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Volumn 18, Issue 1, 2017, Pages

Experimental design and quantitative analysis of microbial community multiomics

Author keywords

[No Author keywords available]

Indexed keywords

EXPERIMENTAL DESIGN; HUMAN; MICROBIAL COMMUNITY; MICROBIOME; MOLECULAR EPIDEMIOLOGY; NONHUMAN; QUANTITATIVE ANALYSIS; ANIMAL; BIOLOGY; DNA BARCODING; GENE EXPRESSION PROFILING; METABOLOMICS; METAGENOMICS; METHODOLOGY; MICROFLORA; PROCEDURES; PROTEOMICS;

EID: 85035330563     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1359-z     Document Type: Review
Times cited : (140)

References (145)
  • 1
  • 5
    • 85017527948 scopus 로고    scopus 로고
    • Microbial strain-level population structure and genetic diversity from metagenomes
    • Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 2017;27:626-38.
    • (2017) Genome Res , vol.27 , pp. 626-638
    • Truong, D.T.1    Tett, A.2    Pasolli, E.3    Huttenhower, C.4    Segata, N.5
  • 6
    • 84961392884 scopus 로고    scopus 로고
    • Strain-level microbial epidemiology and population genomics from shotgun metagenomics
    • Scholz M, Ward DV, Pasolli E, Tolio T, Zolfo M, Asnicar F, et al. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat Methods. 2016;13:435-8.
    • (2016) Nat Methods , vol.13 , pp. 435-438
    • Scholz, M.1    Ward, D.V.2    Pasolli, E.3    Tolio, T.4    Zolfo, M.5    Asnicar, F.6
  • 7
    • 84862276328 scopus 로고    scopus 로고
    • Structure, function and diversity of the healthy human microbiome
    • Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207-14.
    • (2012) Nature , vol.486 , pp. 207-214
  • 8
    • 77950251400 scopus 로고    scopus 로고
    • A human gut microbial gene catalogue established by metagenomic sequencing
    • Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59-65.
    • (2010) Nature , vol.464 , pp. 59-65
    • Qin, J.1    Li, R.2    Raes, J.3    Arumugam, M.4    Burgdorf, K.S.5    Manichanh, C.6
  • 12
    • 84908325271 scopus 로고    scopus 로고
    • Artificial sweeteners induce glucose intolerance by altering the gut microbiota
    • Suez J, Korem T, Zeevi D, Zilberman-Schapira G, Thaiss CA, Maza O, et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014;514:181-6.
    • (2014) Nature , vol.514 , pp. 181-186
    • Suez, J.1    Korem, T.2    Zeevi, D.3    Zilberman-Schapira, G.4    Thaiss, C.A.5    Maza, O.6
  • 13
    • 84994738020 scopus 로고    scopus 로고
    • Linking the human gut microbiome to inflammatory cytokine production capacity
    • Schirmer M, Smeekens SP, Vlamakis H, Jaeger M, Oosting M, Franzosa EA, et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell. 2016;167:1125-36.
    • (2016) Cell , vol.167 , pp. 1125-1136
    • Schirmer, M.1    Smeekens, S.P.2    Vlamakis, H.3    Jaeger, M.4    Oosting, M.5    Franzosa, E.A.6
  • 14
    • 84968901892 scopus 로고    scopus 로고
    • Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity
    • Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T, et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science. 2016;352:565-9.
    • (2016) Science , vol.352 , pp. 565-569
    • Zhernakova, A.1    Kurilshikov, A.2    Bonder, M.J.3    Tigchelaar, E.F.4    Schirmer, M.5    Vatanen, T.6
  • 15
    • 84971201113 scopus 로고    scopus 로고
    • Gut microbiota, metabolites and host immunity
    • Rooks MG, Garrett WS. Gut microbiota, metabolites and host immunity. Nat Rev Immunol. 2016;16:341-52.
    • (2016) Nat Rev Immunol , vol.16 , pp. 341-352
    • Rooks, M.G.1    Garrett, W.S.2
  • 16
  • 17
    • 84949675208 scopus 로고    scopus 로고
    • The microbiome quality control project: baseline study design and future directions
    • Sinha R, Abnet CC, White O, Knight R, Huttenhower C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 2015;16:276.
    • (2015) Genome Biol , vol.16 , pp. 276
    • Sinha, R.1    Abnet, C.C.2    White, O.3    Knight, R.4    Huttenhower, C.5
  • 19
    • 84942862056 scopus 로고    scopus 로고
    • The path to routine use of genomic biomarkers in the cancer clinic
    • Boutros PC. The path to routine use of genomic biomarkers in the cancer clinic. Genome Res. 2015;25:1508-13.
    • (2015) Genome Res , vol.25 , pp. 1508-1513
    • Boutros, P.C.1
  • 20
    • 84869436774 scopus 로고    scopus 로고
    • Interpreting noncoding genetic variation in complex traits and human disease
    • Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol. 2012;30:1095-106.
