MIQLVKEKFDVTRLRFVLLGLLVGLMSGTVVSLFRYCIEIGLHYSRLVYRYLRIAPFVWWEWALLIGINLGLALIVARLLKREPYIAGSGIPQVEGQLAGELEMHWWSILWRKFIGGILALGPGLFLGREGPSIQLGASVGQGFASGFKLSGTDRRLLIASGAAAGLAAAFNAPIAGTLFVLEEIYHNFSPLVWLTALAGAIGSNFISLNVFGLVPVLHLSYSRSLPVSNYWHLILLGIVLGLFGYLYQRVLLVMPRWYHQLTHLPRPIQGIVPFLLVILVGYFSPNLLGGGNGLIVGFGQYVPPLFVLIAIFIIRFVFSMISYGSGLPGGIFLPILSLGAVIGAVYGVLMNQLGLLSHVYIMNLIIFSMAGYFAGIGKAPFTAILLVTEMVGNLTHLMPLAVISLTAYLVVDLLGGAPIYEALLKQMTMPKTVQQLHRPDHLEIPVFFGSPLNGKMVRDMPWPKEALLIGIRRGEQEVIPHGDTLIHEGDTLVLLTDTTQRPQVKQRIDALLATLEKKHQD 23 | >WP_013356282.1 cation:proton antiporter 24 | MEFLGTLVIILIATSLFGHLASRVGIPAVIGQLFVGIILGPALLNWVHANDFIHIFSEIGVIILMFIAGLESDLTLLKKYLRPSIIVALLGVLIPMVLIYPVGIVFGLTQFESLFLSVIFAATSVSISVAVMKELNLLDSKSGSTVLGAAVVDDVLAVVLLSIMVSLIGTKAGTDTQIPLALTFLEQLIYFAAIYFVMRFIAPLLARLGTKLLNPVGPTIMAMILCFGMAYIANLIGLSAVIGAFFAGIAIAQTHVAHEVDQSIEPIGYAIFIPVFFVSIGLSMSLAGIEHDLLLIIVLTIVATLTKLLGAGSGAKWAGFSSNEAYLVGAGMVSRGEMALIIAQIGYQSKLISDDYYSAIITAIILTTLLAPLLLKHAARHINY 25 | >WP_095587199.1 IS5-like element ISLpl3 family transposase 26 | MTTPKRYELEDAQWDRIKGYFPPYRTGRPSSLDNRTALNAILWLMRSGAPWRDLPERYGSWKTVYSRFRAWVSSGLFEQVFLELIDDPDMENLSLDSTIVRAHQKATGGKKNAECMVENQAIGLSRGGRTTKIHALVDGLGNPLGFRLTGGQVHDSQVASELLEGFDISQSNIIEDKAYGTAKLRQYIEDKAGVYTIPPKENTKDKWTCDYHVYCERHLIENFFNQLKNFRRIATRYDKLAHVYLATVYIASICILLK 27 | >WP_159037601.1 IS3 family transposase 28 | MIYYTTAQEGDIRVHRNVTDWGIRDNFLTGRVQYWYNQHHQGIGSTKILSNLRKDPAVTEQVTLKQVKRIMSKLGIRCQVRQKKHSRIKQQEQYLQDNVFKSAF 29 | >WP_159037602.1 DDE-type integrase/transposase/recombinase 30 | MFLNQRFSVNQPNQVWLADSTELTYGVNGQYKVRLSGALDLYGRHLIAHKLSETETSKAKVQVFQRAFNFAGDVHPVVHTDRGSACTSGTFNNFLAQHEVTRSMSRPGMPYDNAPMERWWNEFKLRWIDRHAKPVTYKELVALVEEGINYFNQLDCSPARNDLTPAEYWNEAV 31 | >WP_080235520.1 class Ib ribonucleoside-diphosphate reductase assembly flavoprotein NrdI 32 | MLYISLSGNTKYFISRLTTYFEKERHVRVSSINVKEHPEFTTLDQPFVTFLPAFLKGGNGVNDGYTEILTTILGDYLAYEGNYQHCYGIIGSGNRNFNKQFALTAKQYAKRFDFPYITDFELRGTAHDIPRIADAILTYRNQFCFQTTKE 33 | >WP_021353390.1 putative holin-like toxin 34 | MSVFQTLSLMLLFGTFLIALLSYIDKHHK 35 | >WP_013356291.1 recombinase family protein 36 | MKYGYARVSTTDQKLANQIELLKLAGAEKIFQEKFTGTTTERPEFQKLLRVLKTGDTLIVTKLDRFARNTREALAIIQELFKENVKVNILNMGLIDNTPTGQLVFTIFSAFAQFERDMIVTRTQEGKLYAKQHDPLFREGRPKTYSDEQIRFAYELRQQGMTYKMIERKTGISKRTQQRRFKLI 37 | >WP_072533469.1 MarR family transcriptional regulator 38 | MVNHQQIKNQFESLTTLQAIQKQAYQMLVQGLAKTEFSMREWGILVYLEQHGQATASELADAFMVTRTLISRNTWQLIQDNLIQSQVNPNDRRIVRLILTVNGQERVQKVFRQVQANLKTFDQSHNLEKLAKQVETLSQQLAKIN 39 | >WP_072533470.1 sensor histidine kinase KdpD 40 | MNQNERASRYLKDANLETSGRGKLKIFFGYAAGVGKTYSMLAEAQDLKQEGQDVIIGYLEDHGRADTRNLARGLESIPVKKESYKGMYLQEFNLDGALERHPQTILVDELAHTNTKKSRHLKRYQDIQELLTAGINVYTTLNVQHLESLHDIIFEQLNVDIQERVPDYIFDMANQVKLIDIEPNDLINRLKIGKIYARNHIRQALANFFKSENLTTLRELALRKMTLRLATNSKVGTDRLLVCLSGSKSNERVIRSAAQMAQAFNSDFIAVYVKNNQFNKWEENLNNNISLAHQLGAKVIKLQGENPALQITEYAKESSVTKIVLGASPYKKIWQTRSTLVYQIGYLLPEVDEYVINSAKTNLNQVQHLNGTFLTHFKGSLSKSGSKRMAKNMLMISSIILGSTVVGELLIFAQVPLVNVVLVYMLGIMLCAINLKDQVYGFLSAFLAVISFNLFFTKPYFSLSSSPVYLLTFFLMLLVSIISNYWTLKLKHQVQYNSNRVYQTEVLLETIRKLQQAKLSTDIIDTTVQQLQKIYSATMVFYECQDSKLLQPKIYLLNQSQSSFYEFSSDNEKAIATWAFKNGQEAGLTTNTLPQAKCLYLPIFGNSPSSVVAVIGLAINSKRSLEIFDRNLIISILDECGQALSRMRILNEKRKAEIKSHQEKLRANLLRGISHDLRTPLTNISGSADILKREGNKLSDVEKYTLYESIFNDSSWLISLVENLLAMTRLESSLQVRHSPELVDDIIQESLQHLSPGFEDHKITVNLSDPLLMVMADAHLITQVIINIVNNAIQHTPEKTKISIRIFQKNTNQVQFEISNNGPKLTNGDLAHLFGLFYTGKQKQPLRAQRGLGIGLSLCKSIIEAYDGNIWAKNLNNGVCFYFTLPIWSKKNEK 41 | >WP_033098912.1 potassium-transporting ATPase subunit KdpC 42 | MVVKTLGKSILAVIVITIICGIYTLIVSAIGQTFFTSQANGSLVRSDQTVRGSALIAQPFTSKKYLWGRQMDITVGDITGQKNNKNIMYSVASQMSPNDKSYQKEIIDKEKTIKRLNPNATYRHVPIDLVTNSGSGLDPEISPTAAKYQVKRIAKARHVSVQKIQRVIDQNTKTRTLGILGEPRVNVLETNLALDKLSK 43 | >WP_072533471.1 potassium-transporting ATPase subunit KdpB 44 | MNEKIEKNQMFSRKMLGAALKETFLKLKPNLQIKNPVMFLVYVSAIATTILFILSLFGISDRIVSSGFIFTVAVILWITCLFASFSEAVAEGRGKAQAAELKKSKRAVMAKRLNKVGDLSNIEKVSGENLNKGDLFLAEAGETIAADGEVVEGAASIDESAITGESAPVIREAGGDRSAVTGGTTVLSDHLVIKVTQEQGASFLDKMIGMVEGANRKKTPNEIALEIFLVALSIIFVLVVAALYTYSQLSSQILDMKNPMSIVWLIALLVCLAPTTIGALLSAIGIAGMSRLNQANVLAMSGRAIEAAGDTDVLLLDKTGTITLGNRRASAFIPVDGHSEKELAIAAQLSSLADETPEGRSIVVLAKEKYSLREQNLEANQAKFIPFSAQTKMSGVNFAGDEIRKGASENIKQYVLTKKNKYSEECQIAVKRIAEQGGTPLVVCKNGVILGVIYLKDIIKPGVKEKFADLRKMGIKTIMITGDNPVTAAAIAAEAGVDSFLSQATPESKMKTIREYQNKGHLVAMTGDGTNDAPALAQADVAVAMNTGTQAAKEAGNMVDLDSSPTKLIKIVQIGKQLLMTRGSLTTFSIANDIAKYFAIIPAMFVGLYPKLNVLNIMHLYSPTSAILSALIFNALIIVALIPLALKGVRYREVPASQLLRHNLLVYGLGGIVVPFIGIKLIDLIIALILQIL 45 | >WP_072533472.1 potassium-transporting ATPase subunit KdpA 46 | MPTWAQYLPYIIVPLLLAIPLGKYISHIINDQNNFMSRIIRPIENKIYKFFRVSNREMSWKTYLISILMFSLVSFLVLFSILRFQNYLPGNPEHFSGLSWSLAFNTAVSFVTNTNWQSYSGEMALSNFSQTIGLTVQNFSSAAVGIAVLFALIRGIQRTEAKAIGNFWRDLTRIFLYLLIPLSLFMSIMLMAGGVPQTTGGYKTATLVQPVAVDKHNQAIYGAEINVKKNTVRIKNHKIDGAKIITKETLPLFPQASQVSIKLLGTNGGGVLGANSAHPYENPTPFTNFIETTAMLLLPMALCFSFGESLKRRKEGWAIFSAMAIMFVVALIFEGIVEQHGTTLAHIGNLAMEGKETRIGIPESAVWSIVTTATSTGATNATLDSFSSLGGLMPMALMQLGEVVFGGVGSGLYGMLGFIILTVFIAGLMVGRTPEYLGKKIGPYEMRMAVIACLSTPIAILTCSAIPAMLKSTLTSLSNSGAHGFSEFLYAFSSAGANNGSSFGGFNGNTIIFNVLLAVAMIISRFLPIIAIMAIAGKMGQHKIVASSSGTLSTTSPTFVFMLIIVVLIIGVLSFFPALSLGPIADFLTH 47 | >WP_072533473.1 hypothetical protein 48 | MIDNYYYKDYSDQYSREKIICLIDNDIKKKMGGYIKSTSGDLFIADVTIFYFFDTAQHLKFDYGFSDKVPISGRKFWEQRINVLEHP 49 | >WP_046783626.1 nucleoside hydrolase 50 | MRNVYFNHDGSVDDLVSLLLLLQMSDVHLTGVGVVGADSYLEPALAATRKVIDRFGHDTHLEVAASDSRGVHPFPKEWRLDAFSLDALPILNESGTIVTPVAPKPAHLDLVDKLQATSEKTTLVMTGPLTDLARALQADPSITAKIEQLYWMGGTFDGRGNVAEPEQDGTTEWNAYWDPQAVKTVWDSDLTIQMVGLESTRQVPLTPAIRQHWATLRQHPAIDFIGQGYALVPALQHFETNSTYFLWDVLTTVASEFPEIVTTKRVTSDVLTEGPGRGRTFETPTGRPVTLVTTVDHDAFFKRIDTLALHADK 51 | >WP_072557541.1 pyruvate oxidase 52 | MAQKASEQLVDVLIDWHVKHVYGLPGDSIDTTVDALRKQQDQIGFVQVRHEEVASLAAAAEAKLTGKLGVCLSIGGPGAIHMLNGLYDAKMDHVPVLALLGQVDTQFLNEEYFQEVNTPQLFADVAVYNKLISATDNLAEIVDEAIRTAYEKHGVAVLTIPDNLPEQRVSDHYISSASPFQLTSPQIDDNRLNTAAKLIRDSKKPLALVGLGAKKAGSQVAAFLEQNHIPFISTLPAKGIIGDNHPNSLGNVGKLGTKPAYEAMQNTDLLLMFGTNYPYTAYLPKPGTAKSIQINTSASAIGKRYPADVGLVADIKDVIKQLNSSETTILHPDDGFLKSCQENVANWNSWMTGKRELDSKPVAPEALFNNINETAPLNTIYSIDVGTATSWSARFLNVSPTQKFILSSYLGTMGCALPGAIAAKINYPDRPVISVNGDGAFAMVMQDFITAVKYKLPLINIVLNNSKLAFIEYEQQAAGQLNYQIDLQDMDYAKFADAAGGVGITAKTSEEFKNALNRAYRVQNMPVLINAYVSDDAPLPGKIVGQEAKGYMKFGSQYLKEEKKIPELPPLKDILRQFF 53 | >WP_060459632.1 biotin transporter BioY 54 | MKRSNTTRLLTLSAMMVAILIILGLFPGIPLGFIPVPIVLQNMGVMMSGELLGPRYGTISVGLFLLLAFLGMPILSGGNGGMAVFIGPTGGYLIAWLFTPLLIGLLINRFQIYNKSWIFELLILLLAGVIFIDLVGAIWLSYQSHITLIAALISNLAFIPGDLIKSALAVIIVRRIRKAMHLSLF 55 | >WP_060459631.1 hypothetical protein 56 | MEERLLILISLKTGDLLLVKKAQNKLSEMIIRGTKQTFLDRPLAYYHIGIVEKEGTDFFVLHATKDHGCIRQPLDQFISEEGLIDVYRKSSPLINSLKILKRAKSMLEAPYNPSFRLDQPGYYCSDYVVNAFKDENIFHLSPMVFGPNGSVLPEWQQYYENLDLPVPNGQLGSSPNSLISQGHLKFIGAINS 57 | >WP_072533475.1 MobA/MobL family protein 58 | MAIFHMSFSNISAGKGRSAIASAAYRSGEKLFDDKEGRHYFYARSVMPESFILTPKNAPAWASNREQLWNEVEKKDRKSNSRYAKEFNVALPIELSEDEQKTLLTKYVQENFVDQGMVADVAIHRDHPDNPHAHVMLTNRPFNPDGTWGQKTKTKYILDSHGNKTKTPAGNVRNRKIWLVDWDKKEKITEWRHNWAASVNQALEQKSIPDRISEKSFVEQGIADTPMQHEGINSKRHERKAFNQQVKNYRKSQAGYKNMQEKVVNQGHLDSLSKHFSFNEKKVVKELSHELKTYISLESLDDKRRMLFNWKNSTLIKHAVGEDVTKQLLTINQQESSLKKADELLNKVVDRTTKKLYPELDFEQTTAAERRELIKETNSEQTIFKGSELNERLMNIRDDLLVRQLLTFTKRPYVGWKLLMQQEKEVKIELKYTLMIHDDSLESLEHVDQGLLEKYSPTEQQKITRAVKDLRTIMAVKQVIQTQYQEVLRRAFPNGNFNELPMIKQEQAYTAVMYYDPVLKPCQAETIEQWQANPPQVFSPQEHQQGLAYLSGQLSLDQLENHHLQRVLKHDGTKQLFLGECKADPTIKNSQIEKIQKQLKGQQAKDDQYRKVNIGHYQPLNYKPVSPSYYLKTAFSNAIMTALYARDEDYERQKQAQGLKETEWEMTKKQRQHQTRNRHEDGGMHL 59 | >WP_002819869.1 hypothetical protein 60 | MSQSNLEKQEAKLKALNQKIKDEKNKIEQRLGKQIISQANLDYANLSNDQIKLLAKQFSEFLKVKSVDH 61 | >WP_013356307.1 hypothetical protein 62 | MSNQYEKLVEQQARLKQKIEREDFKLRQSKYYENRQARKARSRRLIQKGALLEKYFQANNLSVEQTEELLKTFADYVNTHKPDKLKNDQPNN 63 | >WP_013356308.1 hypothetical protein 64 | MKKPSWVDLGNIYRFPKAGLNFKEIGHLSKLDQNKISDFTLALKSKRRGKDQKIIQQRSSKRKHKENKAELTQRIQKQLKDFAKNNPEIKPKNNSENKNDRPSY 65 | >WP_010014534.1 type II toxin-antitoxin system RelB/DinJ family antitoxin 66 | MAVKEKKRVQVKIDKDLADDTEAVLSELGLNPTTAINMFYKRIVANGALPFNASLSEEEKANLRFLKATEGTPVTEFKDAKEVADWLNDPDED 67 | >WP_072533476.1 hypothetical protein 68 | MENRNPSSQIPKKKRKLVELLECKDYIVPLNPYVIVKKTIYRSSARITNQQRDQVKRVLRKQGYQGRISSIRYTRVNYKYSFKVSVKYQGSVGKFLVNTKNETVEFKGMV 69 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # COPLA, a taxonomic classifier of plasmids 2 | 3 | ## How to install COPLA 4 | 5 | ### Dependencies 6 | 7 | COPLA is a pipeline of scripts programmed with ```bash```, ```python```, ```perl```, and ```ruby```. In addition to the respective runtimes, COPLA requires several third-party programs and Python libraries to function. The following list details the specific versions used during the development of COPLA: 8 | 9 | - BLAST+ 2.9.0 10 | - Prodigal v2.6.3 11 | - HMMER v3.3 12 | - PlasmidFinder 2.1 13 | - MacSyFinder 1.0.5 14 | - graph-tool 2.33 15 | - GNU Parallel 20161222 16 | - ani.rb (downloaded on Mar 16, 2017 from https://github.com/lmrodriguezr/enveomics) 17 | 18 | ### Installation 19 | 20 | First we need to download the scripts and databases used by COPLA from GitHub: 21 | 22 | ~~~bash 23 | # Change PROJECT_ROOT_DIRECTORY value to suit your preferences 24 | PROJECT_ROOT_DIRECTORY=copla 25 | git clone https://github.com/santirdnd/COPLA ${PROJECT_ROOT_DIRECTORY} 26 | cd ${PROJECT_ROOT_DIRECTORY} 27 | ~~~ 28 | 29 | A conda environment has been provided as a convenient way to install most of the COPLA dependencies. Just run the following command: 30 | 31 | ~~~bash 32 | conda env create -f copla.environment.yml -n copla 33 | ~~~ 34 | 35 | Alternatively, run this other command if you want to use the exact environment used during COPLA development: 36 | 37 | ~~~bash 38 | conda create --file copla.spec-file.txt -n copla 39 | ~~~ 40 | 41 | MacSyFinder, the program used for MPF typing, is a bit trickier to install as it is a Python2 program and multiple versions of Python can't be mixed in the same conda environment. A specific conda environment is used to facilitate its installation. Run the following commands to install MacSyFinder: 42 | 43 | ~~~bash 44 | conda env create -f macsyfinder.environment.yml -n macsyfinder 45 | conda activate macsyfinder 46 | cd ${CONDA_PREFIX} 47 | wget https://github.com/gem-pasteur/macsyfinder/archive/macsyfinder-1.0.5.tar.gz 48 | tar zxf macsyfinder-1.0.5.tar.gz 49 | wget https://github.com/gem-pasteur/macsyview/archive/macsyview-1.0.1.tar.gz 50 | tar zxf macsyview-1.0.1.tar.gz 51 | mv macsyfinder-macsyfinder-1.0.5 src 52 | mv macsyview-macsyview-1.0.1 src/macsyview 53 | cd src 54 | python setup.py build 55 | python setup.py test -vv 56 | python setup.py install --prefix=${CONDA_PREFIX} 57 | conda deactivate 58 | cd ${PROJECT_ROOT_DIRECTORY} 59 | ~~~ 60 | 61 | Finally, we need to download the software for ANI calculation. We will use ```ani.rb```, a Ruby script provided in the Enveomics Collection: 62 | 63 | ~~~bash 64 | wget https://github.com/lmrodriguezr/enveomics/raw/master/Scripts/ani.rb 65 | chmod +x ani.rb 66 | mv ani.rb bin/ 67 | ~~~ 68 | 69 | Once we have all the necessary software, we need to download the databases that COPLA uses. For this reason, the ```download_Copla_databases.sh``` script has been provided, which additionally generates the ```CoplaDB.fofn``` file in the ```databases/Copla_RS84``` directory. At the end of the command execution, this file should contain a list with the absolute paths to the Fasta sequences that make up the database. Just be sure to adapt the contents of the ```copla.ini``` configuration file to your local installation so that ```CoplaDB.fofn``` is generated automatically: 70 | 71 | ~~~bash 72 | bin/download_Copla_databases.sh 73 | head databases/Copla_RS84/CoplaDB.fofn 74 | ~~~ 75 | 76 | A last script, ```post_install_test.sh```, has been developed to verify the correct installation and operation of COPLA. If warning or error messages appear for any of the tests performed, it will be an indication that parts of the COPLA installation should be checked. Anyway, a success message in the last two tests would indicate that COPLA may well be installed correctly. 77 | 78 | ~~~bash 79 | bin/post_install_test.sh 80 | ~~~ 81 | 82 | Other relevant infomation: 83 | 84 | - The MOBscan relaxase database was downloaded from [https://castillo.dicom.unican.es/mobscan_about](https://castillo.dicom.unican.es/mobscan_about) 85 | - CONJScan, the database for MPF typing used by MacSyFinder, was downloaded on 2019-05-30 from [https://github.com/gem-pasteur/Macsyfinder_models](https://github.com/gem-pasteur/Macsyfinder_models) and updated using the relaxase HMM profiles from MOBscan 86 | - The PlasmidFinder replicon database was downloaded on 2019-07-31 from [https://bitbucket.org/genomicepidemiology/plasmidfinder_db.git](https://bitbucket.org/genomicepidemiology/plasmidfinder_db.git) 87 | - The Comprehensive Antibiotic Resistance Database (CARD) database was downloaded on 2020-10-15 from [https://card.mcmaster.ca/download](https://card.mcmaster.ca/download) 88 | 89 | ## Using COPLA 90 | 91 | COPLA can be used to predict the PTU to which a plasmid belongs. For this just its nucleotide sequence is needed: 92 | 93 | ~~~bash 94 | bin/copla.py test/NZ_CP028167.1.fna \ 95 | databases/Copla_RS84/RS84f_sHSBM.pickle \ 96 | databases/Copla_RS84/CoplaDB.fofn \ 97 | test/NZ_CP028167.1_output 98 | ~~~ 99 | 100 | Draft plasmids are supported by COPLA automatically executing a simple concatenation of their contigs. 101 | 102 | If the plasmid ORFeome is uploaded by the user, it will be used by COPLA (option `-a`) to obtain a type MOB and MPF according to the user specifications. 