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Experimental design and quantitative analysis of microbial community multiomics

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Mallick, Himel, Ma, Siyuan, Franzosa, Eric A., Vatanen, Tommi, Morgan, Xochitl C., Huttenhower, Curtis·2017·Genome Biology
DOI 10.1186/s13059-017-1359-zPMC PMC5708111
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Abstract

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.

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  • Recent efforts have been made to consolidate and standardize sequencing and analysis protocols to improve overall quality and compatibility of microbiome research.

    “Recent efforts have been made to consolidate and standardize sequencing and analysis protocols, to improve overall quality and compatibility of research in the microbiome field (Knight et al., 2018;Mallick et al., 2017;Pollock et al., 2018).”

    From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Mallick et al., 2017

    The section “Integration of studies needs to address confounding effects that are unique to microbiome data” (page unavailable) explains that differing sequencing and analysis protocols create study-specific biases and describes ways to improve cross-study comparability. But it says efforts to address these problems have been limited and calls for further protocols; it does not establish that sequencing and analysis protocols have recently been consolidated and standardized. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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  1. [1]Kostic AD Gevers D Siljander H Vatanen T Hyotylainen T Hamalainen AM The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes Cell Host Microbe 2015 17 260 73 10.1016/j.chom.2015.01.001 25662751 PMC4689191 · DOI
  2. [2]Yatsunenko T Rey FE Manary MJ Trehan I Dominguez-Bello MG Contreras M Human gut microbiome viewed across age and geography Nature 2012 486 222 7 22699611 PMC3376388
  3. [3]Costello EK Lauber CL Hamady M Fierer N Gordon JI Knight R Bacterial community variation in human body habitats across space and time Science 2009 326 1694 7 10.1126/science.1177486 19892944 PMC3602444 · DOI · Paper page
  4. [4]Luo C Knight R Siljander H Knip M Xavier RJ Gevers D ConStrains identifies microbial strains in metagenomic datasets Nat Biotechnol 2015 33 1045 52 10.1038/nbt.3319 26344404 PMC4676274 · DOI
  5. [5]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 10.1101/gr.216242.116 28167665 PMC5378180 · DOI · Paper page
  6. [6]Scholz M Ward DV Pasolli E Tolio T Zolfo M Asnicar F Strain-level microbial epidemiology and population genomics from shotgun metagenomics Nat Methods 2016 13 435 8 10.1038/nmeth.3802 26999001 · DOI
  7. [7]Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome Nature 2012 486 207 14 10.1038/nature11234 22699609 PMC3564958 · DOI · Paper page
  8. [8]Qin J Li R Raes J Arumugam M Burgdorf KS Manichanh C A human gut microbial gene catalogue established by metagenomic sequencing Nature 2010 464 59 65 10.1038/nature08821 20203603 PMC3779803 · DOI · Paper page
  9. [9]Abubucker S Segata N Goll J Schubert AM Izard J Cantarel BL Metabolic reconstruction for metagenomic data and its application to the human microbiome PLoS Comput Biol 2012 8 e1002358 10.1371/journal.pcbi.1002358 22719234 PMC3374609 · DOI
  10. [10]Verberkmoes NC Russell AL Shah M Godzik A Rosenquist M Halfvarson J Shotgun metaproteomics of the human distal gut microbiota ISME J 2009 3 179 89 10.1038/ismej.2008.108 18971961 · DOI
  11. [11]Zeevi D Korem T Zmora N Israeli D Rothschild D Weinberger A Personalized nutrition by prediction of glycemic responses Cell 2015 163 1079 94 10.1016/j.cell.2015.11.001 26590418 · DOI · Paper page