    • (2012) Nat Biotechnol , vol.30 , pp. 1095-1106
    • Ward, L.D.1    Kellis, M.2
  • 21
    • 67650021209 scopus 로고    scopus 로고
    • Microbial community profiling for human microbiome projects: tools, techniques, and challenges
    • Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 2009;19:1141-52.
    • (2009) Genome Res , vol.19 , pp. 1141-1152
    • Hamady, M.1    Knight, R.2
  • 22
    • 84901296354 scopus 로고    scopus 로고
    • The mycobiota: interactions between commensal fungi and the host immune system
    • Underhill DM, Iliev ID. The mycobiota: interactions between commensal fungi and the host immune system. Nat Rev Immunol. 2014;14:405-16.
    • (2014) Nat Rev Immunol , vol.14 , pp. 405-416
    • Underhill, D.M.1    Iliev, I.D.2
  • 25
    • 77955963505 scopus 로고    scopus 로고
    • Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed
    • Booijink CC, Boekhorst J, Zoetendal EG, Smidt H, Kleerebezem M, de Vos WM. Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed. Appl Environ Microbiol. 2010;76:5533-40.
    • (2010) Appl Environ Microbiol , vol.76 , pp. 5533-5540
    • Booijink, C.C.1    Boekhorst, J.2    Zoetendal, E.G.3    Smidt, H.4    Kleerebezem, M.5    Vos, W.M.6
  • 26
    • 84893686882 scopus 로고    scopus 로고
    • Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships
    • McHardy IH, Goudarzi M, Tong M, Ruegger PM, Schwager E, Weger JR, et al. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome. 2013;1:17.
    • (2013) Microbiome , vol.1 , pp. 17
    • McHardy, I.H.1    Goudarzi, M.2    Tong, M.3    Ruegger, P.M.4    Schwager, E.5    Weger, J.R.6
  • 27
    • 84977549975 scopus 로고    scopus 로고
    • Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome
    • Grassl N, Kulak NA, Pichler G, Geyer PE, Jung J, Schubert S, et al. Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome. Genome Med. 2016;8:44.
    • (2016) Genome Med , vol.8 , pp. 44
    • Grassl, N.1    Kulak, N.A.2    Pichler, G.3    Geyer, P.E.4    Jung, J.5    Schubert, S.6
  • 28
    • 84907300008 scopus 로고    scopus 로고
    • Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease
    • Palm NW, de Zoete MR, Cullen TW, Barry NA, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014;158:1000-10.
    • (2014) Cell , vol.158 , pp. 1000-1010
    • Palm, N.W.1    Zoete, M.R.2    Cullen, T.W.3    Barry, N.A.4    Stefanowski, J.5    Hao, L.6
  • 30
    • 84927692256 scopus 로고    scopus 로고
    • The first 1000 cultured species of the human gastrointestinal microbiota
    • Rajilic-Stojanovic M, de Vos WM. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol Rev. 2014;38:996-1047.
    • (2014) FEMS Microbiol Rev , vol.38 , pp. 996-1047
    • Rajilic-Stojanovic, M.1    Vos, W.M.2
  • 31
    • 84978969338 scopus 로고    scopus 로고
    • Role and mechanisms of action of Escherichia coli Nissle 1917 in the maintenance of remission in ulcerative colitis patients: an update
    • Scaldaferri F, Gerardi V, Mangiola F, Lopetuso LR, Pizzoferrato M, Petito V, et al. Role and mechanisms of action of Escherichia coli Nissle 1917 in the maintenance of remission in ulcerative colitis patients: an update. World J Gastroenterol. 2016;22:5505-11.
    • (2016) World J Gastroenterol , vol.22 , pp. 5505-5511
    • Scaldaferri, F.1    Gerardi, V.2    Mangiola, F.3    Lopetuso, L.R.4    Pizzoferrato, M.5    Petito, V.6
  • 32
    • 84868225149 scopus 로고    scopus 로고
    • Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes
    • Kaas RS, Friis C, Ussery DW, Aarestrup FM. Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes. BMC Genomics. 2012;13:577.
    • (2012) BMC Genomics , vol.13 , pp. 577
    • Kaas, R.S.1    Friis, C.2    Ussery, D.W.3    Aarestrup, F.M.4
  • 35
    • 84976481496 scopus 로고    scopus 로고
    • Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity
    • Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson BO. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A. 2016;113:E3801-9.
    • (2016) Proc Natl Acad Sci U S A , vol.113 , pp. E3801-E3809
    • Bosi, E.1    Monk, J.M.2    Aziz, R.K.3    Fondi, M.4    Nizet, V.5    Palsson, B.O.6
  • 36
    • 33644659999 scopus 로고    scopus 로고
    • Complete genome sequence of USA300, an epidemic clone of community-acquired methicillin-resistant Staphylococcus aureus
    • Diep BA, Gill SR, Chang RF, Phan TH, Chen JH, Davidson MG, et al. Complete genome sequence of USA300, an epidemic clone of community-acquired methicillin-resistant Staphylococcus aureus. Lancet. 2006;367:731-9.
