ctb/README.md

## README.md

      
    Raw
  

              README.md
            
          
    multik analysis of strain variation


## cmp-multik-multisig.py
#! /usr/bin/env python
"""
Do a multi-k Jaccard resemblance analysis between many genome
signatures and a single metagenome.

Usage:

  cmp-multik-multisig.py metagenome.sig query-1.sig [ query-2.sig ... ]

Construct signatures with:

  sourmash compute -k 21,31,41,51 --dna --scaled 10000 *.fa

"""
import sys
sys.path.insert(0, '/Users/t/dev/sourmash')
import sourmash_lib
from sourmash_lib.signature import load_signatures
from collections import defaultdict
import csv


KSIZES=[21,31,41,51]


def main():
    import argparse
    p = argparse.ArgumentParser()
    p.add_argument('against_sig')
    p.add_argument('query_sigs', nargs='+')
    args = p.parse_args()

    minhashdict = defaultdict(dict)
    signame_to_file = {}

    for sigfile in args.query_sigs:
        sigs = load_signatures(sigfile)
        for sig in sigs:
            ksize = sig.minhash.ksize
            minhashdict[sig.name()][ksize] = sig.minhash
        signame_to_file[sig.name()] = sigfile

    against_minhashes = {}
    against_name = None
    for against_sig in load_signatures(args.against_sig):
        ksize = against_sig.minhash.ksize
        against_minhashes[ksize] = against_sig.minhash
        against_name = against_sig.name()

    csvout = csv.writer(sys.stdout)
    for signame in minhashdict:
        results = []

        for K in KSIZES:
            against = against_minhashes[K]
            query = minhashdict[signame][K]
            match = query.jaccard(against)
            results.append(match)

        sigfile = signame_to_file[signame]
        results.insert(0, sigfile)
        results.insert(0, signame)
        results.insert(0, against_name)

        csvout.writerow(results)


if __name__ == '__main__':
    main()

## multik.csv

          
            podar-reads
            Salinispora_tropica_CNB-440
            g47.fa.sig
            0.9858585858585859
            0.9758064516129032
            0.9602649006622517
            0.9513184584178499

            
              podar-reads
              Sulfitobacter_NAS-14.1_scf_1099451320477_
              g61.fa.sig
              1.0
              0.997093023255814
              1.0
              0.9943502824858758

            
              podar-reads
              Zymomonas_mobilis_subsp._mobilis_ZM4
              g59.fa.sig
              0.9735449735449735
              0.9639175257731959
              0.9581151832460733
              0.9540816326530612

            
              podar-reads
              Enterococcus_faecalis_V583
              g20.fa.sig
              0.6798679867986799
              0.5945945945945946
              0.5946843853820598
              0.5578231292517006

            
              podar-reads
              Clostridium_thermocellum_ATCC_27405
              g16.fa.sig
              1.0
              1.0
              1.0
              0.9973404255319149

            
              podar-reads
              Bacteroides_thetaiotaomicron_VPI-5482
              g5.fa.sig
              1.0
              0.9983739837398374
              1.0
              0.9966887417218543

            
              podar-reads
              Persephonella_marina_EX-H1
              g55.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Geobacter_sulfurreducens_PCA
              g23.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Nitrosomonas_europaea_ATCC_19718
              g35.fa.sig
              1.0
              0.9959349593495935
              0.9922480620155039
              1.0

            
              podar-reads
              Pyrococcus_furiosus_DSM_3638
              g42.fa.sig
              0.9947916666666666
              1.0
              0.9893048128342246
              1.0

            
              podar-reads
              Shewanella_baltica_OS223, complete genome
              g64.fa.sig
              0.949685534591195
              0.9352380952380952
              0.8930693069306931
              0.8210332103321033

            
              podar-reads
              Chloroflexus_aurantiacus_J-10-fl
              g15.fa.sig
              1.0
              1.0
              0.9979166666666667
              1.0

            
              podar-reads
              Haloferax_volcanii_DS2
              g24.fa.sig
              0.9824561403508771
              0.9637681159420289
              0.9636363636363636
              0.9640287769784173

            
              podar-reads
              Pyrobaculum_aerophilum_str._IM2
              g39.fa.sig
              1.0
              1.0
              1.0
              0.9919354838709677

            
              podar-reads
              Sulfitobacter_sp._EE-36_scf_1099451318008_
              g60.fa.sig
              0.9967213114754099
              1.0
              1.0
              0.9967105263157895