103 | 104 | Indicating the query topology (option `-t`, linear or circular) is recommended for more accurate MPF typing. If not provided, COPLA uses a circular topology by default. Multifasta input sequences are always processed using a linear topology. For further details see Abby _et al._ [PMID: 26979785](https://pubmed.ncbi.nlm.nih.gov/26979785/). 105 | 106 | Finally, if the user provides the taxonomic data asociated with the plasmid host (options `-k`, `-p`, `-c`, `-o`, `-f`, `-g`, and `-s`), COPLA will warn the user about changes in the host range of the query's assigned PTU. 107 | 108 | The following command shows a complete example of the use of COPLA: 109 | 110 | ~~~bash 111 | bin/copla.py test/NZ_CP028329.1.fna \ 112 | databases/Copla_RS84/RS84f_sHSBM.pickle \ 113 | databases/Copla_RS84/CoplaDB.fofn \ 114 | test/NZ_CP028329.1.fna_output \ 115 | -a test/NZ_CP028329.1.faa \ 116 | -t circular \ 117 | -k Bacteria \ 118 | -p Firmicutes \ 119 | -c Bacilli \ 120 | -o Lactobacillales \ 121 | -f Lactobacillaceae \ 122 | -g Lactobacillus \ 123 | -s 'Lactobacillus sp. D1501' 124 | ~~~ 125 | 126 | ## How COPLA works 127 | 128 | Just as sHSBM, COPLA infers the PTU membership from the similarity relationships between plasmids in the database. After calculating the ANI percentage identity between the query and the reference plasmid set, COPLA inserts the query into the reference network and performs an statistical search for similar plasmids. Finally, the query is assigned to a known PTU, or to a new PTU (labeled as PTU-?) if the algorithm find clues pointing to that outcome. PTUs will not be named for clusters with fewer than 4 members. For the user to evaluate the COPLA PTU assignation of a query plasmid, a score is provided based on the overlap of the graph partitions before and after the query was inserted into the reference network (for additional details see troubleshooting bellow). 129 | 130 | ## COPLA output 131 | 132 | COPLA output consists of five different files: 133 | 134 | - __query.ptu_prediction.tsv__: File providing the predicted PTU of the query, the graded PTU host range (updated by the taxonomic info of the query if provided by the user), the prediction score, and additional information (see troubleshooting) to help interpret the output. 135 | - __query.related_plasmids.tsv__: List of reference plasmids used for the calculation of the prediction score. 136 | - __query.ani.tsv__: List of ANI percentage identities between the query and reference plasmids. Only plasmids with a detected homology spanning >50% of the shorter genome are reported (Best reciprocal BLASTn hits of 1,000 bp genome fragments with >70% idendity over >70% length. Fragments are obtained using a sliding window algorithm with 200 bp steps). File columns indicate query, reference accession number, ANI, standard deviation of ANI, fragments used for the calculation of ANI and fragments of the reference genome. 137 | - __query.qry_info.tsv__: Other relevant plasmid information: genome length, MOB typing (according to MOBscan), MPF typing (according to CONJScan but with the relaxase HMM profiles updated to those of MOBscan database), replicon typing (based on PlasmidFinder using 80% identity threshold) and AMR genes (based on a BLASTn search against the CARD database) 138 | - __query.faa__: If not provided by the user, this file is the plasmid ORFeome calculated by Prodigal. Prodigal autolearning mode is used for plasmids >100 Kb, smaller plasmids are calculated using Prodigal meta mode. 139 | 140 | ### Troubleshooting: Predicted outcome types 141 | 142 | COPLA predictions can result in three different outcomes for PTU membership of the query plasmid. A query could be a member of: (i) a known PTU, (ii) a putative new PTU (provisionally named as "PTU-?"), or (iii) the plasmid remains unclassified (displayed as "-"). The quality of these predictions can be further evaluated with the help of the score output. To assess COPLA performance, we sampled 1,000 RefSeq200 plasmids not present in the reference database (RefSeq84). As shown in Figure 1, results indicate that ~88% of all queries (spanning all three classes) the prediction score a >99%, with the remaining predictions scoring lower values as shown in the figure. 143 | 144 | ![Score distribution](Results.png) 145 | 146 | ___Figure 1.___ _Score distribution for 1,000 plasmids sampled from RefSeq200, not present in the COPLA reference database (RefSeq84). The figure displays a semilogarithmic plot of the number of plasmids containing each given score._ 147 | 148 | To validate the PTU assignment, a score of 90% is recommended. This threshold means that, for a 10-member PTU, the AI has conficting data for clustering 1 of them. As an example, the problematic plasmid could be a cointegrate of plasmids belonging to two different PTUs. Assuming the 90% score threshold, COPLA confidently assigned 93% of the 1,000 samples. 149 | 150 | To help understand the different results that COPLA provides, Figure 2 shows several representative schematics. 151 | 152 | ![Representative prediction outcomes](Outcomes.png) 153 | 154 | ___Figure 2.___ _Representative prediction outcomes. The query plasmid is represented by the node with the red inner circle. For all other nodes, the color of the inner circle represents the PTU assigned in the reference database (i.e. using only RefSeq84 plasmids). The outer rings represent the PTU assigned by COPLA. Yellow represent the PTU assigned to the query, green is a different PTU, and grey colors represent not assigned PTUs._ 155 | 156 | When the query is part of a small graph component (4 members or less) the PTU prediction is pretty much trivial (cases 1 to 4): 157 | 158 | - Case 1 shows a query represented by a singleton, with no ANI relatioships to any reference plasmids, a not uncommon event (355 out of 1,000 samples). In this case, COPLA output includes the sentence: _PTU could not be assigned. Query is part of a graph component of size 1_. 159 | 160 | - Case 2 represents a putative PTU, consisting on a set of plasmids unrelated to all other plasmids in the dataset. It is left unnamed as it does not reach the 4-member threshold. In this case (70 out of 1,000 samples) COPLA output includes the sentence: _PTU could not be assigned. Query is part of a graph component of size 2|3_. 161 | 162 | - Cases 3 and 4 represent the finding of a new (putative) PTU, by clustering the query with 3 reference plasmids. These reference plasmids could all belong to the same cluster (case 3) or not (with the query acting as an attractor of different plasmids as shown by case 4). These cases (17 out of 1,000 samples) are highlighted in COPLA output by including the sentence: _New (putative) PTU. Query is part of a graph component of size 4_. 163 | 164 | In cases 5 to 7 the query is part of a larger graph component (>4 members), that is, a known PTU. In these cases, the assignation of the query is straightforward. These cases represent the predominant scenarios in which no significant differences in graph partitioning were detected. 165 | 166 | - In case 5 we have (i) queries for which a PTU can be unequivocally assigned and score is 100% (308 out of 1,000 samples), and (ii) cases in which the score is fewer than 100% (58 out of 1,000 samples with score >=90% and 46 samples with score <90%). COPLA output includes the sentence: _Query is a PTU-xxx plasmid_. 167 | - Case 6 shows a scenario in which no PTU was assigned because of the 4-member threshold. This case (113 out of 1,000 samples) is highlighted in COPLA output by including the sentence: _PTU could not be assigned. Query is part of a sHSBM cluster of size 1|2|3_. 168 | - The finding of a new (putative) PTU is shown in case 7. In this case (8 out of 1,000 samples) COPLA output includes the sentence: _New (putative) PTU. Query is part of a sHSBM cluster of size >=4_. 169 | 170 | Cases 8 and 9 represent scenarios where the inclusion of the query leads to a different partition in the connected component to which the query belongs. Case 8 highlights the case of plasmids (such as cointegrate plasmids) for which the algorithm can not decide unambigously to which PTU should assign the query. Case 9 showcases situations of loosely defined PTUs (showing low intragroup density, such as PTU-FE see ref. [1]). The score provided by COPLA is key to detect and to assess the level of confidence of the PTU prediction. 171 | 172 | - If the new cluster had fewer than 4 members no PTU is assigned. This case (4 out of 1,000 samples with a score >=90% and 5 with a score <90%) is highlighted in COPLA output by including the sentence: _PTU could not be assigned. Query is part of a sHSBM cluster of size 1|2|3_. 173 | - If the cluster could not be assimilated to a previously known PTU, a new (putative) PTU is defined. In this case (16 out of 1,000 samples, all with score lower than 90%) COPLA output includes the sentence: _New (putative) PTU. Query is part of a sHSBM cluster of size >=4_. 174 | 175 | ## References 176 | 177 | If you find COPLA useful, please cite the following papers: 178 | 179 | - PTU concept and graded plasmid host range definition: 180 | 181 | [1] Redondo-Salvo, S., Fernández-López, R., Ruiz, R., Vielva, L., de Toro, M., Rocha, E.P.C., Garcillán-Barcia, M.P., de la Cruz, F. “Pathways for Horizontal Gene Transfer in Bacteria Revealed by a Global Map of Their Plasmids.” *Nat Commun* **11**, 3602 (2020). https://doi.org/10.1038/s41467-020-17278-2 182 | 183 | - Description of the COPLA algorithm: 184 | 185 | [2] Redondo-Salvo, S., Bartomeus, R., Vielva, L., Tagg, K.A., Webb, H.E., Fernández-López, R., de la Cruz, F. “COPLA, a taxonomic classifier of plasmids.” (2020). 186 | -------------------------------------------------------------------------------- /copla.spec-file.txt: -------------------------------------------------------------------------------- 1 | # This file may be used to create an environment 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https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-hdfca744_2.tar.bz2 179 | https://conda.anaconda.org/bioconda/noarch/cgecore-1.5.6-pyh3252c3a_0.tar.bz2 180 | https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.50.2-h3442318_1.tar.bz2 181 | https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.3-py39h98787fa_0.tar.bz2 182 | https://conda.anaconda.org/bioconda/linux-64/perl-http-cookies-6.04-pl526_0.tar.bz2 183 | https://conda.anaconda.org/bioconda/linux-64/perl-http-daemon-6.01-pl526_1.tar.bz2 184 | https://conda.anaconda.org/bioconda/linux-64/perl-http-negotiate-6.01-pl526_3.tar.bz2 185 | https://conda.anaconda.org/conda-forge/noarch/pip-20.3.1-pyhd8ed1ab_0.tar.bz2 186 | https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.24-h8300230_0.tar.bz2 187 | https://conda.anaconda.org/bioconda/noarch/perl-libwww-perl-6.39-pl526_0.tar.bz2 188 | https://conda.anaconda.org/conda-forge/linux-64/graph-tool-2.35-py39h946176e_1.tar.bz2 189 | https://conda.anaconda.org/bioconda/linux-64/perl-lwp-protocol-https-6.07-pl526_4.tar.bz2 190 | https://conda.anaconda.org/bioconda/linux-64/entrez-direct-13.9-pl526h375a9b1_0.tar.bz2 191 | https://conda.anaconda.org/bioconda/linux-64/blast-2.10.1-pl526he19e7b1_3.tar.bz2 192 | https://conda.anaconda.org/bioconda/noarch/plasmidfinder-2.1.1-0.tar.bz2 193 | -------------------------------------------------------------------------------- /bin/sHSBM.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import os 4 | import sys 5 | import pickle 6 | import pathlib 7 | import argparse 8 | import numpy as np 9 | import pandas as pd 10 | import graph_tool.all as gt 11 | import matplotlib.pyplot as plt 12 | 13 | parser = argparse.ArgumentParser( 14 | description='Cluster a set of plasmids into PTUs') 15 | parser.add_argument('outdir', type=str, 16 | help='output directory') 17 | parser.add_argument('-a', '--adjacency', type=str, 18 | default='databases/Copla_RS84/RS84f_adjacency_matrix_ANIp50.tsv.gz', 19 | help='adjacency matrix') 20 | parser.add_argument('-m', '--metadata', type=str, 21 | default='databases/Copla_RS84/RS84f_plasmid_metadata.tsv', 22 | help='plasmid metadata') 23 | parser.add_argument('-j', '--hierarchical', action='store_true', 24 | help='Use hierarchical SBM variant') 25 | parser.add_argument('-d', '--deg_corr', action='store_true', 26 | help='Use degree corrected SBM variant') 27 | parser.add_argument('-w', '--weight_model', type=str, 28 | choices=['None', 'Exponential', 'Normal', 'LogNormal'], default='None', 29 | help='Edge weight distribution model') 30 | parser.add_argument('-n', '--nToss', type=int, 31 | default=100, 32 | help='number of initial SBM algorithm initializations') 33 | parser.add_argument('-l', '--maxLevels', type=int, 34 | default=15, 35 | help='maximum number of levels for HSBM') 36 | parser.add_argument('-t', '--transient', type=int, 37 | default=2000, 38 | help='number of iterations to skip transitory phase') 39 | parser.add_argument('-i', '--iters', type=int, 40 | default=10000, 41 | help='number of iterations to store posterior statistics') 42 | parser.add_argument('-f', '--filter', action='store_true', 43 | help='Filter out connected components with less than 4 members') 44 | parser.add_argument('-r', '--refLabel', type=str, 45 | choices=['PTU_Curated', 'PTU_sHSBM'], default='PTU_sHSBM', 46 | help='column name for using as reference of PTU labeling') 47 | parser.add_argument('--version', action='version', version='%(prog)s 1.0') 48 | args = parser.parse_args() 49 | 50 | pathlib.Path(args.outdir).mkdir(parents=True, exist_ok=True) 51 | 52 | # Load network data 53 | nodes = pd.read_csv(args.metadata, sep="\t") 54 | ani = pd.read_csv(args.adjacency, compression='gzip', sep="\t", header=None, names=nodes.AccessionVersion) 55 | nVertices = len(list(nodes.AccessionVersion)) 56 | ani_np = ani.to_numpy() # Complete