    • (2006) Lancet , vol.367 , pp. 731-739
    • Diep, B.A.1    Gill, S.R.2    Chang, R.F.3    Phan, T.H.4    Chen, J.H.5    Davidson, M.G.6
  • 37
    • 84995644697 scopus 로고    scopus 로고
    • An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography
    • Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 2016;26:1612-25.
    • (2016) Genome Res , vol.26 , pp. 1612-1625
    • Nayfach, S.1    Rodriguez-Mueller, B.2    Garud, N.3    Pollard, K.S.4
  • 38
    • 84887323708 scopus 로고    scopus 로고
    • Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis
    • Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. Elife. 2013;2:e01202.
    • (2013) Elife , vol.2
    • Scher, J.U.1    Sczesnak, A.2    Longman, R.S.3    Segata, N.4    Ubeda, C.5    Bielski, C.6
  • 39
    • 84925044505 scopus 로고    scopus 로고
    • Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences
    • Eren AM, Morrison HG, Lescault PJ, Reveillaud J, Vineis JH, Sogin ML. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 2015;9:968-79.
    • (2015) ISME J , vol.9 , pp. 968-979
    • Eren, A.M.1    Morrison, H.G.2    Lescault, P.J.3    Reveillaud, J.4    Vineis, J.H.5    Sogin, M.L.6
  • 40
    • 84923545661 scopus 로고    scopus 로고
    • Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution
    • Tikhonov M, Leach RW, Wingreen NS. Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution. ISME J. 2015;9:68-80.
    • (2015) ISME J , vol.9 , pp. 68-80
    • Tikhonov, M.1    Leach, R.W.2    Wingreen, N.S.3
  • 44
    • 85031129219 scopus 로고    scopus 로고
    • UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing
    • Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. 2016; doi: https://doi.org/10.1101/081257.
    • (2016) bioRxiv.
    • Edgar, R.C.1
  • 47
    • 84947487970 scopus 로고    scopus 로고
    • Twenty years of bacterial genome sequencing
    • Loman NJ, Pallen MJ. Twenty years of bacterial genome sequencing. Nat Rev Microbiol. 2015;13:787-94.
    • (2015) Nat Rev Microbiol , vol.13 , pp. 787-794
    • Loman, N.J.1    Pallen, M.J.2
  • 49
    • 84858990515 scopus 로고    scopus 로고
    • Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes
    • Giannoukos G, Ciulla DM, Huang K, Haas BJ, Izard J, Levin JZ, et al. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 2012;13:R23.
    • (2012) Genome Biol , vol.13 , pp. R23
    • Giannoukos, G.1    Ciulla, D.M.2    Huang, K.3    Haas, B.J.4    Izard, J.5    Levin, J.Z.6
  • 50
    • 84886725824 scopus 로고    scopus 로고
    • Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses
    • Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061-8.
    • (2013) ISME J , vol.7 , pp. 2061-2068
    • Blazewicz, S.J.1    Barnard, R.L.2    Daly, R.A.3    Firestone, M.K.4
  • 53
    • 84901363655 scopus 로고    scopus 로고
    • Waste not, want not: why rarefying microbiome data is inadmissible
    • McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10:e1003531.
    • (2014) PLoS Comput Biol , vol.10
    • McMurdie, P.J.1    Holmes, S.2
  • 54
    • 84988864262 scopus 로고    scopus 로고
    • Revised estimates for the number of human and bacteria cells in the body
    • Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 2016;14:e1002533.
    • (2016) PLoS Biol , vol.14
    • Sender, R.1    Fuchs, S.2    Milo, R.3
  • 55
    • 0018842022 scopus 로고
    • The microbial contribution to human faecal mass
    • Stephen AM, Cummings JH. The microbial contribution to human faecal mass. J Med Microbiol. 1980;13:45-56.
    • (1980) J Med Microbiol , vol.13 , pp. 45-56
    • Stephen, A.M.1    Cummings, J.H.2
  • 56
    • 84862286169 scopus 로고    scopus 로고
    • A framework for human microbiome research
    • Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486:215-21.
    • (2012) Nature , vol.486 , pp. 215-221
  • 57
    • 84938484290 scopus 로고    scopus 로고
    • 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles
    • Hang J, Desai V, Zavaljevski N, Yang Y, Lin X, Satya RV, et al. 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles. Microbiome. 2014;2:31.
    • (2014) Microbiome , vol.2 , pp. 31
    • Hang, J.1    Desai, V.2    Zavaljevski, N.3    Yang, Y.4    Lin, X.5    Satya, R.V.6
  • 58
  • 59
    • 84863920287 scopus 로고    scopus 로고
    • Microbial interactions: from networks to models
    • Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538-50.