  12. [13]Schirmer M Smeekens SP Vlamakis H Jaeger M Oosting M Franzosa EA Linking the human gut microbiome to inflammatory cytokine production capacity Cell 2016 167 1125 36 10.1016/j.cell.2016.10.020 27814509 PMC5131922 · DOI
  13. [14]Zhernakova A Kurilshikov A Bonder MJ Tigchelaar EF Schirmer M Vatanen T Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity Science 2016 352 565 9 10.1126/science.aad3369 27126040 PMC5240844 · DOI · Paper page
  14. [15]Rooks MG Garrett WS Gut microbiota, metabolites and host immunity Nat Rev Immunol 2016 16 341 52 10.1038/nri.2016.42 27231050 PMC5541232 · DOI · Paper page
  15. [16]Vatanen T Kostic AD d'Hennezel E Siljander H Franzosa EA Yassour M Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans Cell 2016 165 842 53 10.1016/j.cell.2016.04.007 27133167 PMC4950857 · DOI · Paper page
  16. [17]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 10.1186/s13059-015-0841-8 26653756 PMC4674991 · DOI
  17. [19]Boutros PC The path to routine use of genomic biomarkers in the cancer clinic Genome Res 2015 25 1508 13 10.1101/gr.191114.115 26430161 PMC4579336 · DOI
  18. [20]Ward LD Kellis M Interpreting noncoding genetic variation in complex traits and human disease Nat Biotechnol 2012 30 1095 106 10.1038/nbt.2422 23138309 PMC3703467 · DOI · Paper page
  19. [21]Hamady M Knight R Microbial community profiling for human microbiome projects: tools, techniques, and challenges Genome Res 2009 19 1141 52 10.1101/gr.085464.108 19383763 PMC3776646 · DOI
  20. [22]Underhill DM Iliev ID The mycobiota: interactions between commensal fungi and the host immune system Nat Rev Immunol 2014 14 405 16 10.1038/nri3684 24854590 PMC4332855 · DOI
  21. [23]Brooks JP Edwards DJ Harwich MD Jr Rivera MC Fettweis JM Serrano MG The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies BMC Microbiol 2015 15 66 10.1186/s12866-015-0351-6 25880246 PMC4433096 · DOI · Paper page
  22. [24]Franzosa EA Morgan XC Segata N Waldron L Reyes J Earl AM Relating the metatranscriptome and metagenome of the human gut Proc Natl Acad Sci U S A 2014 111 E2329 38 10.1073/pnas.1319284111 24843156 PMC4050606 · DOI
  23. [25]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 10.1128/AEM.00502-10 20562280 PMC2918960 · DOI
  24. [26]McHardy IH Goudarzi M Tong M Ruegger PM Schwager E Weger JR Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships Microbiome 2013 1 17 10.1186/2049-2618-1-17 24450808 PMC3971612 · DOI
  25. [27]Grassl N Kulak NA Pichler G Geyer PE Jung J Schubert S Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome Genome Med 2016 8 44 10.1186/s13073-016-0293-0 27102203 PMC4841045 · DOI
  26. [28]Palm NW de Zoete MR Cullen TW Barry NA Stefanowski J Hao L Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease Cell 2014 158 1000 10 10.1016/j.cell.2014.08.006 25171403 PMC4174347 · DOI · Paper page
  27. [29]Geva-Zatorsky N Sefik E Kua L Pasman L Tan TG Ortiz-Lopez A Mining the human gut microbiota for immunomodulatory organisms Cell 2017 168 928 43 10.1016/j.cell.2017.01.022 28215708 PMC7774263 · DOI · Paper page
  28. [30]Rajilic-Stojanovic M de Vos WM The first 1000 cultured species of the human gastrointestinal microbiota FEMS Microbiol Rev 2014 38 996 1047 10.1111/1574-6976.12075 24861948 PMC4262072 · DOI · Paper page
  29. [31]Scaldaferri F Gerardi V Mangiola F Lopetuso LR Pizzoferrato M Petito V 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 10.3748/wjg.v22.i24.5505 27350728 PMC4917610 · DOI
  30. [32]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 10.1186/1471-2164-13-577 23114024 PMC3575317 · DOI
  31. [33]Salipante SJ Roach DJ Kitzman JO Snyder MW Stackhouse B Butler-Wu SM Large-scale genomic sequencing of extraintestinal pathogenic Escherichia coli strains Genome Res 2015 25 119 28 10.1101/gr.180190.114 25373147 PMC4317167 · DOI
  32. [34]Dobrindt U Chowdary MG Krumbholz G Hacker J Genome dynamics and its impact on evolution of Escherichia coli Med Microbiol Immunol 2010 199 145 54 10.1007/s00430-010-0161-2 20445988 · DOI