            
              podar-reads
              Archaeoglobus_fulgidus_DSM_4304
              g4.fa.sig
              1.0
              1.0
              1.0
              0.9954954954954955

            
              podar-reads
              Treponema_denticola_ATCC_35405
              g57.fa.sig
              1.0
              1.0
              1.0
              0.9965397923875432

            
              podar-reads
              Burkholderia1_xenovorans_LB400_chromosome_1,_complete_sequence
              g8.fa.sig
              0.916135881104034
              0.8467391304347827
              0.7883995703544576
              0.7740585774058577

            
              podar-reads
              Methanococcus_maripaludis_strain_S2,_complete_sequence
              g31.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Salinispora_arenicola_CNS-205
              g46.fa.sig
              0.9761904761904762
              0.965034965034965
              0.9526515151515151
              0.9547038327526133

            
              podar-reads
              Thermus_thermophilus_HB27
              g56.fa.sig
              0.7965116279069767
              0.772972972972973
              0.7128205128205128
              0.6502242152466368

            
              podar-reads
              Chlorobiumtepidum_TLS
              g14.fa.sig
              1.0
              1.0
              1.0
              0.9949748743718593

            
              podar-reads
              Methanopyrus_kandleri_AV19
              g32.fa.sig
              1.0
              1.0
              0.9940119760479041
              1.0

            
              podar-reads
              Sulfurihydrogenibium_yellowstonense_SS-5
              g49.fa.sig
              0.9872611464968153
              0.9726775956284153
              0.9647887323943662
              0.96

            
              podar-reads
              Acidobacterium_capsulatum_ATCC_51196
              g1.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Methanocaldococcus_jannaschii_DSM_2661
              g29.fa.sig
              1.0
              1.0
              0.9941176470588236
              1.0

            
              podar-reads
              Thermotoga_sp._RQ2
              g54.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Pyrobaculum_calidifontis_JCM_11548
              g41.fa.sig
              1.0
              0.9947368421052631
              1.0
              1.0

            
              podar-reads
              Sulfolobus_tokodaii str. 7 DNA, complete genome
              g63.fa.sig
              1.0
              1.0
              0.9963898916967509
              1.0

            
              podar-reads
              Bacteroides_vulgatus_ATCC_8482
              g6.fa.sig
              0.9978021978021978
              1.0
              1.0
              0.997872340425532

            
              podar-reads
              Nostoc_sp._PCC_7120_DNA
              g36.fa.sig
              0.998371335504886
              1.0
              0.9983792544570502
              1.0

            
              podar-reads
              Wolinella_succinogenes_DSM_1740
              g58.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Hydrogenobaculum_sp._Y04AAS1
              g26.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Chlorobiumlimicola_DSM_245
              g11.fa.sig
              0.9961832061068703
              1.0
              0.9960629921259843
              0.9965870307167235

            
              podar-reads
              Dictyoglomus_turgidum_DSM_6724
              g19.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Chlorobiumphaeovibrioides_DSM_265
              g13.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Fusobacterium_nucleatum_subsp._nucleatum_ATCC_25586
              g21.fa.sig
              0.17757009345794392
              0.10476190476190476
              0.08196721311475409
              0.04700854700854701

            
              podar-reads
              Caldibescii_DSM_6725
              g10.fa.sig
              1.0
              0.9966216216216216
              0.9964664310954063
              1.0

            
              podar-reads
              Desulfovibrio_vulgaris_DP4
              g18.fa.sig
              0.7911764705882353
              0.7566765578635015
              0.7238372093023255
              0.6676136363636364

            
              podar-reads
              Caldisaccharolyticus_DSM_8903
              g9.fa.sig
              0.9963369963369964
              1.0
              0.9966442953020134
              0.9963503649635036

            
              podar-reads
              SulfuriYO3AOP1
              g50.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Methanosarcina_acetivorans_str._C2A
              g33.fa.sig
              0.9962264150943396
              0.996219281663516
              0.9927007299270073
              0.9907407407407407

            
              podar-reads
              Leptothrix_cholodnii_SP-6
              g28.fa.sig
              0.972972972972973
              0.9681908548707754
              0.9632034632034632
              0.9515789473684211

            
              podar-reads
              Akkermansia_muciniphila_ATCC_BAA-835
              g3.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Thermotoga_petrophila_RKU-1
              g53.fa.sig
              0.9947368421052631
              0.9945945945945946
              0.9751552795031055
              0.9788359788359788