    • (2012) Nat Rev Microbiol , vol.10 , pp. 538-550
    • Faust, K.1    Raes, J.2
  • 60
    • 84888586768 scopus 로고    scopus 로고
    • The genotypic view of social interactions in microbial communities
    • Mitri S, Foster KR. The genotypic view of social interactions in microbial communities. Annu Rev Genet. 2013;47:247-73.
    • (2013) Annu Rev Genet , vol.47 , pp. 247-273
    • Mitri, S.1    Foster, K.R.2
  • 61
    • 84928807017 scopus 로고    scopus 로고
    • Unraveling interactions in microbial communities-from co-cultures to microbiomes
    • Tan J, Zuniga C, Zengler K. Unraveling interactions in microbial communities-from co-cultures to microbiomes. J Microbiol. 2015;53:295-305.
    • (2015) J Microbiol , vol.53 , pp. 295-305
    • Tan, J.1    Zuniga, C.2    Zengler, K.3
  • 62
    • 84979986238 scopus 로고    scopus 로고
    • A synthetic ecology perspective: how well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?
    • Yu Z, Krause SM, Beck DA, Chistoserdova L. A synthetic ecology perspective: how well does behavior of model organisms in the laboratory predict microbial activities in natural habitats? Front Microbiol. 2016;7:946.
    • (2016) Front Microbiol , vol.7 , pp. 946
    • Yu, Z.1    Krause, S.M.2    Beck, D.A.3    Chistoserdova, L.4
  • 63
    • 84996599775 scopus 로고    scopus 로고
    • Compositional data analysis of the microbiome: fundamentals, tools, and challenges
    • Tsilimigras MC, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol. 2016;26:330-5.
    • (2016) Ann Epidemiol , vol.26 , pp. 330-335
    • Tsilimigras, M.C.1    Fodor, A.A.2
  • 65
    • 84865733148 scopus 로고    scopus 로고
    • Inferring correlation networks from genomic survey data
    • Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8:e1002687.
    • (2012) PLoS Comput Biol , vol.8
    • Friedman, J.1    Alm, E.J.2
  • 66
    • 84943379443 scopus 로고    scopus 로고
    • CCLasso: correlation inference for compositional data through Lasso
    • Fang H, Huang C, Zhao H, Deng M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics. 2015;31:3172-80.
    • (2015) Bioinformatics , vol.31 , pp. 3172-3180
    • Fang, H.1    Huang, C.2    Zhao, H.3    Deng, M.4
  • 68
    • 84955613013 scopus 로고    scopus 로고
    • Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production
    • Wu GD, Compher C, Chen EZ, Smith SA, Shah RD, Bittinger K, et al. Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production. Gut. 2016;65:63-72.
    • (2016) Gut , vol.65 , pp. 63-72
    • Wu, G.D.1    Compher, C.2    Chen, E.Z.3    Smith, S.A.4    Shah, R.D.5    Bittinger, K.6
  • 70
    • 85018464842 scopus 로고    scopus 로고
    • Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort
    • Org E, Blum Y, Kasela S, Mehrabian M, Kuusisto J, Kangas AJ, et al. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol. 2017;18:70.
    • (2017) Genome Biol , vol.18 , pp. 70
    • Org, E.1    Blum, Y.2    Kasela, S.3    Mehrabian, M.4    Kuusisto, J.5    Kangas, A.J.6
  • 73
    • 85020054792 scopus 로고    scopus 로고
    • Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies
    • Thorsen J, Brejnrod A, Mortensen M, Rasmussen MA, Stokholm J, Al-Soud WA, et al. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies. Microbiome. 2016;4:62.
    • (2016) Microbiome , vol.4 , pp. 62
    • Thorsen, J.1    Brejnrod, A.2    Mortensen, M.3    Rasmussen, M.A.4    Stokholm, J.5    Al-Soud, W.A.6
  • 74
    • 84888865593 scopus 로고    scopus 로고
    • Differential abundance analysis for microbial marker-gene surveys
    • Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200-2.
    • (2013) Nat Methods , vol.10 , pp. 1200-1202
    • Paulson, J.N.1    Stine, O.C.2    Bravo, H.C.3    Pop, M.4
  • 75
    • 84866549438 scopus 로고    scopus 로고
    • Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment
    • Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13:R79.
    • (2012) Genome Biol , vol.13 , pp. R79
    • Morgan, X.C.1    Tickle, T.L.2    Sokol, H.3    Gevers, D.4    Devaney, K.L.5    Ward, D.V.6
  • 76
    • 75249087100 scopus 로고    scopus 로고
    • edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
    • Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139-40.