  33. [36]Diep BA Gill SR Chang RF Phan TH Chen JH Davidson MG Complete genome sequence of USA300, an epidemic clone of community-acquired methicillin-resistant Staphylococcus aureus Lancet 2006 367 731 9 10.1016/S0140-6736(06)68231-7 16517273 · DOI · Paper page
  34. [38]Scher JU Sczesnak A Longman RS Segata N Ubeda C Bielski C Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis Elife 2013 2 e01202 10.7554/eLife.01202 24192039 PMC3816614 · DOI · Paper page
  35. [39]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 10.1038/ismej.2014.195 25325381 PMC4817710 · DOI
  36. [40]Tikhonov M Leach RW Wingreen NS Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution ISME J 2015 9 68 80 10.1038/ismej.2014.117 25012900 PMC4274427 · DOI
  37. [41]Faith JJ Guruge JL Charbonneau M Subramanian S Seedorf H Goodman AL The long-term stability of the human gut microbiota Science 2013 341 1237439 10.1126/science.1237439 23828941 PMC3791589 · DOI · Paper page
  38. [42]Eren AM Borisy GG Huse SM Mark Welch JL Oligotyping analysis of the human oral microbiome Proc Natl Acad Sci U S A 2014 111 E2875 84 10.1073/pnas.1409644111 24965363 PMC4104879 · DOI · Paper page
  39. [43]Callahan BJ McMurdie PJ Rosen MJ Han AW Johnson AJ Holmes SP DADA2: high-resolution sample inference from Illumina amplicon data Nat Methods 2016 13 581 3 10.1038/nmeth.3869 27214047 PMC4927377 · DOI · Paper page
  40. [44]Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. 2016; doi: https://doi.org/10.1101/081257. · DOI
  41. [46]McInerney JO McNally A O'Connell MJ Why prokaryotes have pangenomes Nat Microbiol 2017 2 17040 10.1038/nmicrobiol.2017.40 28350002 · DOI
  42. [47]Loman NJ Pallen MJ Twenty years of bacterial genome sequencing Nat Rev Microbiol 2015 13 787 94 10.1038/nrmicro3565 26548914 · DOI
  43. [48]Konstantinidis KT Ramette A Tiedje JM The bacterial species definition in the genomic era Philos Trans R Soc Lond B Biol Sci 2006 361 1929 40 10.1098/rstb.2006.1920 17062412 PMC1764935 · DOI
  44. [49]Giannoukos G Ciulla DM Huang K Haas BJ Izard J Levin JZ Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes Genome Biol 2012 13 R23 10.1186/gb-2012-13-3-r23 22455878 PMC3439974 · DOI
  45. [50]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 10.1038/ismej.2013.102 23823491 PMC3806256 · DOI · Paper page
  46. [51]Franzosa EA Hsu T Sirota-Madi A Shafquat A Abu-Ali G Morgan XC Huttenhower C Sequencing and beyond: integrating molecular 'omics' for microbial community profiling Nat Rev Microbiol 2015 13 360 72 10.1038/nrmicro3451 25915636 PMC4800835 · DOI
  47. [53]McMurdie PJ Holmes S Waste not, want not: why rarefying microbiome data is inadmissible PLoS Comput Biol 2014 10 e1003531 10.1371/journal.pcbi.1003531 24699258 PMC3974642 · DOI
  48. [55]Stephen AM Cummings JH The microbial contribution to human faecal mass J Med Microbiol 1980 13 45 56 10.1099/00222615-13-1-45 7359576 · DOI · Paper page
  49. [56]Human Microbiome Project Consortium A framework for human microbiome research Nature 2012 486 215 21 10.1038/nature11209 22699610 PMC3377744 · DOI · Paper page
  50. [57]Hang J Desai V Zavaljevski N Yang Y Lin X Satya RV 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles Microbiome 2014 2 31 10.1186/2049-2618-2-31 25228989 PMC4165438 · DOI
  51. [58]Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, Knight R. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 2016;1. PMC5069758 27822526
  52. [59]Faust K Raes J Microbial interactions: from networks to models Nat Rev Microbiol 2012 10 538 50 10.1038/nrmicro2832 22796884 · DOI
  53. [60]Mitri S Foster KR The genotypic view of social interactions in microbial communities Annu Rev Genet 2013 47 247 73 10.1146/annurev-genet-111212-133307 24016192 · DOI
  54. [61]Tan J Zuniga C Zengler K Unraveling interactions in microbial communities—from co-cultures to microbiomes J Microbiol 2015 53 295 305 10.1007/s12275-015-5060-1 25935300 · DOI