            
              podar-reads
              Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence
              g17.fa.sig
              0.9688473520249221
              0.9516129032258065
              0.9519230769230769
              0.9146757679180887

            
              podar-reads
              Ignicoccus_hospitalis_KIN4/I
              g27.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Bordetella_bronchiseptica_strain_RB50
              g7.fa.sig
              0.9274509803921569
              0.9032846715328468
              0.8853974121996303
              0.8665413533834586

            
              podar-reads
              Gemmatimonas_aurantiaca_T-27_DNA
              g22.fa.sig
              1.0
              1.0
              1.0
              0.997737556561086

            
              podar-reads
              Rhodopirellula_baltica_SH_1_complete_genome
              g44.fa.sig
              1.0
              1.0
              0.9985401459854014
              1.0

            
              podar-reads
              Chlorobiumphaeobacteroides_DSM_266
              g12.fa.sig
              0.9963369963369964
              1.0
              1.0
              0.9966101694915255

            
              podar-reads
              Shewanella_baltica_OS185
              g48.fa.sig
              0.9869848156182213
              0.9783889980353635
              0.9577981651376147
              0.9314814814814815

            
              podar-reads
              Pelodictyon_phaeoclathratiforme_BU-1
              g37.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Porphyromonas_gingivalis_ATCC_33277_DNA
              g38.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Pyrobaculum_arsenaticum_DSM_13514
              g40.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Pyrococcus_horikoshii_OT3_DNA
              g43.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Ruegeria_pomeroyi_DSS-3
              g45.fa.sig
              0.9628712871287128
              0.9327146171693735
              0.8989071038251366
              0.8634020618556701

            
              podar-reads
              Herpetosiphon_aurantiacus_ATCC_23779
              g25.fa.sig
              0.996875
              0.9879725085910653
              0.9854014598540146
              0.9938556067588326

            
              podar-reads
              Desulfovibrio_piger_ATCC_29098
              g62.fa.sig
              1.0
              0.9962962962962963
              0.9957983193277311
              0.9924528301886792

            
              podar-reads
              Nanoarchaeum_equitans_Kin4-M
              g34.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Aciduliprofundum_boonei_T469
              g2.fa.sig
              1.0
              1.0
              0.9939393939393939
              1.0

            
              podar-reads
              Methanococcus_maripaludis_C5
              g30.fa.sig
              1.0
              1.0
              1.0
              1.0

            
              podar-reads
              Thermoanaerobacter_pseudethanolicus_ATCC_33223
              g51.fa.sig
              1.0
              1.0
              0.9956331877729258
              1.0

            
              podar-reads
              Thermotoga_neapolitana_DSM_4359
              g52.fa.sig
              1.0
              1.0
              0.9951923076923077
              0.9884393063583815