    • (2010) Bioinformatics , vol.26 , pp. 139-140
    • Robinson, M.D.1    McCarthy, D.J.2    Smyth, G.K.3
  • 77
    • 84924629414 scopus 로고    scopus 로고
    • Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
    • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    • (2014) Genome Biol , vol.15 , pp. 550
    • Love, M.I.1    Huber, W.2    Anders, S.3
  • 78
    • 84896735766 scopus 로고    scopus 로고
    • voom: precision weights unlock linear model analysis tools for RNA-seq read counts
    • Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.
    • (2014) Genome Biol , vol.15 , pp. R29
    • Law, C.W.1    Chen, Y.2    Shi, W.3    Smyth, G.K.4
  • 79
    • 84955314492 scopus 로고    scopus 로고
    • Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics
    • Jonsson V, Osterlund T, Nerman O, Kristiansson E. Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genomics. 2016;17:78.
    • (2016) BMC Genomics , vol.17 , pp. 78
    • Jonsson, V.1    Osterlund, T.2    Nerman, O.3    Kristiansson, E.4
  • 81
    • 66249145772 scopus 로고    scopus 로고
    • Statistical methods for detecting differentially abundant features in clinical metagenomic samples
    • White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol. 2009;5:e1000352.
    • (2009) PLoS Comput Biol , vol.5
    • White, J.R.1    Nagarajan, N.2    Pop, M.3
  • 83
    • 77953176036 scopus 로고    scopus 로고
    • A scaling normalization method for differential expression analysis of RNA-seq data
    • Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
    • (2010) Genome Biol , vol.11 , pp. R25
    • Robinson, M.D.1    Oshlack, A.2
  • 84
    • 77958471357 scopus 로고    scopus 로고
    • Differential expression analysis for sequence count data
    • Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
    • (2010) Genome Biol , vol.11 , pp. R106
    • Anders, S.1    Huber, W.2
  • 85
    • 84991010752 scopus 로고    scopus 로고
    • A two-part mixed-effects model for analyzing longitudinal microbiome compositional data
    • Chen EZ, Li H. A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics. 2016;32:2611-7.
    • (2016) Bioinformatics , vol.32 , pp. 2611-2617
    • Chen, E.Z.1    Li, H.2
  • 87
    • 84975223360 scopus 로고    scopus 로고
    • MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
    • Bucci V, Tzen B, Li N, Simmons M, Tanoue T, Bogart E, et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 2016;17:121.
    • (2016) Genome Biol , vol.17 , pp. 121
    • Bucci, V.1    Tzen, B.2    Li, N.3    Simmons, M.4    Tanoue, T.5    Bogart, E.6
  • 89
    • 84873510063 scopus 로고    scopus 로고
    • A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets
    • Koren O, Knights D, Gonzalez A, Waldron L, Segata N, Knight R, et al. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput Biol. 2013;9:e1002863.
    • (2013) PLoS Comput Biol , vol.9
    • Koren, O.1    Knights, D.2    Gonzalez, A.3    Waldron, L.4    Segata, N.5    Knight, R.6
  • 91
    • 84979971115 scopus 로고    scopus 로고
    • Machine learning meta-analysis of large metagenomic datasets: tools and biological insights
    • Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput Biol. 2016;12:e1004977.
    • (2016) PLoS Comput Biol , vol.12
    • Pasolli, E.1    Truong, D.T.2    Malik, F.3    Waldron, L.4    Segata, N.5
  • 92
    • 0002154022 scopus 로고    scopus 로고
    • A new method for non parametric multivariate analysis of variance
    • Anderson MJ. A new method for non parametric multivariate analysis of variance. Austral Ecol. 2001;26:32-46.
    • (2001) Austral Ecol , vol.26 , pp. 32-46
    • Anderson, M.J.1
  • 93
    • 84929159912 scopus 로고    scopus 로고
    • Testing in microbiome-profiling studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test
    • Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, et al. Testing in microbiome-profiling studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test. Am J Hum Genet. 2015;96:797-807.
    • (2015) Am J Hum Genet , vol.96 , pp. 797-807
    • Zhao, N.1    Chen, J.2    Carroll, I.M.3    Ringel-Kulka, T.4    Epstein, M.P.5    Zhou, H.6
  • 94
    • 0027804103 scopus 로고
    • Non parametric multivariate analyses of changes in community structure
    • Clarke KR. Non parametric multivariate analyses of changes in community structure. Aust J Ecol. 1993;18:117-43.
    • (1993) Aust J Ecol , vol.18 , pp. 117-143
    • Clarke, K.R.1
  • 95
    • 84990989968 scopus 로고    scopus 로고
    • PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances
    • Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics. 2016;32:2618-25.
    • (2016) Bioinformatics , vol.32 , pp. 2618-2625
    • Tang, Z.Z.1    Chen, G.2    Alekseyenko, A.V.3
  • 96
    • 84996424394 scopus 로고    scopus 로고
    • Kernel-Penalized regression for analysis of microbiome data
    • arXiv
    • Randolph TW, Zhao S, Copeland W, Hullar M, Shojaie A. Kernel-Penalized regression for analysis of microbiome data. arXiv 2015;arXiv:151100297.