  55. [62]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 27379075 PMC4908856
  56. [63]Tsilimigras MC Fodor AA Compositional data analysis of the microbiome: fundamentals, tools, and challenges Ann Epidemiol 2016 26 330 5 10.1016/j.annepidem.2016.03.002 27255738 · DOI
  57. [65]Friedman J Alm EJ Inferring correlation networks from genomic survey data PLoS Comput Biol 2012 8 e1002687 10.1371/journal.pcbi.1002687 23028285 PMC3447976 · DOI
  58. [66]Fang H Huang C Zhao H Deng M CCLasso: correlation inference for compositional data through Lasso Bioinformatics 2015 31 3172 80 10.1093/bioinformatics/btv349 26048598 PMC4693003 · DOI
  59. [67]Kurtz ZD Muller CL Miraldi ER Littman DR Blaser MJ Bonneau RA Sparse and compositionally robust inference of microbial ecological networks PLoS Comput Biol 2015 11 e1004226 10.1371/journal.pcbi.1004226 25950956 PMC4423992 · DOI · Paper page
  60. [68]Wu GD Compher C Chen EZ Smith SA Shah RD Bittinger K Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production Gut 2016 65 63 72 10.1136/gutjnl-2014-308209 25431456 PMC4583329 · DOI · Paper page
  61. [69]Bouslimani A Porto C Rath CM Wang M Guo Y Gonzalez A Molecular cartography of the human skin surface in 3D Proc Natl Acad Sci U S A 2015 112 E2120 9 10.1073/pnas.1424409112 25825778 PMC4418856 · DOI
  62. [70]Org E Blum Y Kasela S Mehrabian M Kuusisto J Kangas AJ Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort Genome Biol 2017 18 70 10.1186/s13059-017-1194-2 28407784 PMC5390365 · DOI
  63. [71]Ashrafian H Li JV Spagou K Harling L Masson P Darzi A Bariatric surgery modulates circulating and cardiac metabolites J Proteome Res 2014 13 570 80 10.1021/pr400748f 24279706 · DOI
  64. [72]Wikoff WR Anfora AT Liu J Schultz PG Lesley SA Peters EC Siuzdak G Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites Proc Natl Acad Sci U S A 2009 106 3698 703 10.1073/pnas.0812874106 19234110 PMC2656143 · DOI · Paper page
  65. [73]Thorsen J Brejnrod A Mortensen M Rasmussen MA Stokholm J Al-Soud WA 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 10.1186/s40168-016-0208-8 27884206 PMC5123278 · DOI
  66. [74]Paulson JN Stine OC Bravo HC Pop M Differential abundance analysis for microbial marker-gene surveys Nat Methods 2013 10 1200 2 10.1038/nmeth.2658 24076764 PMC4010126 · DOI
  67. [75]Morgan XC Tickle TL Sokol H Gevers D Devaney KL Ward DV Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment Genome Biol 2012 13 R79 10.1186/gb-2012-13-9-r79 23013615 PMC3506950 · DOI · Paper page
  68. [76]Robinson MD McCarthy DJ Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 2010 26 139 40 10.1093/bioinformatics/btp616 19910308 PMC2796818 · DOI · Paper page
  69. [77]Love MI Huber W Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol 2014 15 550 10.1186/s13059-014-0550-8 25516281 PMC4302049 · DOI · Paper page
  70. [78]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 10.1186/gb-2014-15-2-r29 24485249 PMC4053721 · DOI · Paper page
  71. [79]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 10.1186/s12864-016-2386-y 26810311 PMC4727335 · DOI
  72. [80]Segata N Izard J Waldron L Gevers D Miropolsky L Garrett WS Huttenhower C Metagenomic biomarker discovery and explanation Genome Biol 2011 12 R60 10.1186/gb-2011-12-6-r60 21702898 PMC3218848 · DOI · Paper page
  73. [81]White JR Nagarajan N Pop M Statistical methods for detecting differentially abundant features in clinical metagenomic samples PLoS Comput Biol 2009 5 e1000352 10.1371/journal.pcbi.1000352 19360128 PMC2661018 · DOI
  74. [83]Robinson MD Oshlack A A scaling normalization method for differential expression analysis of RNA-seq data Genome Biol 2010 11 R25 10.1186/gb-2010-11-3-r25 20196867 PMC2864565 · DOI · Paper page