ANI matrix (symetric, with autoloops) 57 | ani_np = np.triu(ani.to_numpy(), 0) # Upper triangular ANI matrix (with autoloops) 58 | ani_np_idx = ani_np.nonzero() 59 | edge_list = np.transpose(ani_np_idx) 60 | 61 | # Create graph object 62 | g = gt.Graph(directed=False) 63 | g.add_vertex(nVertices) 64 | g.add_edge_list(edge_list) 65 | 66 | # Use AccessionVersion to name the vertices 67 | v_AccVer = g.new_vertex_property('string', nodes.AccessionVersion.to_list()) 68 | g.vertex_properties['AccessionVersion'] = v_AccVer 69 | 70 | # Add other relevant properties to vertices 71 | v_MOB = g.new_vertex_property('string', nodes.MOB_60.to_list()) 72 | g.vertex_properties['MOB'] = v_MOB 73 | v_MPF = g.new_vertex_property('string', nodes.MPF.to_list()) 74 | g.vertex_properties['MPF'] = v_MPF 75 | v_PFinder = g.new_vertex_property('string', nodes.PFinder_80.to_list()) 76 | g.vertex_properties['PFinder'] = v_PFinder 77 | v_AMR = g.new_vertex_property('string', nodes.CARD_80.to_list()) 78 | g.vertex_properties['AMR'] = v_AMR 79 | v_Size = g.new_vertex_property('int', nodes.Size.to_list()) 80 | g.vertex_properties['Size'] = v_Size 81 | v_Topology = g.new_vertex_property('string', nodes.Topology.to_list()) 82 | g.vertex_properties['Topology'] = v_Topology 83 | v_TaxKingdom = g.new_vertex_property('string', nodes.TaxSuperkingdom.to_list()) 84 | g.vertex_properties['TaxKingdom'] = v_TaxKingdom 85 | v_TaxPhylum = g.new_vertex_property('string', nodes.TaxPhylum.to_list()) 86 | g.vertex_properties['TaxPhylum'] = v_TaxPhylum 87 | v_TaxClass = g.new_vertex_property('string', nodes.TaxClass.to_list()) 88 | g.vertex_properties['TaxClass'] = v_TaxClass 89 | v_TaxOrder = g.new_vertex_property('string', nodes.TaxOrder.to_list()) 90 | g.vertex_properties['TaxOrder'] = v_TaxOrder 91 | v_TaxFamily = g.new_vertex_property('string', nodes.TaxFamily.to_list()) 92 | g.vertex_properties['TaxFamily'] = v_TaxFamily 93 | v_TaxGenus = g.new_vertex_property('string', nodes.TaxGenus.to_list()) 94 | g.vertex_properties['TaxGenus'] = v_TaxGenus 95 | v_TaxSpecies = g.new_vertex_property('string', nodes.TaxSpecies.to_list()) 96 | g.vertex_properties['TaxSpecies'] = v_TaxSpecies 97 | if args.refLabel == 'PTU_Curated': 98 | v_PtuRef = g.new_vertex_property('string', nodes.PTU_Curated.to_list()) 99 | else: 100 | v_PtuRef = g.new_vertex_property('string', nodes.PTU_sHSBM.to_list()) 101 | g.vertex_properties['PtuRef'] = v_PtuRef 102 | 103 | # Add edge weights 104 | e_ANI = g.new_edge_property('double') 105 | e_ANI.a = ani_np[ani_np_idx] / 100 106 | g.edge_properties['ANI'] = e_ANI 107 | 108 | # Transform weights 109 | if (args.weight_model != 'None'): 110 | y = g.ep.ANI.copy() 111 | if (args.weight_model == 'LogNormal'): 112 | y.a = 0 - np.log(y.a) 113 | 114 | # Find out connected components with less than 4 members 115 | comp, hist = gt.label_components(g) 116 | g.vertex_properties['CComp'] = comp 117 | v_CC4 = g.new_vertex_property('bool', np.isin(comp.a, np.where(hist >= 4))) 118 | g.vertex_properties['CC4_filter'] = v_CC4 119 | 120 | # Filter out connected components with less than 4 members 121 | if args.filter: 122 | g.set_vertex_filter(g.vp.CC4_filter) 123 | g.purge_vertices() 124 | g.set_vertex_filter(None) 125 | 126 | # Save graph (graph-tool format) 127 | fname = 'graph' 128 | g.save(os.path.join(args.outdir,fname+'.gt.gz')) 129 | g.save(os.path.join(args.outdir,fname+'.xml.gz')) 130 | 131 | def write_classes(filename, graph): 132 | f = open(filename, 'w') 133 | header = "AccessionVersion\tCComp\tBlock\tBlockCC\tBlockCC4" 134 | if 'sHSBM' in g.vp.keys(): 135 | header += "\tsHSBM\tPTU\tHRange" 136 | f.write(header+"\n") 137 | for v in graph.vertices(): 138 | AccessionVersion = graph.vp.AccessionVersion[v] 139 | CComp = str(graph.vp.CComp[v]) 140 | Block = str(graph.vp.Block[v]) 141 | BlockCC = graph.vp.BlockCC[v] 142 | BlockCC4 = graph.vp.BlockCC4[v] 143 | if 'sHSBM' in g.vp.keys(): 144 | sHSBM = graph.vp.sHSBM[v] 145 | Ptu = graph.vp.Ptu[v] 146 | HRange = graph.vp.HRange[v] 147 | f.write("\t".join((AccessionVersion, CComp, Block, BlockCC, BlockCC4, sHSBM, Ptu, HRange)) + "\n") 148 | else: 149 | f.write("\t".join((AccessionVersion, CComp, Block, BlockCC, BlockCC4)) + "\n") 150 | f.close() 151 | 152 | def block_annotation(graph, state): 153 | if args.hierarchical: 154 | levels = state.get_levels() 155 | 156 | # Find the informative hierarchical levels (i.e. the non-redundant levels, those with non-equivalent block assignment) 157 | def check_level_redundancy(l): 158 | x = state.project_partition(l, 0).a 159 | y = state.project_partition(l+1, 0).a 160 | return gt.partition_overlap(x, y, norm=True) == 1 161 | L = len(levels) - 1 162 | redundant_levels = [False]+list(map(check_level_redundancy, reversed(range(L)))) 163 | nr_levels = [L-i for i, x in enumerate(redundant_levels) if not x] 164 | 165 | b = levels[0].get_blocks() 166 | bcc = graph.new_vertex_property('string') 167 | bcc4 = graph.new_vertex_property('string') 168 | for i in np.unique(b.a): 169 | b_filter = (b.a == i) 170 | u = gt.GraphView(graph, vfilt=b_filter) 171 | tmp = [] 172 | r = u.get_vertices()[0] 173 | for l in range(len(levels)): 174 | r = levels[l].get_blocks()[r] 175 | if l in nr_levels: 176 | tmp.append(str(r)) 177 | tmp.reverse() 178 | comp, hist = gt.label_components(u) 179 | for v in u.vertices(): 180 | tag = '_'.join(tmp + [str(comp[v])]) 181 | bcc[v] = tag 182 | bcc4[v] = tag if (hist[comp[int(v)]] >= 4) else '-' 183 | else: 184 | b = state.get_blocks() 185 | bcc = graph.new_vertex_property('string') 186 | bcc4 = graph.new_vertex_property('string') 187 | for i in np.unique(b.a): 188 | b_filter = (b.a == i) 189 | u = gt.GraphView(graph, vfilt=b_filter) 190 | comp, hist = gt.label_components(u) 191 | for v in u.vertices(): 192 | tag = '_'.join([str(i), str(comp[v])]) 193 | bcc[v] = tag 194 | bcc4[v] = tag if (hist[comp[int(v)]] >= 4) else '-' 195 | 196 | return((b, bcc, bcc4)) 197 | 198 | def ptu_annotation(graph): 199 | complex = [] 200 | bcc4 = list(graph.vp.BlockCC4) 201 | bcc4_nr = np.unique(bcc4) 202 | 203 | # The algorithm will join clusters belonging to the same hierarchical branch. However, to save 204 | # time, we only check up to certain hierarchical level and not transverse the 5 upper levels 205 | #common_levels = 7 206 | if bcc4_nr[0] != '-': 207 | common_levels = len(bcc4_nr[0].split('_')) - 5 208 | else: 209 | common_levels = len(bcc4_nr[1].split('_')) - 5 210 | 211 | for n, i in enumerate(bcc4_nr[:-1]): 212 | if i == '-': 213 | continue 214 | i_filter = (np.array(bcc4) == i) 215 | u = gt.GraphView(graph, vfilt=i_filter) 216 | u_vertices = u.num_vertices() 217 | u_edges = u.num_edges() 218 | u_size = np.median(list(u.vp.Size)) 219 | for j in bcc4_nr[n+1:]: 220 | if (j == '-'): 221 | continue 222 | if ('_'.join(j.split('_')[:common_levels]) != '_'.join(i.split('_')[:common_levels])): 223 | continue 224 | j_filter = (np.array(bcc4) == j) 225 | w = gt.GraphView(graph, vfilt=j_filter) 226 | w_vertices = w.num_vertices() 227 | w_edges = w.num_edges() 228 | w_size = np.median(list(w.vp.Size)) 229 | if (u_size > w_size): 230 | s_comp = True if (abs(u_size - w_size) < (u_size * 0.5)) else False 231 | else: 232 | s_comp = True if (abs(u_size - w_size) < (w_size * 0.5)) else False 233 | k_filter = np.logical_or(i_filter, j_filter) 234 | z = gt.GraphView(graph, vfilt=k_filter) 235 | z_vertices = z.num_vertices() 236 | z_edges = z.num_edges() 237 | # z_comp_p is true if the number of intercluster edges is >50% of posible edges between both clusters taking into account their respective densities 238 | z_comp_p = True if (z_edges - (u_edges + w_edges) > (u_vertices * w_vertices*((u_edges-u_vertices)/(u_vertices*(u_vertices-1)/2))*((w_edges-w_vertices)/(w_vertices*(w_vertices-1)/2))*0.5)) else False 239 | if z_comp_p and s_comp: 240 | # Annotate both clusters as belonging to the same PTU 241 | # Find the first cluster already included in one PTU and insert the othe one 242 | for m, c in enumerate(complex): 243 | if (i in c) or (j in c): 244 | if i not in c: 245 | complex[m].append(i) 246 | if j not in c: 247 | complex[m].append(j) 248 | break 249 | else: 250 | complex.append([i, j]) 251 | # Take into account if each cluster were already included in different PTUs 252 | for m, c in enumerate(complex[:-1]): 253 | for l, d in enumerate(complex[m+1:]): 254 | for e in c: 255 | if e in d: 256 | complex[m] = complex[m] + list(set(d) - set(c)) 257 | complex.pop(l+m+1) 258 | break 259 | cmplx = {} 260 | for i in bcc4_nr: 261 | for m, c in enumerate(complex): 262 | if i in c: 263 | cmplx[i] = c[0] 264 | break 265 | else: 266 | cmplx[i] = i 267 | v_sHSBM = graph.new_vertex_property('string') 268 | for v in graph.vertices(): 269 | v_sHSBM[v] = cmplx[graph.vp.BlockCC4[v]] 270 | 271 | #for i in np.unique(list(v_sHSBM)): 272 | # if i == '-': 273 | # continue 274 | # i_filter = (np.array(list(v_sHSBM)) == i) 275 | # u = gt.GraphView(graph, vfilt=i_filter) 276 | # d_intra = (u.num_edges() - u.num_vertices()) / ((u.num_vertices() * (u.num_vertices() - 1))/2) 277 | # if (d_intra < 0.25): 278 | # print(i, str(d_intra)) 279 | # k_filter = np.logical_not(i_filter) 280 | # z = gt.GraphView(graph, vfilt=k_filter) 281 | # d_inter = (g.num_edges()-(u.num_edges()+z.num_edges())) / (u.num_vertices()*z.num_vertices()) 282 | # if (d_inter > 0) and (d_intra/d_inter < 500): 283 | # print(i, str(d_intra), str(d_inter), str(d_intra/d_inter)) 284 | 285 | # Rename as many PTUs as possible aligning the partition labels 286 | v_PtuRef = g.vp.PtuRef.copy() 287 | ptuRef_index={} 288 | for i, p in enumerate(np.unique(list(v_PtuRef))): 289 | ptuRef_index[p] = i 290 | sHSBM_index={} 291 | for i, p in enumerate(np.unique(list(v_sHSBM))): 292 | sHSBM_index[p] = i 293 | ptuRef_num = [] 294 | sHSBM_num = [] 295 | for v in g.vertices(): 296 | ptuRef_num.append(ptuRef_index[v_PtuRef[v]]) 297 | sHSBM_num.append(sHSBM_index[v_sHSBM[v]]) 298 | sHSBM_aligned = gt.align_partition_labels(sHSBM_num, ptuRef_num) 299 | v_Ptu = graph.new_vertex_property('string') 300 | goodPtuLabels = {} 301 | for v in g.vertices(): 302 | if v_sHSBM[v] == '-': 303 | v_Ptu[v] = '-' 304 | elif sHSBM_aligned[int(v)] == ptuRef_num[int(v)]: 305 | v_Ptu[v] = v_PtuRef[v] 306 | if v_sHSBM[v] not in goodPtuLabels: 307 | goodPtuLabels[v_sHSBM[v]] = v_PtuRef[v] 308 | else: 309 | v_Ptu[v] = '?' + v_sHSBM[v] 310 | for v in g.vertices(): 311 | if v_Ptu[v].startswith('?'): 312 | if v_sHSBM[v] in goodPtuLabels: 313 | v_Ptu[int(v)] = goodPtuLabels[v_sHSBM[v]] 314 | 315 | return(v_sHSBM, v_Ptu) 316 | 317 | def hrange_annotation(graph): 318 | ptus = list(graph.vp.Ptu) 319 | 320 | ptu_hrange = {} 321 | for n, i in enumerate(np.unique(ptus)): 322 | if i == '-': 323 | ptu_hrange[i] = '-' 324 | continue 325 | 326 | i_filter = (np.array(ptus) == i) 327 | u = gt.GraphView(graph, vfilt=i_filter) 328 | for l in ['TaxKingdom', 'TaxPhylum', 'TaxClass', 'TaxOrder', 'TaxFamily', 'TaxGenus', 'TaxSpecies']: 329 | taxa = set() 330 | for v in u.vertices(): 331 | if u.vertex_properties[l][v] != '-': 332 | taxa.add(u.vertex_properties[l][v]) 333 | if len(taxa) > 1: 334 | if (l == 'TaxKingdom') or (l == 'TaxPhylum') or (l == 'TaxClass'): 335 | ptu_hrange[i] = 'VI' 336 | break 337 | elif l == 'TaxOrder': 338 | ptu_hrange[i] = 'V' 339 | break 340 | elif l == 'TaxFamily': 341 | ptu_hrange[i] = 'IV' 342 | break 343 | elif l == 'TaxGenus': 344 | ptu_hrange[i] = 'III' 345 | break 346 | elif l == 'TaxSpecies': 347 | ptu_hrange[i] = 'II' 348 | break 349 | else: 350 | ptu_hrange[i] = 'I' 351 | 352 | v_HRange = graph.new_vertex_property('string') 353 | for v in graph.vertices(): 354 | v_HRange[v] = ptu_hrange[graph.vp.Ptu[v]] 355 | 356 | return v_HRange 357 | 358 | state_list, entropy_list = [], [] 359 | for k in range(args.nToss): 360 | if args.hierarchical: 361 | # Nested stochastic block model (hierarchical SBM) 362 | if (args.weight_model == 'None'): 363 | state = gt.minimize_nested_blockmodel_dl(g, deg_corr=args.deg_corr) 364 | elif (args.weight_model == 'Exponential'): 365 | state = gt.minimize_nested_blockmodel_dl(g, deg_corr=args.deg_corr, state_args=dict(recs=[y], rec_types=['real-exponential'])) 366 | else: 367 | state = gt.minimize_nested_blockmodel_dl(g, deg_corr=args.deg_corr, state_args=dict(recs=[y], rec_types=['real-normal'])) 368 | state_0 = state.get_levels()[0] 369 | nClass = len(np.unique(state_0.get_blocks().a)) 370 | else: 371 | # Flat stochastic block model (SBM) 372 | if (args.weight_model == 'None'): 373 | state = gt.minimize_blockmodel_dl(g, deg_corr=args.deg_corr) 374 | elif (args.weight_model == 'Exponential'): 375 | state = gt.minimize_blockmodel_dl(g, deg_corr=args.deg_corr, state_args=dict(recs=[y], rec_types=['real-exponential'])) 376 | else: 377 | state = gt.minimize_blockmodel_dl(g, deg_corr=args.deg_corr, state_args=dict(recs=[y], rec_types=['real-normal'])) 378 | nClass = len(np.unique(state.get_blocks().a)) 379 | entropy = state.entropy() 380 | 381 | # Update state 382 | state_list.append(state) 383 | entropy_list.append(entropy) 384 | print("Toss %d of %d: %d classes, entropy %f" % (k, args.nToss, nClass, entropy)) 385 | 386 | # Save graph 387 | (v_Block, v_BlockCC, v_BlockCC4) = block_annotation(g, state) 388 | g.vertex_properties['Block'] = v_Block 389 | g.vertex_properties['BlockCC'] = v_BlockCC 390 | g.vertex_properties['BlockCC4'] = v_BlockCC4 391 | fname = "SBM_i%d_%d_%f" % (k, nClass, entropy) 392 | write_classes(os.path.join(args.outdir,fname+'.tsv'), g) 393 | pickle.dump([g, state], open(os.path.join(args.outdir,fname+'.pickle'), 'wb'), -1) 394 | k = np.argmin(entropy_list) 395 | state, entropy = state_list[k], entropy_list[k] 396 | if args.hierarchical: 397 | bs = state.get_bs() 398 | bs += [np.zeros(1)] * (args.maxLevels - len(bs)) 399 | state = state.copy(bs=bs, sampling=True) 400 | state_0 = state.get_levels()[0] 401 | nClass = len(np.unique(state_0.get_blocks().a)) 402 | else: 403 | nClass = len(np.unique(state.get_blocks().a)) 404 | print("Selected toss %d: %d classes, entropy %f" % (k, nClass, entropy)) 405 | 406 | # Avoid the transient state 407 | gt.mcmc_equilibrate(state, wait=args.transient, nbreaks=2, multiflip=True, mcmc_args=dict(niter=10), verbose=False) 408 | entropy = state.entropy() 409 | if args.hierarchical: 410 | state_0 = state.get_levels()[0] 411 | nClass = len(np.unique(state_0.get_blocks().a)) 412 | else: 413 | nClass = len(np.unique(state.get_blocks().a)) 414 | print("%d classes, entropy %f" % (nClass, entropy)) 415 | 416 | # Save graph 417 | (v_Block, v_BlockCC, v_BlockCC4) = block_annotation(g, state) 418 | g.vertex_properties['Block'] = v_Block 419 | g.vertex_properties['BlockCC'] = v_BlockCC 420 | g.vertex_properties['BlockCC4'] = v_BlockCC4 421 | fname = "SBM_transient_%d_%f" % (nClass, entropy) 422 | write_classes(os.path.join(args.outdir,fname+'.tsv'), g) 423 | pickle.dump([g, state], open(os.path.join(args.outdir,fname+'.pickle'), 'wb'), -1) 424 | 425 | # Callback to collect the vertex marginal probabilities 426 | dls = [] # Description length history 427 | if args.hierarchical: 428 | pv = [None] * len(state.get_levels()) # Vertex marginals 429 | else: 430 | pv = None # Vertex marginals 431 | pe = None # Edge marginals 432 | def collect_marginals(s): 433 | global pv, pe 434 | if args.hierarchical: 435 | levels = s.get_levels() 436 | pv = [sl.collect_vertex_marginals(pv[l], b=gt.perfect_prop_hash([sl.b])[0]) for l, sl in enumerate(levels)] 437 | pe = levels[0].collect_edge_marginals(pe) 438 | else: 439 | b = gt.perfect_prop_hash([s.b])[0] 440 | pv = s.collect_vertex_marginals(pv, b=b) 441 | pe = s.collect_edge_marginals(pe) 442 | dls.append(s.entropy()) 443 | 444 | # Apply MCMC 445 | gt.mcmc_equilibrate(state, force_niter=args.iters, mcmc_args=dict(niter=10), callback=collect_marginals) 446 | entropy = state.entropy() 447 | if args.hierarchical: 448 | S_mf = [gt.mf_entropy(sl.g, pv[l]) for l, sl in enumerate(state.get_levels())] 449 | S_bethe = gt.bethe_entropy(g, pe)[0] 450 | L = -np.mean(dls) 451 | state_0 = state.get_levels()[0] 452 | nClass = len(np.unique(state_0.get_blocks().a)) 453 | print("%d classes, entropy %f, mean_field %f, bethe %f" % (nClass, entropy, L+sum(S_mf), L+S_bethe+sum(S_mf[1:]))) 454 | else: 455 | S_mf = gt.mf_entropy(g, pv) 456 | S_bethe = gt.bethe_entropy(g, pe)[0] 457 | L = -np.mean(dls) 458 | nClass = len(np.unique(state.get_blocks().a)) 459 | print("%d classes, entropy %f, mean_field %f, bethe %f" % (nClass, entropy, L+S_mf, L+S_bethe)) 460 | 461 | # Save final graph 462 | (v_Block, v_BlockCC, v_BlockCC4) = block_annotation(g, state) 463 | g.vertex_properties['Block'] = v_Block 464 | g.vertex_properties['BlockCC'] = v_BlockCC 465 | g.vertex_properties['BlockCC4'] = v_BlockCC4 466 | (v_sHSBM, v_Ptu) = ptu_annotation(g) 467 | g.vertex_properties['sHSBM'] = v_sHSBM 468 | g.vertex_properties['Ptu'] = v_Ptu 469 | v_HRange = hrange_annotation(g) 470 | g.vertex_properties['HRange'] = v_HRange 471 | fname = "SBM_mcmc_%d_%f" % (nClass, entropy) 472 | write_classes(os.path.join(args.outdir,fname+'.tsv'), g) 473 | pickle.dump([g, state, dls, pv, pe, S_mf, S_bethe], open(os.path.join(args.outdir,fname+'.pickle'), 'wb'), -1) 474 | 475 | # Draw final state 476 | if args.hierarchical: 477 | state_0.draw(output=os.path.join(args.outdir,fname+'.png')) 478 | state_0.draw(output=os.path.join(args.outdir,fname+'.svg')) 479 | e = state_0.get_matrix() 480 | plt.matshow(e.todense()) 481 | plt.savefig(os.path.join(args.outdir,fname+'.blocks.png')) 482 | plt.savefig(os.path.join(args.outdir,fname+'.blocks.svg')) 483 | else: 484 | state.draw(output=os.path.join(args.outdir,fname+'.png')) 485 | state.draw(output=os.path.join(args.outdir,fname+'.svg')) 486 | e = state.get_matrix() 487 | plt.matshow(e.todense()) 488 | plt.savefig(os.path.join(args.outdir,fname+'.blocks.png')) 489 | plt.savefig(os.path.join(args.outdir,fname+'.blocks.svg')) 490 | 491 | # Final version has to be edited by hand to rename new PTUs (those starting with ?) 492 | pickle.dump([g, state], open(os.path.join(args.outdir,'sHSBM.pickle'), 'wb'), -1) 493 | -------------------------------------------------------------------------------- /test/NZ_CP028167.1.fna: -------------------------------------------------------------------------------- 1 | >NZ_CP028167.1 Escherichia coli strain CFSAN064036 plasmid pGMI17-004_2, complete sequence 2 | CCTACAGACCGACCAAGCCGAGACCAAGGATCTATTAAACGCAGCGTTTATCATGCGTATCATTGGTTTGGGTGTGCTACCAAGTTTGCTTGTGGCTTTTGTTAAGGTGGATTATCCGACTTGGGGCAAGGGTTTGATGCGCCGATTGGGCTTGATCGTGGCAAGTCTTGCGCTGATTTTACTGCCTGTGGTGGCGTTCAGCAGTCATTATGCCAGTTTCTTTCGCGTGCATAAGCCGCTGCGTAGCTATGTCAATCCGATCATGCCAATCTACTCGGTGGGTAAGCTTGCCAGTATTGAGTATAAAAAAGCCAGTGCGCCAAAAGATACCATTTATCACGCCAAAGACGCGGTACAAGCAACCAAGCCTGATATGCGTAAGCCACGCCTAGTGGTGTTCGTCGTCGGTGAGACGGCACGCGCCGATCATGTCAGCTTCAATGGCTATGAGCGCGATACTTTCCCACAGCTTGCCAAGATCGATGGCGTGACCAATTTTAGCAATGTCACATCGTGCGGCACATCGACGGCGTATTCTGTGCCGTGTATGTTCAGCTATCTGGGCGCGGATGAGTATGATGTCGATACCGCCAAATACCAAGAAAATGTGCTGGATACGCTGGATCGCTTGGGCGTAAGTATCTTGTGGCGTGATAATAATTCGGACTCAAAAGGCGTGATGGATAAGCTGCCAAAAGCGCAATTTGCCGATTATAAATCCGCGACCAACAACGCCATCTGCAACACCAATCCTTATAACGAATGCCGCGATGTCGGTATGCTCGTTGGCTTAGATGACTTTGTCGCTGCCAATAACGGCAAAGATATGCTGATCATGCTGCACCAAATGGGCAATCACGGGCCTGCGTATTTTAAGCGATATGATGAAAAGTTTGCCAAATTCACGCCAGTGTGTGAAGGTAATGAGCTTGCCAAGTGCGAACATCAGTCCTTGATCAATGCTTATGACAATGCCTTGCTTGCCACCGATGATTTCATCGCTCAAAGTATCCAGTGGCTGCAGACGCACAGCAATGCCTATGATGTCTCAATGCTGTATGTCAGCGATCATGGCGAAAGTCTGGGTGAGAACGGTGTCTATCTACATGGTATGCCAAATGCCTTTGCACCAAAAGAACAGCGCAGTGTGCCTGCATTTTTCTGGACGGATAAGCAAACTGGCATCACGCCAATGGCAACCGATACCGTCCTGACCCATGACGCGATCACGCCGACATTATTAAAGCTGTTTGATGTCACCGCGGACAAAGTCAAAGACCGCACCGCATTCATCCGCTGATTTCTCCCTGTATTTTTTCCAAACCCACCGCACACTCCATTCGTATTATGGGCGGTGGGGTGGGGTTTGTTATGCCGTATTTATCAAATAAACGCCTACTTGCTGAGATGAGTATCGCTCTTGTCATGGCGATCGTTGCCACGCTGACCCTTGAGCACAGTCAGATTGATCTGATGGTCGCTGATTGGTTTTATCTGGGTATGGGGCATTGGATGGTTGCCAAGCAAGCTTTTTTGCCAGATTTGCTACTGTATTCTGGACTAAAAAAGCTGCTGATGGCGATGCTGATCTACTTGCTGGTTGCGACCATTTGCCGTGCTTATCATGAGAAAAAGGGCAATGCTATCACTGCCAAGTGGCTTGTCCCAGTGACAAAATTTCGCGTGCGTGAGCTTGCGTATCTGGTGCTGACTTTGATCCTAGTGCCGACAGTTGTCGCGTCATTGAAGGCATATACTCATGTGGTCTGCCCTGTGCATTTGACGATTTTTGATGGTACGCTGCCGTATTTGCCGATGCTTGATAGTATGCGTAACACCATTCCTGATAAGTGCTTTCCTGCGGCGCATGCCAGTAGCGGATTTGCGCTGTTTGCCTTTGCGTTTGCGCCAAGTTTGCGCCGCCGTCGTGGTGCGATCATCATCGTGGTGATGGCATTGGGCTGGGCGATGGGCTGCTATAAGATGATTATTGGCGATCATTTTTTGAGCCATACGGTGGTGTCGATGATGCTTGCGTGGGCGATGTCGGCAGGGCTTGCGTGGGTGTTTTTTAAGAAGGGTGAACAAGTTTAATCGCCTTTAAAAGTAAAAACGGTCGTATCGGTTATTACGGAACGGTTTTTTTCAGTTGAGTTTCAAAGCGATGATTCATCAACCTGGAAACTACCAATAGGAACGGCTTTACTTACGCCTATCGGTCAAGAATTGATGAAAATATGCGGTTCCACTCCAGATATTGCGTATTTAAATAAATTCCTCAACAAAATAAATGTTGAAGGAAGCCAGGTAAAACTATCAATTATTAATATGTAAAGAGGTCGAGTTACCTGTCTGAGTTCCACTAACCATAATTAAAAGCGACCGCAAAAACTGCCCAGTGAAAACTGTGTTGCCAGTAATCACAAATGCAGCAAGTACTAATAAAGAGCCTGTTAAGCCCGCAAATATGTGGGCTTATTTTTAATTACTCTTCATCATCCCGCAGGAACTGACAAAAATCTTCGCTCCCCATCATATCGACAGTGTTTTTAAATTCGAAAAAAGAAATTCCTGTCATAGCACACACGCGCTGGATAATCAGTTTTTTCTTTTGCTGTCTGACGGTTGCGGTTTCACCAAACTGGATAACAGCCAGGACTGTTTCATCATATGATTTCAGGACAAAATCACAGGTTATATTCTGCACTTCACGGATTAAATCTTTTACAGCTTCCTCGCTACCATGAGGACGAACAAATTCAATGAGCCGTATTTTGGGAAAAACGGAATAATCAGGTATAATATTACGTGAAAGAATGCCTAAAAACTCTGATTCATTATTTGAGAGTAAGGGTACTTTAGTAAAACAATCAACCTCCCCAATGGCTGTAAAAGCATTGGTTGTCAAAGCTGGATTGTTCTTAATAAAATCAGAGAATACATTTTCAGCTGATGGCAAAACAACAGCAGAATTATGTTGTTCATCTCGTTTTTTATTACGGTTAAATATCATTTCATATTCCTTTTAAAAGAGCCTGCGGCAGGCACAGGCAAAAACATCGTTTTTGGACAATCTGATTCAAATGTTAACATTTGACATCGGATTGTCTATCCTGCATTGTTCTTTACTGTCTGTAAGATCGGCGCTCATTCTTCCGGCGTTCTCCCGATACCAAAAAGACGGAACGCAAAACGGCTTTCGATCCACCATTCAAAAATCTGCTCTGCGCAACCACAAACAGTTAAGGCGATCCATGCAGCAAGAACAACGTAACTACTGACATCTGCACCCATGTACCAGGCGATAGTTAAACCCATCACTGCGATACCGCCTAAAAACTGCAACAAGAAACGAAATCCCGCAAGAATCCCCAATGGAATAACAACAACCAGATAAAGGGAAGCAGCCAGAAGGTATCTGACAGGTGTCAGCAGATAAAAGAACCTGGATATTTTCTTTTTGTCAGAATAAACGGCATCATTTGCAACATCGGTTGAATGATCATTCATGCTGTCCCTGATTCTTTGAAAATCTACGATATTATCCTTCATATTGTTATGACCTCAATTTTCAGGTGGGTACATTTTATCAAACATATTAGTAAACTCTGAGTCTTCCCTGGTATCGCCCTCATAACCAATTGTATAAGAGAACTTTAATTTATAAACTTTTCTCCCAATTTTTTCTGAAACTACGAAACTTAAGTTCTTTACTTCCGTATGTTTCATGATTTCTTTTACTGATTTATTAAGAACATCTCTTTTAAAAGCAGGATAATCTGGATACTTATAATCCTTCACCCCCCCCGCCCCTATTGTATAAAGATTCAATTCATCTTTAAGTTCATCTATTGTCATTTCAAAACTATTCGCGCGAGAATTATTACTATATATTTTACGAATGAGTTGATATAGTGATGCAGCATTAACACTTGTCAGTCGAACAACAGATAATAAAACTTGCGTTGTATAACGTTTTTCTTCACCAATAAGTTTACATAAAAATCTTTTCGCTTCTTTCGTAAACTTGATCCCCACTCTACCTTCATTCCTGTAATAAGTAGAAAAAACAGTCAGACTCAAATTCATTGAATCAGGTACATTACTTTTAGTGAACTTAAATCCCAAATCACTACTCAGTTCTAATATTTCATCATGTTTCAGCTTAAGAAGCGTCGTATCTAATATATTAGAGCCATCCCTCAAAGCAAGATAAGCCGCATCAGGCTTAACCTGAACCCATTTAATGTAATCGGCAACTGTCACATAAAATATGTGATCATCATCGAATTCATTTTTAGAGTTTATCTGGCACATGGCTAAAAATAAGACTTTTTTAGCAGCCATAGGCAATGTTGACAGCGTTGAATTCAATTCATTTCGATGTCTCACTTTTGTTAAGCGAGAAAGGGAAGTCTTATTTGTCGTCATTCTCATTGTTCCAACACTCTTGATAATCAGACTTTAACACCAGAAACATGATATTACAACACAATCAATATCATGTTTAACCGAGTATCTTCTCATGTTTCATATCTGTGATTTATCTCACAAAGGTAATCCATGCTCAAAATCCACACTGGATTCAAGGCAGATTAACAACAGATTCAAAATAGATTCACCAGAAAGAACAACGAATTAGTATTCTTATGACACATTTTGTGGATATCACCGAGGAAGCTCTCATGTTTAACCGAGGAAGCTCTCATGTTTAACCGAGGAAGCTCTCATGTTTAACCGAGGAAGCTCTCATGTTTAACCGAGGAAGTTATCATGTTTAACCGAGGAAGCTCTCATGTTTAGACAAAAAACACGCTTAAAATCAATAAATTAGAGCTACTCTAAGTTTTTAAGTTTTTAAGAAAAACAAGAAAAGATAAGGCTTTTAATGCGTGCGAATATTCAGGAACTGTGGATAACTCAACAAAACCAGCAACGATGATGGTTCCTTACCATAAATCTGTCCCCCGATGATTATTACCCGCAAGCTAAAACAAACATCATCAATCATTAAAACTCATAACTTTCAATAACATAAAATCATCAAAAAAATATTTGATGCCTTATGTATCTCAATTTTCAAGACCATAAATTTGTCCCTAGTTAACTTTAACTTATTGAAATAAATATAATATTCGTGCGTACTGATCCTAATGTCACATCGCAAAAATTCCGCTTCCTCGCTCACTCAGTCGCTACGCTCCTGCCGTTCGGCTGCGGCGAGCGGTATCTCCTTTCTCAAAACCAGAGAGATTCAGATCATATGATCCGTTATTTGATCCTTTTTGGATCTTCCACTAAGAAGCCCTGAATCGCGCTCTGCCGCTTCGTTCGATACAACCATGAGGAAAACCATGCGAAATGAAAAAAATCGCTTAAAATGCGATTCAGAGCGTTTTAGGGAGGTTCTGTTAGTTCTCCTGCATTGCGCCCGTAGTTCGCTAACGCTCACACGCGCAGTACAGCGCCCCGCCATGCTGCAATGCACCAGGGCGGGGTAAACTCGCTTGCGCTCGCGCTAACTGCGGCATTCTGCGGCGCAAATCAGCATAATCATGGCCTGCTGGTCAGTTGGGCTTTGCAGGGTGAAGGGATGGCCTGTGCACCCTGGTACGTTCGTCAGCGGCTGCGCCGCGCTTCGGGGCGATGTGGCTCCTGTGCTGCCCTGGTATTTTCGTTCGCCCCTTCCATCCGCTTACGCGGATTAAAAAACGTGATAACAATCACTTATCGAGTAAAAAAACGGGAGAAAACTTGCAAATACGACTTAAAGGTCGTATTATTGTTTCATCGAAAGGGGAATGGCCCCTCTCACAATAGCCCAGCAGGGCAGGAGATTAAAAAAATGAATATCCAGGAAGCATTAAACGTTTTTGGATTATCCGGCGAATTAACTGAAAAAGATATCAAAGCAGCATACAGAAAAGCCGCTTTAAAATATCATCCAGATCGTAACCCGTTAGGGGCTGAACTGATGAAAGCGGTAAATGCAGCTTTTGATGTATTGATGGCAAATATTGATAAAATAAATCAGTTCCAGAGCACTGATGAACATGCACGATATAATTACGGTGATGACCTGGAAAAAGTATTAAACGTTCTTTCTGGTTTATCTGGTCTGGTATTTGAAGTGATAGGTAACTGGGTCTGGATTAGTGGAGAAACCATTACACATAAGGAAACTTTAAAAGAAATCGGGTGTAAATGGGCGGCAAAGAAAAAACAATGGTTTTATCGTCCAGACGAACATAAAAGTTACTGGAATCGTGAAGAACACACGATAGAAGAAATCCGCGCAAAATACGGTACAACCGGACAGCGCAGGGCGACAGGGTGGCAACGCGTGGAAACCAGAGCGTAACCAGAACGGGGGCGAGAAGCCCCCGATATTATAGCCAGCCCGTCTTTTGGGGCATCGAACCCAAAAAGACGGGAACAGCAGCCACGTAGCAAAAAGGGAAACAGATGAACTCTTTTTTTGAACAATATCACCCTGTCTTTGAAGTTGTCTGCCGTATCCTGGGGAACGGCTGGCGCGTGAACAAACTTGATGATTGTTCGTCTCGTATAAAACTGACGTCACCGCAGTTTAAAAATTACTCTGTGCATATTCGGATGGAAAAAGACCGCTTTTCTGTGGTGGGGAGTGTGGATAGTCGTTCCTGGCGTAGTCCGCATCATGTTTGCACGTTATCCAGAAAACGGAATCCCGTTGATATTGCAGCCGATATCGAGCGAAAAATTCTGGTAAATGCTTCGCAGGAGGTGTTACAGGCGATTGAGTATGAGAAACACCAGGTGGAAAAAAAGGACGAAATTCTGATCCTGAAAGGTATGTTATCGCAACTTGTCCAGCTTGAAAGCTGGTATGGGGCATTGACAGGCTTTAAAGCTGAAAACGGATTGAACGGTAAAGTCACCGAACAGGGCGACAGTTATGATTTACAGATTCGGGGCTTGAGTATAGATCAACTTGTTAAAATTACAGGGTATTTGAAACAGTTATGAGAAAAAATAAAGGTGATGTGACTTATTTTCTTGAAAAAGAAGGTGACAATTACAGGCTTACAAAAAGGATAAAAGCCAGAACAAATGTTAAAATCGGGAACAAAACTACGAAAATTACATTGTATGATGCTGTACTGAATGAGAATGAATTACAACATATTGATTTTACGTGTGCCGGATTAAGGGAAGACGATGAGACGCCAGTTAAGAATTTAATTAAGGAGTTTATGTTAAATGAAACCCGATAATACACCTGAAAATGTAAAAAAATTACGTTTAAAAGCAGGATTAACACAAAAGGAATGCAGTCATATTTATGGTGTTGGGCTAAGAACCTGGCAGAAAAAAGAGGAAGTTAATACTCAAAACAGTCAAAGTCTGTCACTGGTTGAGTTTGAGTTTCTTTTGTTACTTGCAGGAGAACATCCTGAATACGTGCTCTGCAAAAGGGAGTCAAAATAAAATGACCTGTGATGGTTTTATATTAGTACCTGGAGTTGCGCTCGTAGCTCGCTGACGCTCACACGCGCAGTACAGCGCCCCGCCATGCTGCAATGCACCAGGGCGGGGTAAACTCGCTTGCGCTCGCGCTAACTGCGGCATTCTGCGGCGCAAATCAGCATAATCATGGCCTGCTGGTCAGTTGGGCTTTGCAGGGTGAAGGGATGGCCTGTGCACCCTGGTACGTTCGTCAGCGGCTGCGCCGCGCTTCGGGGCGATGTGGCTCCTGTGCTGCCCTGGTATTTTCGTTCGCCCCTTCCATCCGCTTACGCGGATTAAAAAACGTGATAACAATCACTTATCGAGTAAAAAAACGGGAGAAAAACTTGCAAATACGACTTAAAGGTCGTATTATTGTTTCATCGAAAGGGGAATGGCCCCTCTCACAATAGCCCAGCAGGGCAGGAGATTTAAAATGAAATGCTCATCTGTTTTTACTTCAACCACTAATCATGTTTTCACATTCGAACGAGTAACGCTTTGCACTATTATCCTTATGCATAAGGATACTGGACAACAGTATGTTGTCATATTTACTGATAACAATAAAATTCGTGACTATAAAGCCGGAATTGTTCCTCAGTTTGGCGAACTTAAGCAGAGTGATGTTGATTTAGTCCTTTTCTACAGGGACGAATATGAAAAATATTTTGATTCATTAAAAGATGGCGATGAATGCTTGAGTTTTAAAGATTTTATTGAGTGTCTTTGTTAAGCATTATGAATGCAGGGGCATCCCTGCATTTTCTGGAAATTTCAATATTGATTCTGAAATTGTCTATTAACATCAATGCAAGGGAAATTCATGAAAACGTTAAAACAAGCCGCAATGCAGTTTGCATCTGATCTGCGTAATAATCGCTGTTTTAAAGCTGCTTATAATGATGCTCTGTTAGAGCTTGATCGGGAAGATTTAAACGCCATTGTACAATGCACATTTCTTTTTGAGCCAAAACGAACATTAGCCGAACTTGATAAATTAATTAAAAAGATAGAAGTAGAAGAAGATTATGAGTTTTGTGTATCCCTTGATAAAGGTAAAGGATTACACGAAGTCAGAACATTTAAAAGTGAGCGTGAGGCGTTGGATGCTTATCGCATTTTGACTCTTTACGGATACACAGTAACTATAGAAAAGAAGGAGGGTAATGACTGACTATTCAAGGTGTGATCAGGTTGTTCCACCAACCTGATCACGTTATCGACAATCAAAACTGAATGAGGTTTTTAAATGTCAACGCGGCAGATCGTACAGCTTGCTGATTCCCTTGTCAAAGGCTGGGAAACCAGAATTCCGGCAATGAAATTTAGCGAATGGGATCAATTCCGGTGGTGGTTAAACTACTTGCAAGGGTATGAAATGTTTTAAATAAAAAAAGGGGCTTTCTGCCCCTTTGTATTTTTTGACAAAAATGCCATGTGTAACTATAATTAGTTGCATATCTATAGGGGATGGATTTATGGGGAAAACAGATAAGCTACTGGCAAAGTTTTTAAACAGTAAAAAAACGTTTGAATGGGATGAACTGGTCGTTTTGTTTTCCTCTTTGGGATATGTCAAAAAGGAAATGCAGGGGTCAAGAGTGCGGTTTTTCAATGCTGAAATCAATCACACCATATTAATGCATCGTCCACATCCGGAAAGTTATATCAAAGGTGGTACGCTGAAAGCAATTAAACAGAATCTGAAAGAGGCTGGATTATTATGAAACATTTAAAATATAAAGGATATTTAGGCACGGTTGAACCCGATTTTGAAAATAATGTCCTGTATGGAAAACTGGCGTTTATTCGGGACCTGGTAACTTATGAGGCTTCTACATTAGCTGAACTGGAACAGGAGTTTAAAACATCTGTTGATCTGTATTTACAGTCTTGTGTGGAGGATGGAAAGGAACCTGATACCCCGTTTAAAGGCGTGTTTAACGTCAGGCTTGATCCAGAACTGCATCGTCGGGTAGCAGAAATGGCGATGGAAGAAGATTTATCGTTGAATGCCTTTGTTAATAAGGCTCTGGAAAAAGAAGTCAGCAATCATCGCGCAGGGGCTTAATTGCCCTTGTTTTTATGCCCTGTAAGGGCATGGGAGGCGCTTTGCGCCGGAAGCCCCGCAGCGCAGCGAGGACTTAAAAAGCGTTACGGCTTCAGCCGTAACAATTCCCAGCCCTGCCAGGCCTGTTTTTTCTTTCACAAATCTTTCAGCTATACGTTTGCCTGATGGTCGAAGACGAAAGAATCTTTCAGCAACGAAATCTGGACATATGCAGCGACGATTTTATAGCCAGGCACAGTGCCACATATGGTACAATCAATGCAGGATAGACAATCCGATGTCAAATGTTAACATTTGAATCAGATTGTCCAAAAACGATGTTTTTGCCTGTGCCTGCCGCAGGCTCTGTTGGGGTGACTTATGAAGATGATAGCGTTCAGGCTAAGTGATGATGAGTTAAAATTTGCAGAACATAACGCTATTTTAAGTGGATTTACCAGTATCAATGCTTTTGCTAAGCATAACGTGCTTAATATTGAAACCAAGCCTGTTAACATCCCAGTTAATAATGAACCAGCAAAGCTGGTATCAGTGCGTCTTTACCCTCATGAAATAGAACTTGTTAAACGTAATGCTGCTCTTCACGGCATGAGCATGAGCAGAGAAATTGCTATTCGTGTGCGTCAGTCTTTGTTAAAGAGTGAAGTCTGTCTTTACCCTGATGAAGTTAAGGAACTTAAAAAACTATCAACTGCTGTAGATCGTGTTGGTCGCAACATTCATTTTATCATAAAAGGTGAAAGATTCTGTACGGTTAACGATCCAGATTTCAGAAAAGACGTTATTGAAGTGATTGAACTGTGTAAGCAAATTGATTCAAAACTTGAAACGTTAACCAAGAGTGTTGTTAACAGATTTGGGTGATTTATGGGTGTGTATGTAGAACAGGAATACCGTATTAAACGCGCTAAAGGGACTGGGCGTGATCCGAAATCTCCTAAGTTATCGGGAAGACATATACATGCCAGTAAATCGGCTTTTAATCATAAAGTCAGACATGGTGCGGACAAGAAAGCATATACCTGGACACCTACAAAAGAAGTCACTTTTAAAATCACTGGCTCAGGTAAAACAGCCGCAGGCATCAAAAATGGTATTGATTACATTACCCGTAATGGTGAACTTGAGGCTTACTGTTATGATGGAAAAGGTTCAGAGCAAACAGGGAAAGGAGAAGACTTTAATAGGGAGTTTACTTCAACTTTATCTAAAGGAAATGATTACTCCAGGACATATCGTGGAGAAAACATCGATCATGTGAAAAACATGGTCTTTTCACCACCACCAGAAGCTGGAGTAAGCAGGGAAGACGCTCTGAAAGCTACGGTAGAATTTCTTAAAGAGACTTATCCAAACCATGCTTTCGTTGCCGTATACCATGATGATAAAGAAGATCATCCTCATGTACATGTTAATATTAAACTTCGTGATGAAGAAACAGGAAGAAGATTAAGACTTACTAAATCTGAAACCAGGAAGTTCAGAAACGGTTTTCATAGAAAACTGAAAGGTATGGGATATGATGTTACCGCTACCTGGAAAAAAGATCCCGAACGTAAACGGGAGATTGAGCGATTACAGGCTGAAAATCCGAAAAGACTCCGGAATGTTTACAAGGTGGTGGATTTTGGGGAGACTTCATATCAGAATAAGGCAGGGGAAAAACGAACACCTTTCCTCACTTATGAAACTTTAAAAGGTGGAAAACAGGTTACGATTTGGGGTAAAGACCTGAAAAATCATTTCGAGTCTGAAAAGCTCCAGCCAGGAACGCTTATCAAAGTAAAAAAACTGGCTCCTACGCTTGTACGTAGTCCAATGTTTAATGATGATGGAACTGTTGCAGGCTACAGGGAAACGCATCGCAATAACTGGCAGATTGAAAATATTGCCCTTGAGCGGAACAGAGAACGTCAGGTTCATGAGCGTGAAACCAGGCAGCCGGAAGAGAGCGACGTTAAAAAGCAGCTATCCAGAAAACATGAGCAAGGTCACAATATCGGGTTTGCTCTTGAACACGGGTTTACAAAGGATTCAGAAGAACATAAAAAACTAAGAATACAGCAAGAGAGGAACTGGAAAGGCCTGGGATTTTAATTTGATTTTGGCGCAGGTGAACAAAGCGGCTACTTTGCTCACCTGCTGATCACAACAACCTACGAGGGAGGTTATCATGACTGAGCATGATGCTATCTGCATTTCTGGACTTCATCAAATTTTTAGTGATGAAGAACATTTGAGTGAGCAACAAAAAGACATTATTCTGATGTACGCTTATGGCTATACCCTGAATGAAATTGCTGATTTCAAAGGACTAAAGCCTTCAACTGTCAGAAAATATCTCGATTCTGTAAGAGCTGAACTGGGAGGTGTCTCGTTGGCAGGGATCAGAACACTGGTACTCATTCGGACTAATGCCCTTCTCGTGTCAAGTCTTTCCAGGATCAGTGAGAGGGGGAATTTATGAAAAAATATCTGCTTTTTGCTTTGCCGTTCTTCGTTGTTGGTTGTAGTGAAGAAGTAAAAAGTGTGGATTGGTGGGGGCAGCATCTTACGGAAGCTAAACAAAAGCAGGCAGAGTGTGAGAAGTCTGGCTCTGATTCTCAGAACTGTAAGAATGTTAAACAAGCATTGTTTATTCAGTCTCAGAAGGATGCTCCAGTCCCTACATTCGATTAACTAATGTTTCAATCATACTAGAGCGCCCATATGGGCGCTTTTTTTTGCATGATCACACGAATCATAATATGACCATTTTGTCAATGTTATTTGATCGTATTGTTGAGCATTGTTATTTTGCGAATCATGTTCGCATCTAAAACAAATCCAAAACACACTTATAGTGGATTGATTAAATGAAGGAGAAATCAGATAAGTCATCGGAAAAAGGGGTTTATGTCACGATAAAACTCGATCCCAGGTTGAATGCATTTATCGAAGAGAAAACGAAAACATCTTTGCTTAGTAAGAGGAAGCAAATTATTTACTTATTAATGCAGAGTATGCGTATTGATGGATATATGGAGCGTTAGCTATGGCGTCTTTATTCCCAGCCTTGTTGCTGTGTGCGGCGTCAGTTCATCCGGATACGATAAATGATATTGCACGGGTCGAGAGTGGCTTCAATCCTTATGCTGTGGCTGAAATTATTCCTGAAAGGGAACGCGGTAGCTATGGCAAAAGTGTTATCAGTCATATGCCCAAAAGTAAGGAGGAAGCTCTAACAGTTATCAGAGAAATTGAAAACAGAAAAAGACGTTATTCTGTCGGATTGATGCAGATTACCAGCAGTAATTTTTCTTTTTATTCCACGTCCGCAGAAAAACTCCTTGATTCGTGTGAAAACTTGTCTGTTTTCGAAAAGATTATCGTGGATTGTTATAAACGAGGGAGGAGCCTGGAGAATGCGCTTAGTTGTTACTATACAGGAAATTTTAGTAACGGTAAGCGAAAAGAAAAAGAATTTAATAATACAAGTTATGTTGAGCGAATTGGTTATACAGGGAACGAAAAAAATATGTTGTACCAGGTACAAGAAGCAACGGGGGAGAACAGCGAAAAAATCGTTCACACAATGCGTCAGTTATCTGGCCCGAAACCATTCTGAAATCTGCTTTTGTTGATAACTCACATCCAACAAAAGTTATTAATTAACAGGACTTATAAAATGTCATTTAAGAAATCAGTATGTAATCGTATCAGTTCATCTGTCACTGCTGTAATGCAGCGTAAAAATAAGGTAGTTATGGCAGTCGCCGGGGCAACAGTTGCATCACCGGCCTTTGCGGATGGATTTTCAAAAGCGGAAACACTGCTGGAAAAGGTTAAAACAGGGCTTAGTGGCCTTTCACTGGTCACGGTGACTATTGCCTGTCTGTGGGTCGGCTACAAGGTTCTGTTTGGTGGAAGCACGATTCGTGAATGCTCACCTATCATCATCGGGGCAATTGTTATCGCCAGTGCGGCTGAAGTTGCATCAATGATGGTTAACTAATAAAGCGGGGACGATAGTATGTCTACACTTTATAAAGCGATGACACGCCCTGCTATGTATGTGGGTGTTCCTGTCGTCCCTCTTACTGTGGTTGCAGGGGCGTTATTTCTGGCGGGGGTTTATATCAGCAAGCTGATCTGGCTGGCTATTCCTGTCGCGGTTTTTGTTCTCAGAATGATCACTAAACAGGACGACCACATTTTTAACCTTTACTTTCTTAAGCTGAAAATGCTCGGAAACTCAGTGTGTAATCGTTTTTTTGGCGCACGAGCGTTCCTCTCCGGACAATATGAAGCTGTTGAAATTGATGAGTTTGTTAATGCCATGAAACTGAATGAGCGAATCACTACTGGTAAATATATACCCTATTCAAGCCATGTTGATAAAAATATTGTCAAAACAAAGAACGGTGATTATGTAGCGACATGGCAACTGATGGGGATTAACTTTGAGTCAATCTCTGCGGAGATGTTAGAAACTATTGACTCACAGGTTGCCACTCTTGTTCGTTCATTTTCCGGACTGCCCGTTTCATTTTATAACCATTCATGCAGGGCGAGTTTTTATGATGCCTTTACCACAAAATCAGGTAATAAATATGCTGATATAATATCAGATTGTTACTATGGTTCGATGAAGAAAAATAAATTCAAGGGGAACACTCTTTACTTTACGCTGATCTACAGACCAGATGGGCGTGTAGAAAAACTGGAAAAACGCAAAAAAAGTATTAAAGAAAAGAAAGATGATATTAACATTCATGTGAAGAGAATGAATGAAATGATTAATACATTCTCTGGCGCTCTTGATAAATTTACCTGTAAACTTCTTGGTATGTATGAGGAGAACGGAAAGGTATTTTCTTCTCAACTGTCATTCTATAATTATCTTCTGACAGGGAAGTTGCAAAAAATCAGAGTCACTGATTCTCCGGTGTATAACGTTCTTGGTGGAGTGGATGTATTTTTTAATCATGATACCGGACAGATATGCCGCATTGATGGAAATAAATTCTTTCGTTCTATTGAAATTAAAGATTTCTGTAGTGAGACTGCATCCGGTGTTTTTGATGTGCTCCAGTATTCTGATGCTGACTATATTATTACGCATTCATATACATCCATGAGCAAATCAGAAGCATTGAGTACCATTAAACGTGCGGAAAAACAGTTAAAGAGTACAGAAGATGATGCGGTTACTCAATTGCAGGAACTGGAAAAGGCAAAAAATGATATTGTGTCCGGTGATATATCCTTTGGTTACTACCATTTCACGCTAATGGTAATGGCGGACAGTATCAGGGAGCTTGACGAATCTGTAAGTAAAATTACGGCAGACTTTACTGACCTGGGCATTATTCCGGCATTGTCAACCATGTCATTGCCAGCGGCATATTTTGCCCAACTTCCGGCGGTTTTTCATTTAAGGCCGCGTTTATCTCCGGTATCGAATGTAAATTTTGTTGAACTGGCATCATTCCATAATTTCTACCAGGGGAAACGCGATAAAAACTGCTGGACAGAAGCGGTTGCTATTCTCAAAACACCAAGCAAACAGGCGTATTATCTGAATTTACATAACTCAGTACTGTTTAAAGATGAAACAGGTGAAAAGAATCTTGCTAATACTAAAGTTATAGGAACGGCGGGTTCAGGTAAGACAATGTTTCTGTCATATCTTGCCTGTTCATTACAGAAATACAATAACCCCGAAACTTTTGCTGATTCTGCAAAAAATAAGAAGTTAACGTGTGTCTTTCTTGATAAGGACAGAGGGGCTGAATTATGTATCCGAATGCTTGGTGGGGAATATTACACCGTAAAAAGCGGTGAGCCGACAGGATGGAATCCTTTTGCTCTGGAGGCAACAAAACGTAACCGTATTTTTGTCAAGCAACTGATGGAGATTCTGTGTACCAGAAATGGTGAAAGATTATCGACAAGAGAAAGATTGCTTATTAGCGAAAGTGTTGATGCTGTGATGGATTTTCCTCCTGGCGAAATGCGGGAATATGGCATTACCAGAATGCTTGAGCATCTTATGCAGAGAGATGATCGGGATGAGCAGGAGAACGGGATTATCCTTCGTTTGTCCCAATGGGCTAATGGTCAGGCTCATGGCTGGGTTTTTGATAATGCTAAAGACACATTCAATATTCAGCATGTCAATAATTTTGGCATTGATGGCACCGAGTTTCTTGATGATCCGATGGTATGTGCACCAATTACTTTCTATCTGCTGTATCGCATTACGCAGCTTCTGGACGGTCGGCGTCTTGTTATCTTCCTGGATGAGTTCTGGAAATGGCTACAGGATGAAGCATTCAGCGACTTTGTCTATAACAAGCTGAAAACAATTCGTAAGCTGAACGGGCTGGTTATTCCGGCAACTCAGTCTCCGGATGAAATACTGAAGAATAAGATTTCACGGGCTGTGGTTGAAGTATGCAGCACCAGTATTTATCTGGCTAACCCTGATGCCGATTACAATGATTATGTGGAAGGGCTTAAGCTGACACCAGAAGAATTTAATATTGTTAAAAATCTTGATCCAATGTCGAGACAGTTCCTGATTAAAAAAAGCAGTCTTAAAAAGGGGGATGGTAAATCATTTTCAGCACTTGCGACACTCGATTTATCGGGACTTGGTGGATATCTGAAAATATTGTCTGCCAGTGCAGATAATCTGGAAATTTTTGAAAGTATTTATCATGAAGGAATGGAGCCTGATGACTGGGTTCCTGAATATCTCGAACGGGCAATCTGACAGGTGAATTTATGAAACGTGTAAAAACATTGATGCTGATATCATTATTGACGGCTTCATTTTATAGTAGCGCAGGAATTCCGGTTGCTATTGATGCCAGTCCGGAATGGGCTGTTGAAGCCGCAAGATGGACAGAACGACTCAAACAATGGTCCGAAACTGCGCAACATTATCAGAGTCAGATTCAGGCATATAAAGACCAGTTAGCCACGGCAACAGGTATAAGGAATATTGCGGCATTCACAAATGAACTAAGTAATCTACAATCAGAACTGACAAATATTTATAAGCAGGGGAACAGTTATATTTCTGATTTTACCAGTAATCCCGAAGGTGCATTGAGCAGCCAGGCTAAAAATCTGTTCAGCAAGTATGGAGCCTTCGATATGTGTAATACGGGCTATGAACGTAATGATAATCTCTGTAAAGCACGGATTGTGTCAACGGCTGCCAGTATTGAACAGGGAAATGAGATCAATAAGCAGTTATCAAGTGCAATGTCTCAGATTCAGAGTTTAAGTAGCCGAATTGAGGCTTCAAAGGATATCAAAGAATCCCAGGATTTGGCTAACGCATTACAGGCGCAGTCGTTAAAAATGCAGGCGATAAAAATGCAGTATGATGTCTGGAATAATAAAAATAAAGCAGATCATGAGATGTTGGTCACACAAGAACAAGAGGCCTTTATTAAACAGCAGAAAGAAGCTGAACCATTAACTTTTGATTAAAAGGTGGATATATGGCTCAGGGGTTTTTCGTTAAATATAATTCTACCGTTATGGATTCGGTAGATAAAATCAGCTCCAGTTATCAAACACAATTTGCTAATGATATTATGTCGTTAGCAACGGTATCGGTTACGTTATATGTACTATGGAAAGGATATCAAATACTTGCCAGTAAAACTCAGACGCCCTTGCAGGATTTAGTATGGGATCTATCAAAATTTGCAATCATAATCATGTTTATAACTAATGCGGATGGGTATTTAACTGCGGCTACTGATGCGTTGCAAGGCATGAAAGATGGTTTTTCTGGAGGGGTGAGTGTCTGGCAAACTTTAGATAACTTATGGAAAAGTACGCAAAATCTTGGTGCGGAAATATACTCTTTAGATAAATCAACATATGTTAAAGATCAAGGTGTAGTTGGTCAGTTTTTAATATGGACTGGCTCGCTTATTTTAATGGCTGTTAGTGTGGTTGTTTTTTTAACAGCAGATGTAACGATGAAACTCCTGATTATTACAGCACCGATATTTATCTTTTGTCTCATGTTTGGTTTTATTCGGAGCATGTTTAATAATTGGTTACAATCGTTATTCTCCAGTATTCTGACAGTTCTATTTGCCAGTTTAGTGATTCGGATAGCAATGGATTTTCAAGGGATGATCCTTTCCCATGCTATTCGTGCTGCACAAACAGGGAATGTAAATCTTGTATCTACAGGTGCTATGGGCTTTATGGCTGGGTTTTTGGGGGCTTTATTGGTTCTTATAGCTAAAGGTTTTGCTGTCCAGTTAGCAGGTGCAGGTGTTGAAGGTGCTGTTCAGGGCGCAGCCATGATGGGGCTGGGTGCTGCTGGTATGGCTACTGGTAAGTCTCTGATGCTTGGCGGTCGTGCTGGTTTAGGTTTTGGCTTAGGGATGGCCGGAAGAACGGGCATGAATTCTTTATCAGGGAAAGCAGGGAATCTGATGGGGCGTGGTGCGCGAACAGCCGCTGAATGGGCGGGTGAAAAGGGATATCAGGCGCTGGCTCCGACAGGTGCTTTGGGGATGAAGGCGAGAAGACTTGCATCCCTGGAAAAAGCACGAGCGAGGAACGCCGCATGAAAATAATGAAACGTTTTTTTTATGTGATGTCCCTGAACTGGATACTGGTGTTCGTTGATAAACATATCACCTCCGGATGGAGCAGGAAGAGAAAGCTAATAACCCAGCTTGTTATTTTAATCACGGGAATTACCTTATTTAAGGTCTTAGGTGGAATCAGTCAGAGCTGGTTGTGACATGCTTCATTATTTTGTCATGATACTTTATTTTGTCCTAATCCTTCTTTTAGGAGTGTTTACCTGCTTCGCAGGAATAAGACTTGGTTTAGGGCTTGCGGGGAAATGGAGAAAGGGTATGGATCTTCGCTTTTTGCTTCTTATTGGTGGGATTCTTTTTCTTCATCTTGGCCTTTATCTCTTAAAGGTGTTCGTGAACGCTTAATAAGGTGAATTTATGCGATGTGTGATTCTTTTGTTTTTCCTGTTTCAGATTTCCGGATGTACCACAAGGAATAACCTTCCTTCTGATGTTTCCGGCGAACTGGAACCCATTAACCACAGCCAGGTAATCAGTTATGAGTGAAAAAATCGAAAAGAAGATTGAGACAGCTAAATCCTTCGAAAGACAACTCTATGCAGAAAATGAACGGTCTAAGAAAAATGCATGGCGTGTTGCAGTGGCAGCATCATTACTGTCCTTATGTCTTGGGGCTGCAATTTGTGTTCTTGTGCCATTAAAAGAAAAAGAGCTTGCTATTGTGGCTGTTGGCGAAAAAACAGGCCGTACAGAACTGATTACTCAGGTTAAACAGGAAAAGATCCTGCAATCTGAAGCACTTGGGCGGTATTTTGTAAACACGTATATTACTTTACGTGAAGGTTATAATTATCCGTCTCTTCAGTATGATTATGAGACTGTACAGCTATATAGCTCGAATACAGTAAAGGACGATTATCTTCGTCTGTATAACAGCGATATGGCTCCTGATAAAATTTATCATAATAACGGTTCTTACGTTGCGGTTGAGGTCATCTCCAATATTATTTCTGACGCAACCGCGCCAGATAAACTGGCCTCTGTGCGCTTTAAAAAAACGACGAGAAACTTCACGACCGGACAGGTTTCAGTTTCATACTGGACGGCTCGTGTGACATATCGTTTTGAACCAGAAAAGTCGGTTAAAAGTTCCAGTCGTGAGCTTAATCCTCTCGGTTTTACTGTAACCAGTTACCAGACAGATCGGGAAGTCAGGGGGGAATGATGAAGAAATTATTTATTGCTACATGTTTATTGCTTCCTGGGCTTGTGTATTCAGCAGCGACTCCGTTACCTTCTGGATTTGATGCAAGGATGCAGACTGTGAGTTATAACGGTGCGAATACGACCGTTATCCGGTCAAAGACAGGATTTCTCACATCAGTTGTTTTTGATGAAGGTGAGGCTGTTATCAGTGCAAAGGCTGGTTTTCCTGCCGGATGGGAAATCACAACCGATGATAATGTTGTTTACATCAATCCGCGTCCCGTTGTTCAGGAACAGGAAGGCGATGAAGGCGAAAAACTGAAAAAGGTTTTCCAGCCAACAGAAAAGGAATGGGATACGAATCTTTTTGTCAGAACAACAAAGCGGATTTACAGTCTGGATCTAATCCTTCTGTCAGAAGAAAAACAAGCGCAACCTGCTTATGTTGTACAATTCCGCTATCCATCAGAAATAGCGAAAAAGAATGCAGAAGAAGTAAGGCTGGCAAAAGAAAAACAGGAAAAACTCAGACAGAAGAAACTGATTTCTGAAAGTTTTGAAAAAGCCGATGCGCCTAAAAACTGGAATTATTTCATGCGAGTCAATGAAAAATATGACAGTCGTCGTATCGCCCCTGATTTTGCTTATGATAATGGCATATTTACCTTTTTAGGTTTTAACTCAGGGAAAGTATTCCCAGCTCCATTTGCTGTAAGAGATGGTCAGGAACAGACACTTGCTTTTAATGTGGAAACAAAAGGCAAGTATAAAATTATGGTTATCCACAACGTTAATGACAAATTTGTTTTACGCTATGGAAACAGCGTTGTTGGTGTTGTGAATAAATCTTTCGGAAAAGTATTAACCGACCAGAGGAATACATCTTCTCCTGCGGTAGAACGTGTTGAGGTGAATAATGACTGATATGAAAAAAACAGATGATTTAGTTGATGATGCGAATCAGTCAAAGCTGGAAAGTAAACCCGTTCATGATATCGGTGATATCAGGAACAAAAATAATCGTATGCGTTCTGGCTTTCTGTTTGGTCTGATGGCTGTATTAGTTATTGGTATTTTTGCGCTGAAAACATATAAAAATTATTTTGCAGATGACCAGCAGAAAGCATCAGAATCAACAGGCGATACCAGCATTTCTCAGGTAAGTAAAATCCGTACTGGTCTTGGTCAGAATTTTGACCCTGTTGAGAACAAAGCAATTATCAATCCAGGCGCTACTGGTTCTGGTAATACTGTTTCTGACGGTCATAAAGAAGTGCCACAGGAGGGGTTCAGGAAATATTTGTCAATACCTGTTGCTGGTCAGGGAGGGAGTCAGACTGGCGCGAGTGGCAGCAGCAGGACATCGCAGACATCAGAGCCACAGGAAGAAAGGAAAGAATCAAAAGAAAATGTGCCTGGTAAATCAGGAATGAAAGTAACTGCGATTAATCTCGATCCTGATCTCTATATTGAGGAAAACAGGCTTATACCGTGTGCTTTAACCACACGCTTTGTTTCTGATGTTGCAGGTCGTATCAGTTGTGTTTTTACAGAAGATGTATGGAGTGCTAATCACCACACAAAATTACTGGAACAGGGAACTAAGGCTTTTGGTCGTTACCAGACCGGAACGCTGAATCATGGACAGGGCAGAATGTTCGTTATGTGGGAGCAGTTGCGGACGCCTGATAATAAACGAATCGATATGGTAAATACAGCAGCAGCTGGTCCCCTGGGAGAAGCGGGCATTGATGGATGGATCGATTCTCACTTCTGGGAGCGTTTTGGTGGTTCTCTTATGCTCAGTATGGTTCAGGATGTAGCGGCGGCGGCGGCGGATAATGCACCTGGTAAAGATCGTAATGTGGATTATACCGAAAACTCACGACAGGCAATGGCTGAAATGGCTAAAGTCGCACTTGAGAATTCGATAAATATTCCTCCAACAATGTATAAAAACCAGGGTGACATAATCACTATTATGGTCGGGGAAGACATTGATTTTTCTGATATCTATGAGTTAAAGGTAAAATAGTATGAGTGGCGTTATTCCTGTCAGCATTAGCAATAAATCGCTCGATTTTTATAAAAATCAAGTATTCCAGTCTTTTCTTGATATTGAAGGGCTAACAGAAATTGCTGTCAACCGTCCTGGTGAAATCTGGACGGAAATTAATGGTGACTGGACATTCCATAATAATGACAGTGTGACCTTTGATTTTTGTAAACGTTTTTCTCATACGCTGGCATCATTTCGGGGAGATGAAATCGGCGAGACCAAGCCATTACTTTCAGCAACACTGGAATCAGGAGAACGTGTACAGGTCGTTTTTCCTCCTGCATGTGAGAGAAATACTATTTCAATCACTATACGTAAACCATCGACACGGCAGATAACACATAAAGAATATGTGGCGAACGGTTTTTATGATTATGTCCAGTCAGGAAAAAAACACAGGACATACGATGATGAGCTTCTTGATTTATTCAGAGCGAGAAATATCGCTGCTTTTATGGAGCTTGCCGTAGCAGCAGGGAAAACCATTGTATTTGCCGGAGCAACGGGTTCCGGTAAAACAACTTATATGAAGAGTCTTATCGATTTCATACCATTGAATACACGGCTGATTACGATTGAAGATGCAGAGGAAATCAAATTTTTTATCCATAAAAATTATGTGCATCTTTTTTACCCTTCGGAATCCGGCAGTGATTCCGGCTCTATCATAACAGCAGCGAAATTAATCAAATCCTGTTTGAGGATGAAACCTGACAGAATTTTACTGGCGGAAATTAAAGGTGGCGATGCCTGGGATTTTGTGAAAGTTGCTGGTTCTGGTCACAGTGGAAGCATGACGTCCATTCACGCGGCCTCGGCAAAAGATGCCATCATTCAGATGGTGACAAAATGTTATCAGAACAGAGAATGTCAGAATCTTCCTTTTGATGTTCTTCGTAAAATCATTATGGACAGTATTGATATTGTTGTTCATGTAGGCAGGGATGGTGTTGTCAGGCATATGAGCGACATCTATTACAAAGGGGCTGAATGTGAAAACTTTTAATAAGTCGCAATCTGTTTTTATATTCGCTGTTATGCTTGTTGCGGCATGGTTTGCAGGTTCATTTATTTTCTTTGTGCTTTACTTCAGCCAGATAAAGAATCTGAAACCATTGAAGGCTGTGTTCAGATCCATTGATGCGTACAGGATGGATATTTTTACAGACAGCCTTACTTTATCTGTTGTGACTACGGATATCAGAGTTTTAGCCTTTGTTGCACTTGGAGCAGGATTTGTCGTGTCTCTTATCATCCCTGTTGCGGCGTTGATAAAACTGAACGAGAAGAAAGAAAATATTTTCGGTGATGCGCGATTTGCAACAATTAATGATATTCGTGAGTCTAACAGTTTCACGCTTGACGGTGACGAAAAAGACGGGATTATCGTTGGTATAAAAGACAAGAAAATCATACGCTATGTGGGAGCAGCATTCAGTGCTATGGGAGCCGGAACAAGGGCTGGTAAAGGTGCTGGTATAGTCATCACTAATCTTATGAAGTACTGGTGGAGTGTTATTATTCTCGATCCAAAAAGGGAGTGTTTTAATATCACCAGTCTTATCAGGAAGGTGATTTTAGGTCATGAGGTTTATAAATTTGATCCCTTTAGTTCGGTAACGCACCGCTTTAACCCTTTGTATTATGTCAGCATGGGAACGAGTGAAGGTTTTAATCAGCTTGAAAATCTTTCGTTGATTATTTACCCCTATAAGACAGATGGTGCGGATGCTGGTAGCTATCTTAATAAGACGGCGGGTGGTGTTTTTAAATCTTATGCTGTCGCTTTATGGTTTATGATTAAGAATGATAAGGCTGGACTAAAAACGCTTGATATTGAGCCTGTCTTCTCAATGTCTAAAATTAATCAGCTTTTTGAGCGTGCAGAACCCGAACATCTTCTTTCATTCATGAATGATATTCGTTCAGAATTAAGGGGGAAAGACAAAACGCTTGCTGATATTGGTGTCGCTGGCCTTAAGGCATTCATTGAGATGGAGGATAAAACAAAAGCACAACTCAAAAGCAACTTTCTTAATGGATTATCACCGTTTGCTAACCCCAATGTGGCAAATGCTACTGATGGAAATGATTTTGATTTGCGGCAGGTGCGGAAAAAGCGGATGACAATTTATTTCTGTATTTCTGGTGATAATGCCAGGTTGGCTGAAAAAATAACAAATATTTTCTTTCAGCTTGCTATACAGGTGAATCTTGAAAAGATGCCAACGGATGATCCGGAAATAAAGCATGACTGCCTTTTTCTACTGGATGAATTCCCGTCAATTGGTGCTGTAGATTACATCAAGAGTAAATCTGGTCTCATTGCTGGTTACAAGCTGAAATTGTTGATTGTTTACCAGGTCGGCTCTCAGCTTGAGGAAATCTACAGTTATGCAGGCAGTAAAACATTACTGGCAAGTGCACCCTGCAAAATTGTTTATTCCGCTTCTGATGTTAAAGATGCGCGGGAGTTATCAGAAGCGATGGGGACACGAACAGTAACTATCGGTTCGAAAAGCAAAAGTCGTAGTCGTGGAGGAACATCGAGATCTGAATCAGAAAGCCTTATTGAGCGTCCACTGGTAACAACAAATGAACTACTTACACTGAAATTTAGTGAAGAAATCCTGGCAATGAAGGGAGAAAACCCGATTCGTTGTGAAAAGGCATTCTACTATCTCAATGAGTATTTCTTTGGTGACTTTGTGAAGGTTGCTCCGGAACTTGCAAACATTTTCCCCAGGAAGAAAGGAAAGCTGGTTATGCCTCCACAGAAAGTATTTGAAAATGAAATCGTCGCAAAAGGGTATCTGGTAGTAAAAGATGTTCCTGATCTCGATAAAGCGGCATAAAGGAGAACATGATGAAGAAGTTAGTGTTTTTATTGATTTCCATTCTTGCTGGTTGTTCATCACCGCCAGAGCCAACACCCGTGCAGTTTGAAAAGGCAAATGAAGTGATCAATCCCTCATTGCCATATGTCCCCGATTTTCACGGTGTCATAAAGTCAGACGTTAGCGGTAAAGGATGGGTTTATGAGATCACTTCCTTATCCGGAGTACAGGCCAGAACGCCGACGTTTTATTACGCTCTGGCTCATGCGGACAGGATTGTAGTGACAACACATGATGCTGGGCTGTGGTTTCGAATCAGAGATCTGTTGAAGATGGAAGGGGCAACAGCAGTCGTGGAATGGCGAAACGAGAAAAGTTTTTTACCTGAACAGGTAAAAATAGTTTTCATTAAAGCACAAAATGAGGTGAAAAATGACTGGAAAAAATAGAGTGGTTTTACCAGCCTTGCTGGCGTTAGGATTAATATCAACAAATGCTAATGCATCTGACCCTTGCGCTTCGGTACTTTGCTTATATGGAAAAGCAGTCGGGCAAGGTGGAGGGAGTGAATGCAGAAGTGCTGAAAAGGATTTTTTTAATATTCTGAAAAAGAAAAAAGGAAGCATTCGCTGGAGTAAAACTTTTGATGCCCGTAAAGCATTTCTGAATCAGTGTTCAACGGCTGATCCTGCTGCTATATCAAAAATCATGAGTAAGTTTGGTCGTGTCAGAGGTTAATTAAAACCTGATAACCGCGCCAACGGGTATCAGGGTACTGCAAGTATCACATTTCGCGCCAACGAAATGTGATGATTAGTTACAATCACTGAGGTTAGAAATGAAAGTAACCAAATCTATTATAGCTATTTTATTGTGCTTATCTGCTGGTAGTGCATAAGTGGATGTAATAAAAAATGCTCGTGCCTTTTTGAAAGGAACCCTCGATCTGAGCGTTCAGGAAGCAGATGAAGATATAGAGGAAAGGGAAGAACTTTATAAAAAGAACGGGGCACAACCTGACTATTTAAGTTACCTGGAGGATTAACACATGCAATATGCATTGTTTGATGGATTTGAACGAAAGTTTTTGCTGGATGCTCTTGAATTCGGTGTTCTGAAGGACTGGAAAGAAAATCCGGTAAAAGAACTTCCTGATATTGATGAATCTGCTCACCCCTTCCATATCTGTTATGGTGGATATTTATTAAACCCTGGTGTTTCAGATTCAGATATTAGCAGAAAAATAAAAGACCAGGCAGGATTCTGGCTGGCAGCTATTGATGATACCCGCATGGATTGTCATTCAATAGCTTATTATGATATTCACACACTCCCTCTGATTTCGTGTGGTCATCAAAAGATAGTTCCTTTTGCCGCGTTAATAAAAGCTGATGAATGCATCATTTCAAAAATTGCTTCGTATTCTGGTTTTGCTGTAACAGCCTTTTTGAGAATTAAAGAGTGGGATATTGCAACTAATATACTTAACCGTGAGGGAATTTTTGCCTTTAATGGCTGTGAGCGCAGATTCAGAGTAGTAAGTAAAGATAACTGGCAACATACAGTATCGGAAGAACGCGCTATCCGTTGTGCCAAAAGATTAATTCAATGTAAAGGATAACAAAATGAGACTTTTTATCGCAGAAAAACCCGCAGTAGCAAATGATATTGTTAAGGCACTTGGTGGCAATTTTACCCGCCATGATGGCTGGTTTGAAAGTGATAACGCCATTGTGACTAACTGTTTTGGTCATATTATCGAATCACAACCGCCGGAAAACTATAATCCTGAATACAAAGCCTGGAAGGTTGAAACGCTTCCTTTACGTCTTTATCCCGTGAAGTATCAGCCTGTTGAAAGTGCAGCAAAACAGGTTAAAACGATTCTCGAACTTATCAGACGTGGAGACGTGACTGAAATTGTTCACGCTGGCGATCCTGATGATGAGGGACAGCTACTGGTTGATGAAGTCCTGGAATATGCAGGCAACACAAAACCCGTAAAGCGCGTTCTGATTAACGACAACACGCTTCCGGCAGTGAAAAAGGCACTGGCAAATCTTAAAGACAATCGTGATTTCAAAGGACTTTACCTTAAGGCGTTGGCGCGGTCAGTTGCCGATGCCGTCTATGGCTTCTCTATGACACGTGCGTACACCATTCCGGCAAAAGCCAGAGGATATCAGGGCGTTCTGTCTGTCGGGCGCGTCCAGACTCCCGTTCTTGGCCTGATTGTGAATCGTACCCGTGCTAACCAGAACCATAAATCCAGTTTTTACTACACCATGACCGGAGTCTTTCAGCGTGGTGCTGATGTTATCAGGGCGAACTGGAAGCCAGGGGAATTTGCTCCGCTGACAGACCGTAAATTACTTGATAAAGCGTGGGCAGACGGAACGGCTGCATCCCTTGCAGGAAAACCGGCTACAGTTGAAGCAGCAGCAACTGATGATAAAAAAACGGCTGCGCCGTTGCCGTTTAATCTGGTCAGACTCCAGCAATACATGAACAAGAAGTTTAAAATGACGGCACAAAAAACGCTGGATATTACGCAACAACTACGCGAAAAATATAAAGCAATTACGTATAACCGCTCTGACTGCTCATATCTTTCAGATGAACAATTCAGCGAAGCGCCGCAGGTTATCGATGCCCTGAAATCAGTCTTTCCTCAGTCGCTGGATATTGATTCTGCACGTAAAAGCAAAGCGTTTAACAGTGCAAAGGTGACTGCGCATACTGCGATAATCCCGACCGCCAGTGTGCCTGATGTTAACGCACTCAGCACCGACGAGCGCAATGTTTACCTGGCGATCGCACAACACTATCTTGTTCAGTTCATGCCTGAAAAAGCATACCAGGAAGTATCGGTTGCCATTCAGTGTGGTGATGAGTCGTTCTATGCCCGTGCCAGAAAAACAACTGACAGCGGATTTGAGGCGTTTCTTGGCGCGGAAACCACAGACGAAGGTGAATCAGAAGATAATGATGATTCCGCTTTTGAACTGCTCTGTAAAATTCGCACAGGAGAAACACTGACGACAAAAGAAGTTATTGTTAATGAGAAGAAAACAACACCGCCGCCGTTATTCACCGAAGCCTCCTTGCTTGCTGCGCTTGTTCGTGTCGCGGATTTTGTCACTGATCCAACAATTAAAAAATTGTTGAAGGATAAGGATAAAGACAAAAAAGATGAACATGGTGGCATTGGTACGCCAGCTACCCGCGCAGCCATTCTGGAAACGCTGAAGAAAAGAAACTATATCACGCTGGAAAAAGGGAAACTTATTCCTACTGATACCGGATATGCGCTTATTGATGCCCTGCCTGGTATAGCGGTTAATCCTGATATGACAGCATTATGGTCTGAAAAGCAGGCTGCCATAGAAAATGGCGACCTGACGGTTGAACAGTTTATTAATGATCTGTACGGTGAACTGACAGGCATGATTTCTGATGTTGACCTGGGCGAGATGAAGATTGAACCCGCTGCGCCAGCAGGGCAGTTTCAACGCCTGGACTCTCCCTGCCCTTCCTGTGGTAAACATATTGTTATCAGGCCGAAAGGTTATTTCTGTACCGGATGTGAATTTAAAATCTGGAGTGAGTTTTCTGGTAAGAAAATCACCCAGGCACAGGCCGAAAAACTGGTTAAATCAGGGAAAACCGATTTGATTAAGGGATTTAAAAAGAAAAGTGGTGGAACGTATGATACAGTTCTTGTCCTTGAGGATAAGAAAACAGGGAAGCTGGGTTTTCCGGCAAGGGCTAAGAAGTGAAAACAAAGCAGGAATGGCTTTTTCAGTTAAGAAAATGTACATCAAGAGATACTCTTGAAAAAGTTATTGAGATTAACCGTTACAAACTGCCTTTATCAGAATCAGAGGCATTTTATTCTGCCGCAGATCATCGCCGAGCAGAACTGGTGATGAATAAACTTTATGATAAGGTGCCTTCCGGCGTCTGGAAGTACGTACACTAAATAAGAGGATTAATTATATGAGCGAACTGACTAAAGAAGATGAATACGGCATTATCAGCCGTACTATGATGAATATTCGTTCATTGCGTGTTTTTGCCCGTGAGATTGATTTTGAGCAGTTGCTCGAAATGCAGGAAAAGCTCAACGTTGTTATTGAAGAACGTCGTGAAGATGCTGAACGTGAAGCGGCTGAACGCCAGGAACTTGAAGCCAAACGTCAGCAGGCCATTGAATACATCATATCTTTAGGTCTTGATCCCGAATCACTCCTTGCTCCTGTAACAGCAGATACAGTAAAAACCAGGCGGAAAGCCAAAGGCGGAGTAAGAAAGGCTAAATACCGCTTTAAGGATGAAAATGGCGAGATCAGAGAATGGTCTGGTAATGGCAAGCGTCCGCTTGCTTTACAGAAGCTGCTTGATGAGGGGCATTTCATGGAAGATTTCCTCATTGAGAAAACTAAGCCGGAACAGGCAGAATAACCCGCCAAGTATACATCACACAGGGCACTGTTGCAAATAGTCGGTGGTGATAAACTTATCATCCCCTTTTGCTGATGGAGCTGCACATGAACCCATTCAAAGGCCGGCATTTTCAGCGTGACATCATTCTGTGGGCCGTACGCTGGTACTGCAAATACGGCATCAGTTACCGTGAGCTGCAGGAGATGCTGGCTGAACGCGGAGTGAATGTCGATCACTCCACGATTTACCGCTGGGTTCAGCGTTATGCGCCTGAAATGGAAAAACGGCTGCGCTGGTACTGGCGTAACCCTTCCGATCTTTGCCCGTGGCACATGGATGAAACCTACGTGAAGGTCAATGGCCGCTGGGCGTATCTGTACCGGGCCGTCGACAGCCGGGGCCGCACTGTCGATTTTTATCTCTCCTCCCGTCGTAACAGCAAAGCTGCATACCGGTTTCTGGGTAAAATCCTCAACAACGTGAAGAAGTGGCAGATCCCGCGATTCATCAACACGGATAAAGCGCCCGCCTATGGTCGCGCGCTTGCTCTGCTCAAACGCGAAGGCCGGTGCCCGTCTGACGTTGAACACCGACAGATTAAGTACCGGAACAACGTGATTGAATGCGATCATGGCAAACTGAAACGGATAATCGGCGCCACGCTGGGATTTAAATCCATGAAGACGGCTTACGCCACCATCAAAGGTATTGAGGTGATGCGTGCACTACGCAAAGGCCAGGCCTCAGCATTTTATTATGGTGATCCCCTGGGCGAAATGCGCCTGGTAAGCAGAGTTTTTGAAATGTAAGGCCTTTGAATAAGACAAAAGGCTGCCTCATCGCTAACTTTGCAACAGTGCCTCACACAGACATACACATAGGTATGTCTGTGTGACATACCTATGTACAGTGCCAGATGGCGTTGCTACGTGAAAAATAACCTACCCTACCCCTGCTTTTTTCGCAGATTTTACGTAGCACACCGTTGCTATAGCATTCATCTAAAAAGCTGAATGTCCTTTTCTGTCGCTGTTGCATTTTAATTCACTGAAAAATATTGAGTAATGAAAATGCCGGTAACGCGTCAAGCTGTTGGCCAACGCGACAGGCAGTATTGGAAGCAACAAAAAAAATGAAAGAAAAATCCCTCAAATGAAGGAGAAAAAACGGAACTACAGCCGAACGACAGGCACAAAAAAACCCGACTGGTAATCGGGCTTTTTTGCGTTTTCCAGTCTTGAGTTGGTGGCTCTGACCGGAGAAGGTTACTCTACACCTAACGTTTACATTTTATTCACTATGGTGCTGTAAAGCAACTAAATGTGGTTAAGGAACCACATTTAGTGGTGTAACGGGTGTTTTGTGTAGCGTATAATCAGTCAGTTAGTCATGTAGTTGGCTTGCAATCCTGCTGTTCCCGTATCTGCGGGTATGAACGATTTTAAAGTCCTGGCTGGATAAACAGGTGGTGCTGTATTCGGTGCTCTGGCAGGAGGAGGTCACTCTACACCTAACAATGCGAGAGAGCCGTTGTGACAGGCAGTGTATGAGCAAGCAACGGTGATTTCCTCCAGTTTTACTGGAGAAACGGATGATTAATATGTTAATCATCGTTCTCAGAGCTGTGGTCGCCGTTGCAAACGCGCTGATTGCAGTTCTGGAACTGATCCGCCAATTCATCGATTGATGACAACGGAAACGTAACTAAGGGCAGGAAGTGAACCGCTTCCTGCCCTTTAACAAAAAAGGAGGATTTATGGATTTTATGAGCATTTTGGGGCGCTGTTTACTTCTTCCTTTCCCTGTTGCGATGTTTGTCGGTTGCCTGTTTCCTGGCATCGATGATCGCTTATCCGGAATGCTTATGAGTGTTGCTGTGGGATGTTTATCGTGGTATGTCACCAGACCGAAGAACCGACAGACAAAGGCCGCGTAAGTGGCCTTTAATTGGGATACCCTTTAGTTACAACATTCTAATAGCTCATTCCATCCTCCGCTCATCCAGCAAACATAACCTCCCTGCCCCTCCCCACTACAGAAAGTGGCGCGCCCCGCCCGTACGGGCGGAATCTAATCGCTTAATTTTGAATGTTGTAACTAAACATACTCATTGCTGTATTGCCACAAGGTTTCGCAGCAAGGGAGGATACCGAAAGTAACGCTGTCACTTTTGCTTTCAAGTGACCAACCAACGGGTACATCACACTGACATACAATAATGTATTTCTTCATGATGTACATCATTGTAATTCATTGTGACATACACTATAGTATGTCCATGTGACATACGCATGCGTACATCACATAGAAATACACATGAGTAAATCACTGTGACATACAAGGGGTACTATGATTATCGTAATCGGTGGTGATAAAGGCGGTACAGGTAAATCACATCTGGCAACCAACTTAACTGTTTGCCTCTCACAACAGGGAAAGAGAACGGGGCTTGTTGAAACAGACCTGAATGGCTCGACCAAAAAATGGAATAAACGCAGAGAACAGGCCGGATTACCGCCCGTCGCATTAAATGAAGCCTATGGCGACATCAGTGCCAAAATAACAAAAATGGCAGATGTAGCAGAAATCCTCATTATAGATACTGCCGGATATGACAGCACGGAGTTCAGAACTGCACTAAAAGTTGCAGACATTGTGATTGTCCCGATCGACCCGCTTGCCCAGGTGGAAGCAGACAGTCTTCAGACTGTGACAAAAATTGTCAGGGATGCGCAAAAAATAAATCCCAGGCTGGCTGCACATGTGCTCCTGTATAAATGCCAGCCAAATACGTTCAGTGAACAACAAGAATTAAGAGACTCTCTCAACAGCCATGATTACTGGCTTAAACCAATGAAAAGCACGGTTTCTTTTCTCCGGGCCTTTGTAAGAGCAATGAATCAGGGAATGGGGGTTCACGAGTTAAAAAGTAATGTTAGCGGTGCCAGCCAGGCAAAAGCACAAATCGAACTCTTGCTGAAAGAACTGGAACTGTAAATCATAGTGACATACACTAATGTACATCATCATGATATACATTAGTGTGAAACAATCAGATTAGAAGGTACATCATGGCAAAGCTGGAACTACCAGAACTAGAACAAAACCAGGAAGTGAACGCCACAAAATTTGTTCAGGACGCAGGAAAACGCCCCACAGAGAACAAAACAAAACTGGCTTCATTCCGCATACCGAATGAATTGCTTGAATTCATTGATAACGAATCAAAAAGACTGCATCGTCCCAAGACAAAAATAATCAAAGCAGCATTACTGGCTTATCAGGATCTGGACGAAAACGAACTCATTAATTTGTGGTTCAGAGCTGACAGAAATGATTAATCATAGCAAGTGGCCCAATAAACTCTTTATCATTTTCTTTTAGATACTTATCTGAATGTACTAACTTCATCACTTTTAAATGTAGTTGTGATTCATTTTTAGTTACTCTTGCAATAAAATACCCTTGTACAGTTGTTGCACGGGTATTTAGGAGCACTAGATGAGCCTTATCGATTTAAGTTTATCAGGATTATCAGAACCAGGAACAAAACTTATTGAAAAAATAAGTGATGCTATTGGTGTGCTTTATGAACCTACAAGAATCAGGAAAAAAGCGAAGGCTGAAGCTGAAGCTAAACGCACAGAGCTAATTTCTAGGTTAGAGCTTGAGGGAATAGAAAAACGGGCAGTTGAGCGTTTTCTCAAACGTGAAACGAAACGACAGGAAAACATTGAAAATATAACAATGCAAGCAGCACAAAGTCTATCTGAGAGTGATAATGTTTCTGATATTGACGAAGATTGGATAGAGGCTTTCTTCAGGGAGTGTGAGGATATTAGTGACGAACAAATGCAAATGCTTTGGGGAAGAATTTTATCCGAAGAAGCAAAGTCAAAAGGTTCGTTTAGTCGTAGAACCTTAAAACTATTATCCACTATAAGCAAAGAAGAAGCGAACCTTATTACTTATTTTGGAAAGTTTGTTTGGCAGGCAAACAAACTCACTCCAATTTTATTTACCGATGAGAATGGAGATACTGAAGGTATAACTTTCGATAAACTTTCGGTTTTAGACTCCTTAGGCGTTATTCAACAAGGCATTGGTTATAGTTTACAAATTATAAATACTCTCAAGTGTATATTCAGTATGGGATTGCGCAATGATTGCCTAATAAAATTTCTGAAATATTTCTGTATCGCATAATTTTTTATATCAGATAAATTGTACTGGATTTCTTAAAAAATTGCAGTATAATTGCCGCAATTATCCCACCGTTTATTTTTTGAGTAGTTTCTCATGATGCAGCATACTTCTGTGTGGTACCGACGCTCGGTCAGTCCGTTTGTTCTTGTGGCGAGTGTTGCCGTTTTCTTGACCGCGACCGCCAATCTTACCTTTTTTGATAAAATCAGCCAAACCTATCCCATCGCGGACAATCTCGGCTTTGTGCTGACGATCGCTGTCGTGCTCTTTGGCGCGATGCTACTGATCACCACGCTGTTATCATCGTATCGCTATGTGCTAAAGCCTGTGTTGATTTTGCTATTAATCATGGGCGCGGTGACCAGTTATTTTACTGACACTTATGGCACGGTCTATGATACGACCATGCTCCAAAATGC 3 | -------------------------------------------------------------------------------- /bin/copla.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import os 4 | import sys 5 | import shutil 6 | import pickle 7 | import pathlib 8 | import argparse 9 | import subprocess 10 | import numpy as np 11 | import pandas as pd 12 | import graph_tool.all as gt 13 | 14 | parser = argparse.ArgumentParser( 15 | description='Predict a plasmid PTU from its sequence') 16 | parser.add_argument('sequence', type=str, 17 | help='nucleotide sequence') 18 | parser.add_argument('refgraph', type=str, 19 | help='pickle of graph with the reference sequences to compare') 20 | parser.add_argument('reflist', type=str, 21 | help='list of reference sequence filenames to compare') 22 | parser.add_argument('outdir', type=str, 23 | help='output directory') 24 | parser.add_argument('-a', '--aminoacid', type=str, 25 | help='aminoacid sequence') 26 | parser.add_argument('-t', '--topology', type=str, 27 | choices=['circular', 'linear'], default='circular', 28 | help='topology of sequence') 29 | parser.add_argument('-k', '--taxKingdom', type=str, 30 | default='-', 31 | help='taxon at kingdom level') 32 | parser.add_argument('-p', '--taxPhylum', type=str, 33 | default='-', 34 | help='taxon at phylum level') 35 | parser.add_argument('-c', '--taxClass', type=str, 36 | default='-', 37 | help='taxon at class level') 38 | parser.add_argument('-o', '--taxOrder', type=str, 39 | default='-', 40 | help='taxon at order level') 41 | parser.add_argument('-f', '--taxFamily', type=str, 42 | default='-', 43 | help='taxon at family level') 44 | parser.add_argument('-g', '--taxGenus', type=str, 45 | default='-', 46 | help='taxon at genus level') 47 | parser.add_argument('-s', '--taxSpecies', type=str, 48 | default='-', 49 | help='taxon at species level') 50 | parser.add_argument('--version', action='version', version='%(prog)s 1.0') 51 | args = parser.parse_args() 52 | 53 | def insert_ani_edges(graph, fname): 54 | idx = {} 55 | for v in graph.vertices(): 56 | idx[graph.vp.AccessionVersion[v]] = v 57 | 58 | with open(fname, 'rt') as fh: 59 | for line in fh: 60 | # ANI output format: 61 | # $ bin/get_ani_identity.sh queries/NC_028464.1.fna CoplaDB.fofn 62 | # queries/NC_028464.1.fna NC_028464.1.fna 100.000 0.000 165 165 63 | # queries/NC_028464.1.fna NC_010643.1.fna 97.546 3.385 122 165 64 | # queries/NC_028464.1.fna NC_009982.1.fna 99.773 0.331 158 165 65 | # queries/NC_028464.1.fna NC_010716.1.fna 97.120 2.937 133 165 66 | items = line.split("\t") 67 | ani = float(items[2]) / 100 68 | #if ani <= 0.7: 69 | # print(line.strip()) 70 | # continue 71 | ref = items[1][0:-4] 72 | e = graph.add_edge(v_qry, graph.vertex(idx[ref])) 73 | graph.ep.ANI[e] = ani 74 | 75 | def insert_fastani_edges(graph, fname): 76 | idx = {} 77 | for v in graph.vertices(): 78 | idx[graph.vp.AccessionVersion[v]] = v 79 | 80 | with open(fname, 'rt') as fh: 81 | for line in fh: 82 | # FastANI output format: 83 | # $ bin/get_fastani_identity.sh queries/NC_028464.1.fna CoplaDB.fofn 84 | # queries/NC_028464.1.fna databases/Copla_RS84/NC_028464.1.fna.gz 100 22 22 85 | # queries/NC_028464.1.fna databases/Copla_RS84/NC_009982.1.fna.gz 99.646 22 22 86 | # queries/NC_028464.1.fna databases/Copla_RS84/NC_010716.1.fna.gz 97.642 20 22 87 | # queries/NC_028464.1.fna databases/Copla_RS84/NC_010643.1.fna.gz 97.4519 18 22 88 | items = line.split("\t") 89 | ani = float(items[2]) / 100 90 | #if ani < 0.8: 91 | # print(line.strip()) 92 | # continue 93 | ref = items[1][21:-7] 94 | e = graph.add_edge(v_qry, graph.vertex(idx[ref])) 95 | graph.ep.ANI[e] = ani 96 | 97 | def run_mobscan(fname, outdir): 98 | pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) 99 | 100 | fname_hmm = os.path.join(outdir, 'hmmscan.log') 101 | fname_dom = os.path.join(outdir, 'hmmscan.domtblout.log') 102 | cmd = ['hmmscan', '--cpu', '9', '--incE', '0.01', '--incdomE', '0.01', '-o', fname_hmm, '--domtblout', fname_dom, 'databases/MOBscan_171004/MOBfamDB', fname] 103 | # Check output of command to handle those plasmids with an empty ORFeome 104 | try: 105 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True) 106 | except subprocess.CalledProcessError: 107 | mob_label = '-' 108 | return(mob_label) 109 | 110 | fname_out = os.path.join(outdir, 'results_tab.tsv') 111 | cmd = ['bin/hmmscan_domtblout_summarize.py', '-e', '0.01', '-i', '0.01', '-c', '0.6', fname_dom, fname_out] 112 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 113 | 114 | mob = [] 115 | if not os.path.isfile(fname_out): 116 | mob_label = '-' 117 | return(mob_label) 118 | with open(fname_out, 'rt') as fh: 119 | for line in fh: 120 | # MOBscan output format: 121 | # $ bin/hmmscan_domtblout_summarize.py -e 0.01 -i 0.01 -c 0.6 queries/NZ_CP014966.1.fna_mobscan/hmm.domtblout.log queries/NZ_CP014966.1.fna_mobscan/results_tab.tsv 122 | # NZ_CP014966.1_97 MOBH T4SS_MOBH 1.00 38 241 6.3e-82 1e-81 123 | # NZ_CP014966.1_191 MOBP T4SS_MOBP1 0.97 45 293 4.3e-63 4.3e-63 124 | if line.strip() == '': 125 | continue 126 | items = line.strip().split("\t", 2) 127 | mob.append(items[1]) 128 | if len(mob) == 0: 129 | mob_label = '-' 130 | else: 131 | mob_label = ';'.join(sorted(mob)) 132 | 133 | return(mob_label) 134 | 135 | def run_conjscan(fname, outdir, type, topology): 136 | pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) 137 | 138 | # Check why using a list does not work 139 | #cmd = ['bin/check_conjugation_systems.sh', fname, outdir, type, topology, 'databases/MacSyFinder_190530/Conjugation'] 140 | #cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True) 141 | cp = subprocess.run('bin/check_conjugation_systems.sh ' + fname + ' ' + outdir + ' ' + type + ' ' + topology + ' databases/MacSyFinder_190530/Conjugation', \ 142 | stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True) 143 | 144 | mpf = [] 145 | fname_out = os.path.join(outdir, 'results_tab.summary.tsv') 146 | # File does not exists if plasmid has no MPF 147 | try: 148 | with open(fname_out, 'rt') as fh: 149 | for line in fh: 150 | if line.strip().startswith('#'): 151 | continue 152 | if line.strip() == '': 153 | continue 154 | items = line.strip().split("\t", 3) 155 | mpf.append(items[2]) 156 | except FileNotFoundError: 157 | pass 158 | if len(mpf) == 0: 159 | mpf_label = '-' 160 | else: 161 | mpf_label = ';'.join(sorted(mpf)) 162 | 163 | return(mpf_label) 164 | 165 | def run_pfinder(fname, outdir): 166 | pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) 167 | 168 | # To search against last PlasmidFinder database 169 | #cmd = ['plasmidfinder.py', '-i', fname, '-o', outdir, '-t', '0.80', '-x', '-q'] 170 | # To search against PlasmidFinder database version from 2019/07/31 171 | cmd = ['plasmidfinder.py', '-i', fname, '-o', outdir, '-t', '0.80', '-x', '-q', '-p', 'databases/PlasmidFinder_190731', '-d', 'enterobacteriaceae,gram_positive'] 172 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 173 | 174 | rep = [] 175 | fname_out = os.path.join(outdir, 'results_tab.tsv') 176 | with open(fname_out, 'rt') as fh: 177 | next(fh) 178 | for line in fh: 179 | if line.strip() == '': 180 | continue 181 | items = line.strip().split("\t", 2) 182 | rep.append(items[1]) 183 | if len(rep) == 0: 184 | rep_label = '-' 185 | else: 186 | rep_label = ';'.join(sorted(rep)) 187 | 188 | return(rep_label) 189 | 190 | def run_blastn_card(fname, outdir): 191 | pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) 192 | 193 | fname_out = os.path.join(outdir, 'card.b6') 194 | cmd = ['blastn', '-task', 'blastn', '-query', fname, '-db', 'databases/CARD_201015/nucleotide_fasta_protein_homolog_model.fasta', '-evalue', '1e-20', '-perc_identity', '80', '-culling_limit', '1', '-outfmt', '6', '-out', fname_out] 195 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 196 | 197 | amr = [] 198 | with open(fname_out, 'rt') as fh: 199 | for line in fh: 200 | if line.strip() == '': 201 | continue 202 | items = line.strip().split("\t", 2) 203 | amr.append(items[1].split('|')[5]) 204 | if len(amr) == 0: 205 | amr_label = '-' 206 | else: 207 | amr_label = ';'.join(sorted(amr)) 208 | 209 | return(amr_label) 210 | 211 | def write_classes(graph, filename): 212 | f = open(filename, 'w') 213 | header = "AccessionVersion\tPTU_Ref\tCComp\tBlock\tBlockCC\tBlockCC4" 214 | if 'sHSBM' in g.vp.keys(): 215 | header += "\tsHSBM\tPTU\tHRange" 216 | f.write(header+"\n") 217 | for v in graph.vertices(): 218 | AccessionVersion = graph.vp.AccessionVersion[v] 219 | PtuRef = str(graph.vp.PtuRef[v]) 220 | CComp = str(graph.vp.CComp[v]) 221 | Block = str(graph.vp.Block[v]) 222 | BlockCC = graph.vp.BlockCC[v] 223 | BlockCC4 = graph.vp.BlockCC4[v] 224 | if 'sHSBM' in g.vp.keys(): 225 | sHSBM = graph.vp.sHSBM[v] 226 | Ptu = graph.vp.Ptu[v] 227 | HRange = graph.vp.HRange[v] 228 | f.write("\t".join((AccessionVersion, PtuRef, CComp, Block, BlockCC, BlockCC4, sHSBM, Ptu, HRange)) + "\n") 229 | else: 230 | f.write("\t".join((AccessionVersion, PtuRef, CComp, Block, BlockCC, BlockCC4)) + "\n") 231 | f.close() 232 | 233 | def block_annotation(graph, state): 234 | levels = state.get_levels() 235 | 236 | # Find the informative hierarchical levels (i.e. the non-redundant levels, those with non-equivalent block assignment) 237 | def check_level_redundancy(l): 238 | x = state.project_partition(l, 0).a 239 | y = state.project_partition(l+1, 0).a 240 | return gt.partition_overlap(x, y, norm=True) == 1 241 | L = len(levels) - 1 242 | redundant_levels = [False]+list(map(check_level_redundancy, reversed(range(L)))) 243 | nr_levels = [L-i for i, x in enumerate(redundant_levels) if not x] 244 | 245 | b = levels[0].get_blocks() 246 | bcc = graph.new_vertex_property('string') 247 | bcc4 = graph.new_vertex_property('string') 248 | for i in np.unique(b.a): 249 | b_filter = (b.a == i) 250 | u = gt.GraphView(graph, vfilt=b_filter) 251 | tmp = [] 252 | r = u.get_vertices()[0] 253 | for l in range(len(levels)): 254 | r = levels[l].get_blocks()[r] 255 | if l in nr_levels: 256 | tmp.append(str(r)) 257 | tmp.reverse() 258 | comp, hist = gt.label_components(u) 259 | for v in u.vertices(): 260 | tag = '_'.join(tmp + [str(comp[v])]) 261 | bcc[v] = tag 262 | bcc4[v] = tag if (hist[comp[int(v)]] >= 4) else '-' 263 | 264 | return((b, bcc, bcc4)) 265 | 266 | def ptu_annotation(graph): 267 | complex = [] 268 | bcc4 = list(graph.vp.BlockCC4) 269 | bcc4_nr = np.unique(bcc4) 270 | 271 | # The algorithm will join clusters belonging to the same hierarchical branch. However, to save 272 | # time, we only check up to certain hierarchical level and not transverse the 5 upper levels 273 | #common_levels = 7 274 | if bcc4_nr[0] != '-': 275 | common_levels = len(bcc4_nr[0].split('_')) - 5 276 | else: 277 | common_levels = len(bcc4_nr[1].split('_')) - 5 278 | 279 | for n, i in enumerate(bcc4_nr[:-1]): 280 | if i == '-': 281 | continue 282 | i_filter = (np.array(bcc4) == i) 283 | u = gt.GraphView(graph, vfilt=i_filter) 284 | u_vertices = u.num_vertices() 285 | u_edges = u.num_edges() 286 | u_size = np.median(list(u.vp.Size)) 287 | for j in bcc4_nr[n+1:]: 288 | if (j == '-'): 289 | continue 290 | if ('_'.join(j.split('_')[:common_levels]) != '_'.join(i.split('_')[:common_levels])): 291 | continue 292 | j_filter = (np.array(bcc4) == j) 293 | w = gt.GraphView(graph, vfilt=j_filter) 294 | w_vertices = w.num_vertices() 295 | w_edges = w.num_edges() 296 | w_size = np.median(list(w.vp.Size)) 297 | if (u_size > w_size): 298 | s_comp = True if (abs(u_size - w_size) < (u_size * 0.5)) else False 299 | else: 300 | s_comp = True if (abs(u_size - w_size) < (w_size * 0.5)) else False 301 | k_filter = np.logical_or(i_filter, j_filter) 302 | z = gt.GraphView(graph, vfilt=k_filter) 303 | z_vertices = z.num_vertices() 304 | z_edges = z.num_edges() 305 | # z_comp_p is true if the number of intercluster edges is >50% of posible edges between both clusters taking into account their respective densities 306 | z_comp_p = True if (z_edges - (u_edges + w_edges) > (u_vertices * w_vertices*((u_edges-u_vertices)/(u_vertices*(u_vertices-1)/2))*((w_edges-w_vertices)/(w_vertices*(w_vertices-1)/2))*0.5)) else False 307 | if z_comp_p and s_comp: 308 | # Annotate both clusters as belonging to the same PTU 309 | # Find the first cluster already included in one PTU and insert the othe one 310 | for m, c in enumerate(complex): 311 | if (i in c) or (j in c): 312 | if i not in c: 313 | complex[m].append(i) 314 | if j not in c: 315 | complex[m].append(j) 316 | break 317 | else: 318 | complex.append([i, j]) 319 | # Take into account if each cluster were already included in different PTUs 320 | for m, c in enumerate(complex[:-1]): 321 | for l, d in enumerate(complex[m+1:]): 322 | for e in c: 323 | if e in d: 324 | complex[m] = complex[m] + list(set(d) - set(c)) 325 | complex.pop(l+m+1) 326 | break 327 | cmplx = {} 328 | for i in bcc4_nr: 329 | for m, c in enumerate(complex): 330 | if i in c: 331 | cmplx[i] = c[0] 332 | break 333 | else: 334 | cmplx[i] = i 335 | v_sHSBM = graph.new_vertex_property('string') 336 | for v in graph.vertices(): 337 | v_sHSBM[v] = cmplx[graph.vp.BlockCC4[v]] 338 | 339 | #for i in np.unique(list(v_sHSBM)): 340 | # if i == '-': 341 | # continue 342 | # i_filter = (np.array(list(v_sHSBM)) == i) 343 | # u = gt.GraphView(graph, vfilt=i_filter) 344 | # d_intra = (u.num_edges() - u.num_vertices()) / ((u.num_vertices() * (u.num_vertices() - 1))/2) 345 | # if (d_intra < 0.25): 346 | # print(i, str(d_intra)) 347 | # k_filter = np.logical_not(i_filter) 348 | # z = gt.GraphView(graph, vfilt=k_filter) 349 | # d_inter = (g.num_edges()-(u.num_edges()+z.num_edges())) / (u.num_vertices()*z.num_vertices()) 350 | # if (d_inter > 0) and (d_intra/d_inter < 500): 351 | # print(i, str(d_intra), str(d_inter), str(d_intra/d_inter)) 352 | 353 | # Rename as many PTUs as possible aligning the partition labels 354 | v_PtuRef = g.vp.PtuRef.copy() 355 | ptuRef_index={} 356 | for i, p in enumerate(np.unique(list(v_PtuRef))): 357 | ptuRef_index[p] = i 358 | sHSBM_index={} 359 | for i, p in enumerate(np.unique(list(v_sHSBM))): 360 | sHSBM_index[p] = i 361 | ptuRef_num = [] 362 | sHSBM_num = [] 363 | for v in g.vertices(): 364 | ptuRef_num.append(ptuRef_index[v_PtuRef[v]]) 365 | sHSBM_num.append(sHSBM_index[v_sHSBM[v]]) 366 | sHSBM_aligned = gt.align_partition_labels(sHSBM_num, ptuRef_num) 367 | v_Ptu = graph.new_vertex_property('string') 368 | goodPtuLabels = {} 369 | for v in g.vertices(): 370 | if v_sHSBM[v] == '-': 371 | v_Ptu[v] = '-' 372 | elif sHSBM_aligned[int(v)] == ptuRef_num[int(v)]: 373 | v_Ptu[v] = v_PtuRef[v] 374 | if v_sHSBM[v] not in goodPtuLabels: 375 | goodPtuLabels[v_sHSBM[v]] = v_PtuRef[v] 376 | else: 377 | v_Ptu[v] = '?' + v_sHSBM[v] 378 | for v in g.vertices(): 379 | if v_Ptu[v].startswith('?'): 380 | if v_sHSBM[v] in goodPtuLabels: 381 | v_Ptu[int(v)] = goodPtuLabels[v_sHSBM[v]] 382 | 383 | return(v_sHSBM, v_Ptu) 384 | 385 | def hrange_annotation(graph): 386 | ptus = list(graph.vp.Ptu) 387 | 388 | ptu_hrange = {} 389 | for n, i in enumerate(np.unique(ptus)): 390 | if i == '-': 391 | ptu_hrange[i] = '-' 392 | continue 393 | 394 | i_filter = (np.array(ptus) == i) 395 | u = gt.GraphView(graph, vfilt=i_filter) 396 | for l in ['TaxKingdom', 'TaxPhylum', 'TaxClass', 'TaxOrder', 'TaxFamily', 'TaxGenus', 'TaxSpecies']: 397 | taxa = set() 398 | for v in u.vertices(): 399 | if u.vertex_properties[l][v] != '-': 400 | taxa.add(u.vertex_properties[l][v]) 401 | if len(taxa) > 1: 402 | if (l == 'TaxKingdom') or (l == 'TaxPhylum') or (l == 'TaxClass'): 403 | ptu_hrange[i] = 'VI' 404 | break 405 | elif l == 'TaxOrder': 406 | ptu_hrange[i] = 'V' 407 | break 408 | elif l == 'TaxFamily': 409 | ptu_hrange[i] = 'IV' 410 | break 411 | elif l == 'TaxGenus': 412 | ptu_hrange[i] = 'III' 413 | break 414 | elif l == 'TaxSpecies': 415 | ptu_hrange[i] = 'II' 416 | break 417 | else: 418 | ptu_hrange[i] = 'I' 419 | 420 | v_HRange = graph.new_vertex_property('string') 421 | for v in graph.vertices(): 422 | v_HRange[v] = ptu_hrange[graph.vp.Ptu[v]] 423 | 424 | return v_HRange 425 | 426 | def plasmid_hrange(graph): 427 | for l in ['TaxKingdom', 'TaxPhylum', 'TaxClass', 'TaxOrder', 'TaxFamily', 'TaxGenus', 'TaxSpecies']: 428 | taxa = set() 429 | for v in graph.vertices(): 430 | if graph.vertex_properties[l][v] != '-': 431 | taxa.add(graph.vertex_properties[l][v]) 432 | if len(taxa) > 1: 433 | if (l == 'TaxKingdom') or (l == 'TaxPhylum') or (l == 'TaxClass'): 434 | hRange = 'VI' 435 | break 436 | elif l == 'TaxOrder': 437 | hRange = 'V' 438 | break 439 | elif l == 'TaxFamily': 440 | hRange = 'IV' 441 | break 442 | elif l == 'TaxGenus': 443 | hRange = 'III' 444 | break 445 | elif l == 'TaxSpecies': 446 | hRange = 'II' 447 | break 448 | else: 449 | hRange = 'I' 450 | 451 | return hRange 452 | 453 | def get_related_plasmids(graph, vfilt): 454 | ref = [] 455 | new = [] 456 | ref_index = {} 457 | new_index = {} 458 | 459 | u = gt.GraphView(graph, vfilt=vfilt) 460 | for v in u.vertices(): 461 | if u.vp.AccessionVersion[v] == qry_acc: 462 | # As the query is not present in the reference network, we get rid of it to calculate the fit of both partitions 463 | continue 464 | if u.vp.sHSBMRef2[v] not in ref_index: 465 | if len(list(ref_index.values())) > 0: 466 | ref_index[u.vp.sHSBMRef2[v]] = np.max(list(ref_index.values())) + 1 467 | else: 468 | ref_index[u.vp.sHSBMRef2[v]] = 0 469 | ref.append(ref_index[u.vp.sHSBMRef2[v]]) 470 | if u.vp.sHSBM2[v] not in new_index: 471 | if len(list(new_index.values())) > 0: 472 | new_index[u.vp.sHSBM2[v]] = np.max(list(new_index.values())) + 1 473 | else: 474 | new_index[u.vp.sHSBM2[v]] = 0 475 | new.append(new_index[u.vp.sHSBM2[v]]) 476 | 477 | return(ref, new) 478 | 479 | # Copy the query plasmid to the working directory 480 | pathlib.Path(args.outdir).mkdir(parents=True, exist_ok=True) 481 | shutil.copy2(args.sequence, args.outdir) 482 | fname_fna = os.path.join(args.outdir, os.path.basename(args.sequence)) 483 | 484 | # Get total base pairs of the query. If query is a multifasta file we asume it is not a closed genome 485 | seq_len = 0 486 | multifasta = False 487 | with open(fname_fna, 'rt') as fh: 488 | next(fh) 489 | for line in fh: 490 | if line.startswith('>'): 491 | multifasta = True 492 | else: 493 | seq_len += len(line.strip()) 494 | 495 | # If not provided use prodigal to get the ORFeome. As per Prokka example we use 100 Kb to use Prodigal autolearning mode 496 | if args.aminoacid: 497 | shutil.copy2(args.aminoacid, args.outdir) 498 | fname_faa = os.path.join(args.outdir, os.path.basename(args.aminoacid)) 499 | else: 500 | fname_faa = fname_fna + '.faa' 501 | if (seq_len >= 100000): 502 | prodigal_mode = 'single' 503 | else: 504 | prodigal_mode = 'meta' 505 | cmd = ['prodigal', '-p', prodigal_mode, '-i', fname_fna, '-a', fname_faa, '-o', '/dev/null', '-q'] 506 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 507 | 508 | # Load RefSeq84 plasmid network 509 | fh = open(args.refgraph, 'rb') 510 | [g, state] = pickle.load(fh) 511 | fh.close() 512 | 513 | # Insert query into the network 514 | qry_acc = '' 515 | v_qry = g.add_vertex() 516 | e = g.add_edge(v_qry, v_qry) 517 | g.vp.AccessionVersion[v_qry] = qry_acc 518 | g.ep.ANI[e] = 1.0 519 | 520 | # Save taxonomic info of the query if available 521 | g.vp.TaxKingdom[v_qry] = args.taxKingdom 522 | g.vp.TaxPhylum[v_qry] = args.taxPhylum 523 | g.vp.TaxClass[v_qry] = args.taxClass 524 | g.vp.TaxOrder[v_qry] = args.taxOrder 525 | g.vp.TaxFamily[v_qry] = args.taxFamily 526 | g.vp.TaxGenus[v_qry] = args.taxGenus 527 | g.vp.TaxSpecies[v_qry] = args.taxSpecies 528 | 529 | # Save as our reference the existing clustering and PTU assignment from the pickle file 530 | qry_null = '' # Using a placeholder until PTU is assigned 531 | g.vp.Block[v_qry] = '0' # Block is a numeric value 532 | g.vertex_properties['BlockRef'] = g.vp.Block.copy() 533 | g.vp.BlockCC[v_qry] = qry_null 534 | g.vertex_properties['BlockCCRef'] = g.vp.BlockCC.copy() 535 | g.vp.BlockCC4[v_qry] = qry_null 536 | g.vertex_properties['BlockCC4Ref'] = g.vp.BlockCC4.copy() 537 | g.vp.sHSBM[v_qry] = qry_null 538 | g.vertex_properties['sHSBMRef'] = g.vp.sHSBM.copy() 539 | g.vp.Ptu[v_qry] = qry_null 540 | g.vertex_properties['PtuRef'] = g.vp.Ptu.copy() 541 | g.vp.HRange[v_qry] = qry_null 542 | g.vertex_properties['HRangeRef'] = g.vp.HRange.copy() 543 | 544 | # Populate query edges with database plasmids 545 | cmd = ['bin/get_ani_identity.pl', fname_fna, args.reflist] 546 | cp = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 547 | insert_ani_edges(g, fname_fna+'.ani.tsv') 548 | 549 | # Additional plasmid characteristics 550 | g.vp.Size[v_qry] = seq_len 551 | g.vp.MOB[v_qry] = run_mobscan(fname_faa, fname_fna+'_mobscan') 552 | genome_type = 'unordered_replicon' if multifasta else 'ordered_replicon' 553 | g.vp.MPF[v_qry] = run_conjscan(fname_faa, fname_fna+'_conjscan', genome_type, args.topology) 554 | g.vp.PFinder[v_qry] = run_pfinder(fname_fna, fname_fna+'_pfinder') 555 | g.vp.AMR[v_qry] = run_blastn_card(fname_fna, fname_fna+'_amr') 556 | 557 | with open(fname_fna+'.qry_info.tsv', 'w') as fh: 558 | fh.write("#Total bp\tMOB\tMPF\tReplicon\tAMR\n") 559 | fh.write("{}\t{}\t{}\t{}\t{}\n".format(g.vp.Size[v_qry], g.vp.MOB[v_qry], g.vp.MPF[v_qry], g.vp.PFinder[v_qry], g.vp.AMR[v_qry])) 560 | 561 | # Check if query belongs to a graph component of less than 4 members 562 | comp, hist = gt.label_components(g) 563 | g.vertex_properties['CComp'] = comp 564 | if hist[comp[v_qry]] <= 4: 565 | i_filter = (comp.a == comp[v_qry]) 566 | u = gt.GraphView(g, vfilt=i_filter) 567 | cl_size = u.num_vertices() 568 | if cl_size < 4: 569 | ptu_pred = '-' 570 | print('PTU could not be assigned') 571 | print('Query is part of a graph component of size {}'.format(cl_size)) 572 | print('However, at least four members are required for PTU assignation') 573 | print('This plasmid could form part of a new, still unnamed, PTU') 574 | print('Other info:') 575 | print(" Size:\t{}".format(g.vp.Size[v_qry])) 576 | print(" MOB:\t{}".format(g.vp.MOB[v_qry])) 577 | print(" MPF:\t{}".format(g.vp.MPF[v_qry])) 578 | print(" Repl:\t{}".format(g.vp.PFinder[v_qry])) 579 | print(" AMR:\t{}".format(g.vp.AMR[v_qry])) 580 | else: 581 | ptu_pred = 'PTU-?' 582 | print('New (putative) PTU') 583 | print('Query is part of a graph component of size {}'.format(cl_size)) 584 | print('This plasmid could form part of a new, still unnamed, PTU') 585 | print('Other info:') 586 | print(" Size:\t{}".format(g.vp.Size[v_qry])) 587 | print(" MOB:\t{}".format(g.vp.MOB[v_qry])) 588 | print(" MPF:\t{}".format(g.vp.MPF[v_qry])) 589 | print(" Repl:\t{}".format(g.vp.PFinder[v_qry])) 590 | print(" AMR:\t{}".format(g.vp.AMR[v_qry])) 591 | with open(fname_fna+'.ptu_prediction.tsv', 'w') as fh: 592 | fh.write("#Predicted_PTU\tHost_Range\tScore\tNotes\n") 593 | score = 1.0 594 | if cl_size < 4: 595 | hrange = '-' 596 | notes = 'PTU could not be assigned. Query is part of a graph component of size {}'.format(cl_size) 597 | else: 598 | hrange = plasmid_hrange(u) 599 | notes = 'New (putative) PTU. Query is part of a graph component of size {}'.format(cl_size) 600 | fh.write("{}\t{}\t{:.4f}\t{}\n".format(ptu_pred, hrange, score, notes)) 601 | with open(fname_fna+'.related_plasmids.tsv', 'w') as fh: 602 | fh.write("#AccessionVersion\tPTU_Ref\tNote\n") 603 | for v in u.vertices(): 604 | note = '*' 605 | fh.write("{}\t{}\t{}\n".format(u.vp.AccessionVersion[v], u.vp.PtuRef[v], note)) 606 | sys.exit() 607 | 608 | # Apply SBM to cluster the query 609 | new_state = state.copy() 610 | new_state.multiflip_mcmc_sweep(d=0, psplit=0, pmerge=0, pmergesplit=0, niter=1000) 611 | 612 | # Apply sHSBM algorithm to the new graph with the query 613 | (v_Block, v_BlockCC, v_BlockCC4) = block_annotation(g, new_state) 614 | g.vertex_properties['Block'] = v_Block 615 | g.vertex_properties['BlockCC'] = v_BlockCC 616 | g.vertex_properties['BlockCC4'] = v_BlockCC4 617 | (v_sHSBM, v_Ptu) = ptu_annotation(g) 618 | g.vertex_properties['sHSBM'] = v_sHSBM 619 | g.vertex_properties['Ptu'] = v_Ptu 620 | v_HRange = hrange_annotation(g) 621 | g.vertex_properties['HRange'] = v_HRange 622 | write_classes(g, fname_fna+'.sHSBM.tsv') 623 | 624 | # TODO: Code is easier if sHSBM column has the data of all clusters and not only those with 4 or more members 625 | v_sHSBM2 = g.vp.sHSBM.copy() 626 | v_sHSBMRef2 = g.vp.sHSBMRef.copy() 627 | for v in g.vertices(): 628 | if v_sHSBM2[v] == '-': 629 | v_sHSBM2[v] = g.vp.BlockCC[v] 630 | if v_sHSBMRef2[v] == '-': 631 | v_sHSBMRef2[v] = g.vp.BlockCCRef[v] 632 | g.vertex_properties['sHSBM2'] = v_sHSBM2 633 | g.vertex_properties['sHSBMRef2'] = v_sHSBMRef2 634 | 635 | # Get all plasmids of the PTU assigned to the query 636 | strict_filter = (np.array(list(g.vp.sHSBM2)) == g.vp.sHSBM2[v_qry]) 637 | #(ref_strict, new_strict) = get_related_plasmids(g, strict_filter) 638 | #if len(ref_strict) == 0: 639 | # # Query was assigned to a singleton 640 | # overlap_strict = 1.0 641 | #else: 642 | # overlap_strict = gt.partition_overlap(ref_strict, new_strict, norm=True) 643 | 644 | # Use the reference clustering of these plasmids to expand the selection to all plasmids of all PTUs with a member in the new PTU assigned to the query 645 | sHSBMRef2_list = set() 646 | u = gt.GraphView(g, vfilt=strict_filter) 647 | for v in u.vertices(): 648 | sHSBMRef2_list.add(u.vp.sHSBMRef2[v]) 649 | expand_filter = np.full_like(strict_filter, False) 650 | for i in sHSBMRef2_list: 651 | i_filter = (np.array(list(g.vp.sHSBMRef2)) == i) 652 | expand_filter = np.logical_or(expand_filter, i_filter) 653 | (ref_expand, new_expand) = get_related_plasmids(g, expand_filter) 654 | if len(ref_expand) == 0: 655 | # Query was assigned to a singleton 656 | overlap_expand = 1.0 657 | else: 658 | overlap_expand = gt.partition_overlap(ref_expand, new_expand, norm=True) 659 | 660 | # Predicted PTU is the most frequent reference label among the new cluster 661 | w = gt.GraphView(g, vfilt=expand_filter) 662 | cl_size = u.num_vertices() 663 | tmp_ptu = np.delete(np.array(list(u.vp.PtuRef)), np.argwhere(np.array(list(u.vp.PtuRef)) == qry_null)) 664 | if cl_size < 4: 665 | ptu_pred = '-' 666 | ptu_related = '-' 667 | # Make sure ptu_related is not '-' if there is another option 668 | # This works because cl_size <= 3 and one of them is the previously deleted qry_null 669 | for i in tmp_ptu: 670 | if i != '-': 671 | ptu_related = i 672 | print('PTU could not be assigned') 673 | print('Query is part of a sHSBM cluster of size {}'.format(cl_size)) 674 | print('However, at least four members are required for PTU assignation') 675 | print('This plasmid could form part of a new, still unnamed, PTU') 676 | if ptu_related != '-': 677 | print('Query is related to {} plasmids'.format(ptu_related)) 678 | print('Other info:') 679 | print(" Size:\t{}".format(g.vp.Size[v_qry])) 680 | print(" MOB:\t{}".format(g.vp.MOB[v_qry])) 681 | print(" MPF:\t{}".format(g.vp.MPF[v_qry])) 682 | print(" Repl:\t{}".format(g.vp.PFinder[v_qry])) 683 | print(" AMR:\t{}".format(g.vp.AMR[v_qry])) 684 | with open(fname_fna+'.ptu_prediction.tsv', 'w') as fh: 685 | fh.write("#Predicted_PTU\tHost_Range\tScore\tNotes\n") 686 | hrange = '-' 687 | score = overlap_expand 688 | notes = 'PTU could not be assigned. Query is part of a sHSBM cluster of size {}'.format(cl_size) 689 | fh.write("{}\t{}\t{:.4f}\t{}\n".format(ptu_pred, hrange, score, notes)) 690 | with open(fname_fna+'.related_plasmids.tsv', 'w') as fh: 691 | fh.write("#AccessionVersion\tPTU_Ref\tNote\n") 692 | for v in w.vertices(): 693 | note = '*' if g.vp.sHSBM[v] == g.vp.sHSBM[v_qry] else '' 694 | fh.write("{}\t{}\t{}\n".format(w.vp.AccessionVersion[v], w.vp.PtuRef[v], note)) 695 | else: 696 | unique, counts = np.unique(list(tmp_ptu), return_counts=True) 697 | ptu_pred = unique[counts.argmax()] 698 | ptu_related = '-' 699 | if (ptu_pred == '-') or (ptu_pred != u.vp.Ptu[v_qry]): 700 | if ptu_pred != u.vp.Ptu[v_qry]: 701 | ptu_related = ptu_pred 702 | ptu_pred = 'PTU-?' 703 | print('New (putative) PTU') 704 | print('Query is part of a sHSBM cluster of size {}'.format(cl_size)) 705 | print('This plasmid could form part of a new, still unnamed, PTU') 706 | if ptu_related != '-': 707 | print('Query is related to {} plasmids'.format(ptu_related)) 708 | print('Other info:') 709 | print(" Size:\t{}".format(g.vp.Size[v_qry])) 710 | print(" MOB:\t{}".format(g.vp.MOB[v_qry])) 711 | print(" MPF:\t{}".format(g.vp.MPF[v_qry])) 712 | print(" Repl:\t{}".format(g.vp.PFinder[v_qry])) 713 | print(" AMR:\t{}".format(g.vp.AMR[v_qry])) 714 | with open(fname_fna+'.ptu_prediction.tsv', 'w') as fh: 715 | fh.write("#Predicted_PTU\tHost_Range\tScore\tNotes\n") 716 | hrange = plasmid_hrange(u) 717 | score = overlap_expand 718 | notes = 'New (putative) PTU. Query is part of a sHSBM cluster of size {}'.format(cl_size) 719 | fh.write("{}\t{}\t{:.4f}\t{}\n".format(ptu_pred, hrange, score, notes)) 720 | with open(fname_fna+'.related_plasmids.tsv', 'w') as fh: 721 | fh.write("#AccessionVersion\tPTU_Ref\tNote\n") 722 | for v in w.vertices(): 723 | note = '*' if g.vp.sHSBM[v] == g.vp.sHSBM[v_qry] else '' 724 | fh.write("{}\t{}\t{}\n".format(w.vp.AccessionVersion[v], w.vp.PtuRef[v], note)) 725 | else: 726 | print('Query is a {} plasmid'.format(ptu_pred)) 727 | print('Query is part of a sHSBM cluster of size {}'.format(cl_size)) 728 | print('Other info:') 729 | print(" Size:\t{}".format(g.vp.Size[v_qry])) 730 | print(" MOB:\t{}".format(g.vp.MOB[v_qry])) 731 | print(" MPF:\t{}".format(g.vp.MPF[v_qry])) 732 | print(" Repl:\t{}".format(g.vp.PFinder[v_qry])) 733 | print(" AMR:\t{}".format(g.vp.AMR[v_qry])) 734 | with open(fname_fna+'.ptu_prediction.tsv', 'w') as fh: 735 | fh.write("#Predicted_PTU\tHost_Range\tScore\tNotes\n") 736 | p_filter = (np.array(list(g.vp.PtuRef)) == ptu_pred) 737 | p_filter[int(v_qry)] = True # Include query 738 | x = gt.GraphView(g, vfilt=p_filter) 739 | hrange = plasmid_hrange(x) 740 | score = overlap_expand 741 | notes = 'Query is a {} plasmid'.format(ptu_pred) 742 | hr_ref = g.vp.HRangeRef[list(x.vertices())[0]] 743 | if hr_ref != hrange: 744 | notes += '. Former PTU host range was {}'.format(hr_ref) 745 | print('Query inclusion has caused PTU host range to increase from {} to {}'.format(hr_ref, hrange)) 746 | fh.write("{}\t{}\t{:.4f}\t{}\n".format(ptu_pred, hrange, score, notes)) 747 | with open(fname_fna+'.related_plasmids.tsv', 'w') as fh: 748 | fh.write("#AccessionVersion\tPTU_Ref\tNote\n") 749 | for v in w.vertices(): 750 | note = '*' if g.vp.sHSBM[v] == g.vp.sHSBM[v_qry] else '' 751 | fh.write("{}\t{}\t{}\n".format(w.vp.AccessionVersion[v], w.vp.PtuRef[v], note)) 752 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------