    • (2015) arXiv , pp. 151100297
    • Randolph, T.W.1    Zhao, S.2    Copeland, W.3    Hullar, M.4    Shojaie, A.5
  • 97
    • 85016418735 scopus 로고    scopus 로고
    • Variability in metagenomic count data and its influence on the identification of differentially abundant genes
    • Jonsson V, Osterlund T, Nerman O, Kristiansson E. Variability in metagenomic count data and its influence on the identification of differentially abundant genes. J Comput Biol. 2017;24:311-26.
    • (2017) J Comput Biol , vol.24 , pp. 311-326
    • Jonsson, V.1    Osterlund, T.2    Nerman, O.3    Kristiansson, E.4
  • 98
    • 85015975393 scopus 로고    scopus 로고
    • Normalization and microbial differential abundance strategies depend upon data characteristics
    • Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.
    • (2017) Microbiome , vol.5 , pp. 27
    • Weiss, S.1    Xu, Z.Z.2    Peddada, S.3    Amir, A.4    Bittinger, K.5    Gonzalez, A.6
  • 100
    • 85021708596 scopus 로고    scopus 로고
    • Multivariate linear regression
    • Olive DJ, editor, Cham: Springer
    • Olive DJ. Multivariate linear regression. In: Olive DJ, editor. Linear regression. Cham: Springer; 2017. p. 343-87.
    • (2017) Linear regression , pp. 343-387
    • Olive, D.J.1
  • 101
    • 84872527720 scopus 로고    scopus 로고
    • Multivariate or multivariable regression?
    • Hidalgo B, Goodman M. Multivariate or multivariable regression? Am J Public Health. 2013;103:39-40.
    • (2013) Am J Public Health , vol.103 , pp. 39-40
    • Hidalgo, B.1    Goodman, M.2
  • 102
    • 84878103123 scopus 로고    scopus 로고
    • Achieving consensus on terminology describing multivariable analyses
    • Tsai AC. Achieving consensus on terminology describing multivariable analyses. Am J Public Health. 2013;103:e1.
    • (2013) Am J Public Health , vol.103
    • Tsai, A.C.1
  • 103
    • 84964048491 scopus 로고    scopus 로고
    • Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis
    • Fang R, Wagner BD, Harris JK, Fillon SA. Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis. Epidemiol Infect. 2016;144:2447-55.
    • (2016) Epidemiol Infect , vol.144 , pp. 2447-2455
    • Fang, R.1    Wagner, B.D.2    Harris, J.K.3    Fillon, S.A.4
  • 104
    • 84996587632 scopus 로고    scopus 로고
    • Challenges for case-control studies with microbiome data
    • Brooks JP. Challenges for case-control studies with microbiome data. Ann Epidemiol. 2016;26:336-41.
    • (2016) Ann Epidemiol , vol.26 , pp. 336-341
    • Brooks, J.P.1
  • 107
    • 33845432928 scopus 로고    scopus 로고
    • Adjusting batch effects in microarray expression data using empirical Bayes methods
    • Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118-27.
    • (2007) Biostatistics , vol.8 , pp. 118-127
    • Johnson, W.E.1    Li, C.2    Rabinovic, A.3
  • 109
    • 84938484417 scopus 로고    scopus 로고
    • The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women
    • Romero R, Hassan SS, Gajer P, Tarca AL, Fadrosh DW, Nikita L, et al. The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome. 2014;2:4.
    • (2014) Microbiome , vol.2 , pp. 4
    • Romero, R.1    Hassan, S.S.2    Gajer, P.3    Tarca, A.L.4    Fadrosh, D.W.5    Nikita, L.6
  • 110
    • 85019108860 scopus 로고    scopus 로고
    • Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases
    • Ananthakrishnan AN, Luo C, Yajnik V, Khalili H, Garber JJ, Stevens BW, et al. Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases. Cell Host Microbe. 2017;21:603-10.
    • (2017) Cell Host Microbe , vol.21 , pp. 603-610
    • Ananthakrishnan, A.N.1    Luo, C.2    Yajnik, V.3    Khalili, H.4    Garber, J.J.5    Stevens, B.W.6
  • 111
    • 84880439384 scopus 로고    scopus 로고
    • Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta
    • Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP, Turnbaugh PJ. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science. 2013;341:295-8.
    • (2013) Science , vol.341 , pp. 295-298
    • Haiser, H.J.1    Gootenberg, D.B.2    Chatman, K.3    Sirasani, G.4    Balskus, E.P.5    Turnbaugh, P.J.6
  • 112
    • 0020527551 scopus 로고
    • Digoxin-inactivating bacteria: identification in human gut flora
    • Saha JR, Butler Jr VP, Neu HC, Lindenbaum J. Digoxin-inactivating bacteria: identification in human gut flora. Science. 1983;220:325-7.