## multik.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "x = []\n",
    "r = csv.reader(open('multik.csv'))\n",
    "\n",
    "a = next(r)\n",
    "KSIZES = [21,31,41,51]\n",
    "for against, query_name, query_file, *kvals in r:\n",
    "    x.append((query_name, query_file, *[ float(i) for i in kvals ]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence\n",
      "55\n"
     ]
    },
    {
     "data": {
      "image/png": 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3T37nx6Qf3kj9vu2cMTYE5TKbgwb7VlQzurSRYl0dNTGb9rZuKhu6sCZqsA+3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103c9a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "for name, filename, *kvals in x:\n",
    "    if kvals[-1] <= 0.9:\n",
    "        continue\n",
    "    pylab.plot(KSIZES, kvals, '-o')\n",
    "    if kvals[-1] <= 0.93:\n",
    "        print(name)\n",
    "    n += 1\n",
    "    \n",
    "pylab.axis(ymin=0.93)\n",
    "pylab.title('K-size response for top 55/64 species in the reads')\n",
    "pylab.xlabel('k-mer size')\n",
    "pylab.ylabel('Jaccard resemblance vs metagenome (estimated)')\n",
    "#pylab.legend(loc='upper right')\n",
    "print(n)\n",
    "pylab.savefig('top.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zymomonas_mobilis_subsp._mobilis_ZM4\n",
      "Enterococcus_faecalis_V583\n",
      "Shewanella_baltica_OS223, complete genome\n",
      "Haloferax_volcanii_DS2\n",
      "Burkholderia1_xenovorans_LB400_chromosome_1,_complete_sequence\n",
      "Salinispora_arenicola_CNS-205\n",
      "Thermus_thermophilus_HB27\n",
      "Sulfurihydrogenibium_yellowstonense_SS-5\n",
      "Fusobacterium_nucleatum_subsp._nucleatum_ATCC_25586\n",
      "Desulfovibrio_vulgaris_DP4\n",
      "Leptothrix_cholodnii_SP-6\n",
      "Thermotoga_petrophila_RKU-1\n",
      "Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence\n",
      "Bordetella_bronchiseptica_strain_RB50\n",
      "Shewanella_baltica_OS185\n",
      "Ruegeria_pomeroyi_DSS-3\n",
      "16\n"
     ]
    },
    {
     "data": {
      "image/png": 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K5a4rqAqOa5FxLdKukPHiWbVIO0JGbTJOkIyKyXOFjFcvq0JGIatC1rVG4l7+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\nJ0SwKUigIYDUCRpT3IhLPpw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P2YTe3l62P/ccu59+mgObN9P7wgsMeQQST6dZ0djIkmiU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\n/wZYj02uthRLlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1056231d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "for name, filename, *kvals in x:\n",
    "    if kvals[-1] > 0.98:\n",
    "        continue\n",
    "    pylab.plot(KSIZES, kvals, '-o', label=filename)\n",
    "    n += 1\n",
    "    print(name)\n",
    "    \n",
    "print(n)\n",
    "    \n",
    "pylab.axis(ymin=0.0)\n",
    "#pylab.legend(loc='upper right')\n",
    "pylab.title('K-size response for bottom 16/64 species in the reads')\n",
    "pylab.xlabel('k-mer size')\n",
    "pylab.ylabel('Jaccard resemblance vs metagenome (estimated)')\n",
    "pylab.savefig('bottom.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geobacter_sulfurreducens_PCA\n",
      "Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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l7vhy4BSgtq/fIlUdUeoITbWR3DCZds3bsTx/+R9+5f65zZ+ZfK/9yg2n1e+t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\nyf8BkiSjaYbGHT8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053b7f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "for name, filename, *kvals in x:\n",
    "    if name not in ('Geobacter_sulfurreducens_PCA', 'Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence'):\n",
    "        continue\n",
    "    pylab.plot(KSIZES, kvals, '-o', label=name[:15] + '...')\n",
    "    print(name)\n",
    "    n += 1\n",
    "    \n",
    "pylab.axis(ymin=0.85, ymax=1.02)\n",
    "pylab.title('K-size response for two species (example)')\n",
    "pylab.xlabel('k-mer size')\n",
    "pylab.ylabel('Jaccard resemblance vs metagenome (estimated)')\n",
    "pylab.legend(loc='lower left')\n",
    "print(n)\n",
    "pylab.savefig('example.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	#! /usr/bin/env python
	"""
	Do a multi-k Jaccard resemblance analysis between many genome
	signatures and a single metagenome.

	Usage:

	cmp-multik-multisig.py metagenome.sig query-1.sig [ query-2.sig ... ]

	Construct signatures with:

	sourmash compute -k 21,31,41,51 --dna --scaled 10000 *.fa

	"""
	import sys
	sys.path.insert(0, '/Users/t/dev/sourmash')
	import sourmash_lib
	from sourmash_lib.signature import load_signatures
	from collections import defaultdict
	import csv


	KSIZES=[21,31,41,51]


	def main():
	import argparse
	p = argparse.ArgumentParser()
	p.add_argument('against_sig')
	p.add_argument('query_sigs', nargs='+')
	args = p.parse_args()

	minhashdict = defaultdict(dict)
	signame_to_file = {}

	for sigfile in args.query_sigs:
	sigs = load_signatures(sigfile)
	for sig in sigs:
	ksize = sig.minhash.ksize
	minhashdict[sig.name()][ksize] = sig.minhash
	signame_to_file[sig.name()] = sigfile

	against_minhashes = {}
	against_name = None
	for against_sig in load_signatures(args.against_sig):
	ksize = against_sig.minhash.ksize
	against_minhashes[ksize] = against_sig.minhash
	against_name = against_sig.name()

	csvout = csv.writer(sys.stdout)
	for signame in minhashdict:
	results = []

	for K in KSIZES:
	against = against_minhashes[K]
	query = minhashdict[signame][K]
	match = query.jaccard(against)
	results.append(match)