  75. [84]Anders S Huber W Differential expression analysis for sequence count data Genome Biol 2010 11 R106 10.1186/gb-2010-11-10-r106 20979621 PMC3218662 · DOI · Paper page
  76. [86]Zhang X Mallick H Tang Z Zhang L Cui X Benson AK Yi N Negative binomial mixed models for analyzing microbiome count data BMC Bioinformatics 2017 18 4 10.1186/s12859-016-1441-7 28049409 PMC5209949 · DOI
  77. [87]Bucci V Tzen B Li N Simmons M Tanoue T Bogart E MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses Genome Biol 2016 17 121 10.1186/s13059-016-0980-6 27259475 PMC4893271 · DOI
  78. [88]Arumugam M Raes J Pelletier E Le Paslier D Yamada T Mende DR Enterotypes of the human gut microbiome Nature 2011 473 174 80 10.1038/nature09944 21508958 PMC3728647 · DOI · Paper page
  79. [90]Gajer P Brotman RM Bai G Sakamoto J Schutte UM Zhong X Temporal dynamics of the human vaginal microbiota Sci Transl Med 2012 4 132ra152 10.1126/scitranslmed.3003605 PMC3722878 22553250 · DOI
  80. [93]Zhao N Chen J Carroll IM Ringel-Kulka T Epstein MP Zhou H Testing in microbiome-profiling studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test Am J Hum Genet 2015 96 797 807 10.1016/j.ajhg.2015.04.003 25957468 PMC4570290 · DOI · Paper page
  81. [94]Clarke KR Non‐parametric multivariate analyses of changes in community structure Aust J Ecol 1993 18 117 43 10.1111/j.1442-9993.1993.tb00438.x · DOI
  82. [95]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 10.1093/bioinformatics/btw311 27197815 PMC5013911 · DOI
  83. [96]Randolph TW, Zhao S, Copeland W, Hullar M, Shojaie A. Kernel-Penalized regression for analysis of microbiome data. arXiv 2015;arXiv:151100297. PMC6138053 30224943
  84. [97]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 10.1089/cmb.2016.0180 27892712 · DOI
  85. [98]Weiss S Xu ZZ Peddada S Amir A Bittinger K Gonzalez A Normalization and microbial differential abundance strategies depend upon data characteristics Microbiome 2017 5 27 10.1186/s40168-017-0237-y 28253908 PMC5335496 · DOI
  86. [100]Olive DJ Olive DJ Multivariate linear regression Linear regression 2017 Cham Springer 343 87
  87. [101]Hidalgo B Goodman M Multivariate or multivariable regression? Am J Public Health 2013 103 39 40 10.2105/AJPH.2012.300897 23153131 PMC3518362 · DOI
  88. [102]Tsai AC Achieving consensus on terminology describing multivariable analyses Am J Public Health 2013 103 e1 10.2105/AJPH.2013.301234 23597350 PMC3679183 · DOI
  89. [103]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 10.1017/S0950268816000662 27049299 PMC9150531 · DOI
  90. [104]Brooks JP Challenges for case-control studies with microbiome data Ann Epidemiol 2016 26 336 41 10.1016/j.annepidem.2016.03.009 27255739 · DOI
  91. [105]Lozupone CA Stombaugh J Gonzalez A Ackermann G Wendel D Vazquez-Baeza Y Meta-analyses of studies of the human microbiota Genome Res 2013 23 1704 14 10.1101/gr.151803.112 23861384 PMC3787266 · DOI · Paper page
  92. [106]Bokulich NA, Rideout JR, Mercurio WG, Shiffer A, Wolfe B, Maurice CF, et al. mockrobiota: a public resource for microbiome bioinformatics benchmarking. mSystems 2016;1. PMC5080401 27822553
  93. [107]Johnson WE Li C Rabinovic A Adjusting batch effects in microarray expression data using empirical Bayes methods Biostatistics 2007 8 118 27 10.1093/biostatistics/kxj037 16632515 · DOI · Paper page
  94. [108]Ritchie ME Phipson B Wu D Hu Y Law CW Shi W Smyth GK limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res 2015 43 e47 10.1093/nar/gkv007 25605792 PMC4402510 · DOI · Paper page
  95. [110]Ananthakrishnan AN Luo C Yajnik V Khalili H Garber JJ Stevens BW Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases Cell Host Microbe 2017 21 603 10 10.1016/j.chom.2017.04.010 28494241 PMC5705050 · DOI · Paper page
  96. [112]Saha JR Butler VP Jr Neu HC Lindenbaum J Digoxin-inactivating bacteria: identification in human gut flora Science 1983 220 325 7 10.1126/science.6836275 6836275 · DOI · Paper page