    • (1983) Science , vol.220 , pp. 325-327
    • Saha, J.R.1    Butler, V.P.2    Neu, H.C.3    Lindenbaum, J.4
  • 113
    • 84925500413 scopus 로고    scopus 로고
    • Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile
    • Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature. 2015;517:205-8.
    • (2015) Nature , vol.517 , pp. 205-208
    • Buffie, C.G.1    Bucci, V.2    Stein, R.R.3    McKenney, P.T.4    Ling, L.5    Gobourne, A.6
  • 115
    • 84990988075 scopus 로고    scopus 로고
    • Writ large: genomic dissection of the effect of cellular environment on immune response
    • Yosef N, Regev A. Writ large: genomic dissection of the effect of cellular environment on immune response. Science. 2016;354:64-8.
    • (2016) Science , vol.354 , pp. 64-68
    • Yosef, N.1    Regev, A.2
  • 118
    • 84863743669 scopus 로고    scopus 로고
    • Birth weight in newborn infants with different diabetes-associated HLA genotypes in three neighbouring countries: Finland, Estonia and Russian Karelia
    • Peet A, Kool P, Ilonen J, Knip M, Tillmann V, Group DS. Birth weight in newborn infants with different diabetes-associated HLA genotypes in three neighbouring countries: Finland, Estonia and Russian Karelia. Diabetes Metab Res Rev. 2012;28:455-61.
    • (2012) Diabetes Metab Res Rev , vol.28 , pp. 455-461
    • Peet, A.1    Kool, P.2    Ilonen, J.3    Knip, M.4    Tillmann, V.5    Group, D.S.6
  • 119
    • 84988565996 scopus 로고    scopus 로고
    • Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering
    • Franzen O, Hu J, Bao X, Itzkowitz SH, Peter I, Bashir A. Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering. Microbiome. 2015;3:43.
    • (2015) Microbiome , vol.3 , pp. 43
    • Franzen, O.1    Hu, J.2    Bao, X.3    Itzkowitz, S.H.4    Peter, I.5    Bashir, A.6
  • 121
    • 84899792426 scopus 로고    scopus 로고
    • Strain/species identification in metagenomes using genome-specific markers
    • Tu Q, He Z, Zhou J. Strain/species identification in metagenomes using genome-specific markers. Nucleic Acids Res. 2014;42:e67.
    • (2014) Nucleic Acids Res , vol.42
    • Tu, Q.1    He, Z.2    Zhou, J.3
  • 122
    • 84934275888 scopus 로고    scopus 로고
    • Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data
    • Sahl JW, Schupp JM, Rasko DA, Colman RE, Foster JT, Keim P. Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data. Genome Med. 2015;7:52.
    • (2015) Genome Med , vol.7 , pp. 52
    • Sahl, J.W.1    Schupp, J.M.2    Rasko, D.A.3    Colman, R.E.4    Foster, J.T.5    Keim, P.6
  • 123
    • 84928981302 scopus 로고    scopus 로고
    • Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance
    • Ahn TH, Chai J, Pan C. Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance. Bioinformatics. 2015;31:170-7.
    • (2015) Bioinformatics , vol.31 , pp. 170-177
    • Ahn, T.H.1    Chai, J.2    Pan, C.3
  • 124
    • 84885070139 scopus 로고    scopus 로고
    • Pathoscope: species identification and strain attribution with unassembled sequencing data
    • Francis OE, Bendall M, Manimaran S, Hong C, Clement NL, Castro-Nallar E, et al. Pathoscope: species identification and strain attribution with unassembled sequencing data. Genome Res. 2013;23:1721-9.
    • (2013) Genome Res , vol.23 , pp. 1721-1729
    • Francis, O.E.1    Bendall, M.2    Manimaran, S.3    Hong, C.4    Clement, N.L.5    Castro-Nallar, E.6
  • 125
    • 84943595976 scopus 로고    scopus 로고
    • Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning
    • Cleary B, Brito IL, Huang K, Gevers D, Shea T, Young S, Alm EJ. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat Biotechnol. 2015;33:1053-60.
    • (2015) Nat Biotechnol , vol.33 , pp. 1053-1060
    • Cleary, B.1    Brito, I.L.2    Huang, K.3    Gevers, D.4    Shea, T.5    Young, S.6    Alm, E.J.7
  • 126
    • 84922735613 scopus 로고    scopus 로고
    • Extensive strain-level copy-number variation across human gut microbiome species
    • Greenblum S, Carr R, Borenstein E. Extensive strain-level copy-number variation across human gut microbiome species. Cell. 2015;160:583-94.
    • (2015) Cell , vol.160 , pp. 583-594
    • Greenblum, S.1    Carr, R.2    Borenstein, E.3
  • 128
    • 84946031490 scopus 로고    scopus 로고
    • Analysis of RNA-Seq data using TopHat and Cufflinks
    • Ghosh S, Chan CK. Analysis of RNA-Seq data using TopHat and Cufflinks. Methods Mol Biol. 2016;1374:339-61.