	sigfile = signame_to_file[signame]
	results.insert(0, sigfile)
	results.insert(0, signame)
	results.insert(0, against_name)

	csvout.writerow(results)


	if __name__ == '__main__':
	main()
podar-reads	Salinispora_tropica_CNB-440	g47.fa.sig	0.9858585858585859	0.9758064516129032	0.9602649006622517	0.9513184584178499
podar-reads	Sulfitobacter_NAS-14.1_scf_1099451320477_	g61.fa.sig	1.0	0.997093023255814	1.0	0.9943502824858758
podar-reads	Zymomonas_mobilis_subsp._mobilis_ZM4	g59.fa.sig	0.9735449735449735	0.9639175257731959	0.9581151832460733	0.9540816326530612
podar-reads	Enterococcus_faecalis_V583	g20.fa.sig	0.6798679867986799	0.5945945945945946	0.5946843853820598	0.5578231292517006
podar-reads	Clostridium_thermocellum_ATCC_27405	g16.fa.sig	1.0	1.0	1.0	0.9973404255319149
podar-reads	Bacteroides_thetaiotaomicron_VPI-5482	g5.fa.sig	1.0	0.9983739837398374	1.0	0.9966887417218543
podar-reads	Persephonella_marina_EX-H1	g55.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Geobacter_sulfurreducens_PCA	g23.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Nitrosomonas_europaea_ATCC_19718	g35.fa.sig	1.0	0.9959349593495935	0.9922480620155039	1.0
podar-reads	Pyrococcus_furiosus_DSM_3638	g42.fa.sig	0.9947916666666666	1.0	0.9893048128342246	1.0
podar-reads	Shewanella_baltica_OS223, complete genome	g64.fa.sig	0.949685534591195	0.9352380952380952	0.8930693069306931	0.8210332103321033
podar-reads	Chloroflexus_aurantiacus_J-10-fl	g15.fa.sig	1.0	1.0	0.9979166666666667	1.0
podar-reads	Haloferax_volcanii_DS2	g24.fa.sig	0.9824561403508771	0.9637681159420289	0.9636363636363636	0.9640287769784173
podar-reads	Pyrobaculum_aerophilum_str._IM2	g39.fa.sig	1.0	1.0	1.0	0.9919354838709677
podar-reads	Sulfitobacter_sp._EE-36_scf_1099451318008_	g60.fa.sig	0.9967213114754099	1.0	1.0	0.9967105263157895
podar-reads	Archaeoglobus_fulgidus_DSM_4304	g4.fa.sig	1.0	1.0	1.0	0.9954954954954955
podar-reads	Treponema_denticola_ATCC_35405	g57.fa.sig	1.0	1.0	1.0	0.9965397923875432
podar-reads	Burkholderia1_xenovorans_LB400_chromosome_1,_complete_sequence	g8.fa.sig	0.916135881104034	0.8467391304347827	0.7883995703544576	0.7740585774058577
podar-reads	Methanococcus_maripaludis_strain_S2,_complete_sequence	g31.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Salinispora_arenicola_CNS-205	g46.fa.sig	0.9761904761904762	0.965034965034965	0.9526515151515151	0.9547038327526133
podar-reads	Thermus_thermophilus_HB27	g56.fa.sig	0.7965116279069767	0.772972972972973	0.7128205128205128	0.6502242152466368
podar-reads	Chlorobiumtepidum_TLS	g14.fa.sig	1.0	1.0	1.0	0.9949748743718593
podar-reads	Methanopyrus_kandleri_AV19	g32.fa.sig	1.0	1.0	0.9940119760479041	1.0
podar-reads	Sulfurihydrogenibium_yellowstonense_SS-5	g49.fa.sig	0.9872611464968153	0.9726775956284153	0.9647887323943662	0.96
podar-reads	Acidobacterium_capsulatum_ATCC_51196	g1.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Methanocaldococcus_jannaschii_DSM_2661	g29.fa.sig	1.0	1.0	0.9941176470588236	1.0
podar-reads	Thermotoga_sp._RQ2	g54.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Pyrobaculum_calidifontis_JCM_11548	g41.fa.sig	1.0	0.9947368421052631	1.0	1.0
podar-reads	Sulfolobus_tokodaii str. 7 DNA, complete genome	g63.fa.sig	1.0	1.0	0.9963898916967509	1.0
podar-reads	Bacteroides_vulgatus_ATCC_8482	g6.fa.sig	0.9978021978021978	1.0	1.0	0.997872340425532
podar-reads	Nostoc_sp._PCC_7120_DNA	g36.fa.sig	0.998371335504886	1.0	0.9983792544570502	1.0