    • (2016) Methods Mol Biol , vol.1374 , pp. 339-361
    • Ghosh, S.1    Chan, C.K.2
  • 129
    • 85006345107 scopus 로고    scopus 로고
    • IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses
    • Narayanasamy S, Jarosz Y, Muller EE, Heintz-Buschart A, Herold M, Kaysen A, et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 2016;17:260.
    • (2016) Genome Biol , vol.17 , pp. 260
    • Narayanasamy, S.1    Jarosz, Y.2    Muller, E.E.3    Heintz-Buschart, A.4    Herold, M.5    Kaysen, A.6
  • 131
    • 84981308664 scopus 로고    scopus 로고
    • COMAN: a web server for comprehensive metatranscriptomics analysis
    • Ni Y, Li J, Panagiotou G. COMAN: a web server for comprehensive metatranscriptomics analysis. BMC Genomics. 2016;17:622.
    • (2016) BMC Genomics , vol.17 , pp. 622
    • Ni, Y.1    Li, J.2    Panagiotou, G.3
  • 132
    • 84880127893 scopus 로고    scopus 로고
    • IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology
    • Leung HC, Yiu SM, Parkinson J, Chin FY. IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology. J Comput Biol. 2013;20:540-50.
    • (2013) J Comput Biol , vol.20 , pp. 540-550
    • Leung, H.C.1    Yiu, S.M.2    Parkinson, J.3    Chin, F.Y.4
  • 133
    • 84859768479 scopus 로고    scopus 로고
    • Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels
    • Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086-92.
    • (2012) Bioinformatics , vol.28 , pp. 1086-1092
    • Schulz, M.H.1    Zerbino, D.R.2    Vingron, M.3    Birney, E.4
  • 134
    • 84955476032 scopus 로고    scopus 로고
    • COGNIZER: a framework for functional annotation of metagenomic datasets
    • Bose T, Haque MM, Reddy C, Mande SS. COGNIZER: a framework for functional annotation of metagenomic datasets. PLoS One. 2015;10:e0142102.
    • (2015) PLoS One , vol.10
    • Bose, T.1    Haque, M.M.2    Reddy, C.3    Mande, S.S.4
  • 135
    • 84990828229 scopus 로고    scopus 로고
    • FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies
    • Kim J, Kim MS, Koh AY, Xie Y, Zhan X. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics. 2016;17:420.
    • (2016) BMC Bioinformatics , vol.17 , pp. 420
    • Kim, J.1    Kim, M.S.2    Koh, A.Y.3    Xie, Y.4    Zhan, X.5
  • 136
    • 84978910824 scopus 로고    scopus 로고
    • MEGAN community edition-interactive exploration and analysis of large-scale microbiome sequencing data
    • Huson DH, Beier S, Flade I, Gorska A, El-Hadidi M, Mitra S, et al. MEGAN community edition-interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:e1004957.
    • (2016) PLoS Comput Biol , vol.12
    • Huson, D.H.1    Beier, S.2    Flade, I.3    Gorska, A.4    El-Hadidi, M.5    Mitra, S.6
  • 138
    • 84938612863 scopus 로고    scopus 로고
    • Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease
    • Morgan XC, Kabakchiev B, Waldron L, Tyler AD, Tickle TL, Milgrom R, et al. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biol. 2015;16:67.
    • (2015) Genome Biol , vol.16 , pp. 67
    • Morgan, X.C.1    Kabakchiev, B.2    Waldron, L.3    Tyler, A.D.4    Tickle, T.L.5    Milgrom, R.6
  • 139
    • 84946878516 scopus 로고    scopus 로고
    • Investigating microbial co-occurrence patterns based on metagenomic compositional data
    • Ban Y, An L, Jiang H. Investigating microbial co-occurrence patterns based on metagenomic compositional data. Bioinformatics. 2015;31:3322-9.
    • (2015) Bioinformatics , vol.31 , pp. 3322-3329
    • Ban, Y.1    An, L.2    Jiang, H.3
  • 142
    • 84997173322 scopus 로고    scopus 로고
    • MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles
    • Shaw GT, Pao YY, Wang D. MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles. BMC Bioinformatics. 2016;17:488.
    • (2016) BMC Bioinformatics , vol.17 , pp. 488
    • Shaw, G.T.1    Pao, Y.Y.2    Wang, D.3
  • 144
    • 84912066103 scopus 로고    scopus 로고
    • BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communities
    • Shafiei M, Dunn KA, Chipman H, Gu H, Bielawski JP. BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communities. PLoS Comput Biol. 2014;10:e1003918.
    • (2014) PLoS Comput Biol , vol.10
    • Shafiei, M.1    Dunn, K.A.2    Chipman, H.3    Gu, H.4    Bielawski, J.P.5


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