podar-reads	Wolinella_succinogenes_DSM_1740	g58.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Hydrogenobaculum_sp._Y04AAS1	g26.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Chlorobiumlimicola_DSM_245	g11.fa.sig	0.9961832061068703	1.0	0.9960629921259843	0.9965870307167235
podar-reads	Dictyoglomus_turgidum_DSM_6724	g19.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Chlorobiumphaeovibrioides_DSM_265	g13.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Fusobacterium_nucleatum_subsp._nucleatum_ATCC_25586	g21.fa.sig	0.17757009345794392	0.10476190476190476	0.08196721311475409	0.04700854700854701
podar-reads	Caldibescii_DSM_6725	g10.fa.sig	1.0	0.9966216216216216	0.9964664310954063	1.0
podar-reads	Desulfovibrio_vulgaris_DP4	g18.fa.sig	0.7911764705882353	0.7566765578635015	0.7238372093023255	0.6676136363636364
podar-reads	Caldisaccharolyticus_DSM_8903	g9.fa.sig	0.9963369963369964	1.0	0.9966442953020134	0.9963503649635036
podar-reads	SulfuriYO3AOP1	g50.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Methanosarcina_acetivorans_str._C2A	g33.fa.sig	0.9962264150943396	0.996219281663516	0.9927007299270073	0.9907407407407407
podar-reads	Leptothrix_cholodnii_SP-6	g28.fa.sig	0.972972972972973	0.9681908548707754	0.9632034632034632	0.9515789473684211
podar-reads	Akkermansia_muciniphila_ATCC_BAA-835	g3.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Thermotoga_petrophila_RKU-1	g53.fa.sig	0.9947368421052631	0.9945945945945946	0.9751552795031055	0.9788359788359788
podar-reads	Deinococcus_radiodurans_R1_chromosome_1,_complete_sequence	g17.fa.sig	0.9688473520249221	0.9516129032258065	0.9519230769230769	0.9146757679180887
podar-reads	Ignicoccus_hospitalis_KIN4/I	g27.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Bordetella_bronchiseptica_strain_RB50	g7.fa.sig	0.9274509803921569	0.9032846715328468	0.8853974121996303	0.8665413533834586
podar-reads	Gemmatimonas_aurantiaca_T-27_DNA	g22.fa.sig	1.0	1.0	1.0	0.997737556561086
podar-reads	Rhodopirellula_baltica_SH_1_complete_genome	g44.fa.sig	1.0	1.0	0.9985401459854014	1.0
podar-reads	Chlorobiumphaeobacteroides_DSM_266	g12.fa.sig	0.9963369963369964	1.0	1.0	0.9966101694915255
podar-reads	Shewanella_baltica_OS185	g48.fa.sig	0.9869848156182213	0.9783889980353635	0.9577981651376147	0.9314814814814815
podar-reads	Pelodictyon_phaeoclathratiforme_BU-1	g37.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Porphyromonas_gingivalis_ATCC_33277_DNA	g38.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Pyrobaculum_arsenaticum_DSM_13514	g40.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Pyrococcus_horikoshii_OT3_DNA	g43.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Ruegeria_pomeroyi_DSS-3	g45.fa.sig	0.9628712871287128	0.9327146171693735	0.8989071038251366	0.8634020618556701
podar-reads	Herpetosiphon_aurantiacus_ATCC_23779	g25.fa.sig	0.996875	0.9879725085910653	0.9854014598540146	0.9938556067588326
podar-reads	Desulfovibrio_piger_ATCC_29098	g62.fa.sig	1.0	0.9962962962962963	0.9957983193277311	0.9924528301886792
podar-reads	Nanoarchaeum_equitans_Kin4-M	g34.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Aciduliprofundum_boonei_T469	g2.fa.sig	1.0	1.0	0.9939393939393939	1.0
podar-reads	Methanococcus_maripaludis_C5	g30.fa.sig	1.0	1.0	1.0	1.0
podar-reads	Thermoanaerobacter_pseudethanolicus_ATCC_33223	g51.fa.sig	1.0	1.0	0.9956331877729258	1.0
podar-reads	Thermotoga_neapolitana_DSM_4359	g52.fa.sig	1.0	1.0	0.9951923076923077	0.9884393063583815