  97. [113]Buffie CG Bucci V Stein RR McKenney PT Ling L Gobourne A Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile Nature 2015 517 205 8 10.1038/nature13828 25337874 PMC4354891 · DOI · Paper page
  98. [115]Yosef N Regev A Writ large: genomic dissection of the effect of cellular environment on immune response Science 2016 354 64 8 10.1126/science.aaf5453 27846493 PMC5111631 · DOI
  99. [116]Schloissnig S Arumugam M Sunagawa S Mitreva M Tap J Zhu A Genomic variation landscape of the human gut microbiome Nature 2013 493 45 50 10.1038/nature11711 23222524 PMC3536929 · DOI · Paper page
  100. [117]Franzosa EA Huang K Meadow JF Gevers D Lemon KP Bohannan BJ Huttenhower C Identifying personal microbiomes using metagenomic codes Proc Natl Acad Sci U S A 2015 112 E2930 8 10.1073/pnas.1423854112 25964341 PMC4460507 · DOI
  101. [118]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 10.1002/dmrr.2303 22492720 · DOI
  102. [119]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 10.1186/s40168-015-0105-6 26434730 PMC4593230 · DOI
  103. [120]Huson DH Auch AF Qi J Schuster SC MEGAN analysis of metagenomic data Genome Res 2007 17 377 86 10.1101/gr.5969107 17255551 PMC1800929 · DOI
  104. [121]Tu Q He Z Zhou J Strain/species identification in metagenomes using genome-specific markers Nucleic Acids Res 2014 42 e67 10.1093/nar/gku138 24523352 PMC4005670 · DOI
  105. [122]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 10.1186/s13073-015-0176-9 26136847 PMC4487561 · DOI
  106. [123]Ahn TH Chai J Pan C Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance Bioinformatics 2015 31 170 7 10.1093/bioinformatics/btu641 25266224 PMC4287953 · DOI
  107. [124]Francis OE Bendall M Manimaran S Hong C Clement NL Castro-Nallar E Pathoscope: species identification and strain attribution with unassembled sequencing data Genome Res 2013 23 1721 9 10.1101/gr.150151.112 23843222 PMC3787268 · DOI
  108. [125]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 10.1038/nbt.3329 26368049 PMC4720164 · DOI
  109. [126]Greenblum S Carr R Borenstein E Extensive strain-level copy-number variation across human gut microbiome species Cell 2015 160 583 94 10.1016/j.cell.2014.12.038 25640238 PMC4507803 · DOI
  110. [127]McClure R Balasubramanian D Sun Y Bobrovskyy M Sumby P Genco CA Computational analysis of bacterial RNA-Seq data Nucleic Acids Res 2013 41 e140 10.1093/nar/gkt444 23716638 PMC3737546 · DOI
  111. [128]Ghosh S Chan CK Analysis of RNA-Seq data using TopHat and Cufflinks Methods Mol Biol 2016 1374 339 61 10.1007/978-1-4939-3167-5_18 26519415 · DOI
  112. [129]Narayanasamy S Jarosz Y Muller EE Heintz-Buschart A Herold M Kaysen A IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses Genome Biol 2016 17 260 10.1186/s13059-016-1116-8 27986083 PMC5159968 · DOI
  113. [130]Westreich ST Korf I Mills DA Lemay DG SAMSA: a comprehensive metatranscriptome analysis pipeline BMC Bioinformatics 2016 17 399 10.1186/s12859-016-1270-8 27687690 PMC5041328 · DOI
  114. [131]Ni Y Li J Panagiotou G COMAN: a web server for comprehensive metatranscriptomics analysis BMC Genomics 2016 17 622 10.1186/s12864-016-2964-z 27515514 PMC4982211 · DOI
  115. [132]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 10.1089/cmb.2013.0042 23829653 · DOI
  116. [133]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 10.1093/bioinformatics/bts094 22368243 PMC3324515 · DOI
  117. [134]Bose T Haque MM Reddy C Mande SS COGNIZER: a framework for functional annotation of metagenomic datasets PLoS One 2015 10 e0142102 10.1371/journal.pone.0142102 26561344 PMC4641738 · DOI
  118. [135]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 10.1186/s12859-016-1278-0 27724866 PMC5057277 · DOI
  119. [136]Huson DH Beier S Flade I Gorska A El-Hadidi M Mitra S MEGAN community edition—interactive exploration and analysis of large-scale microbiome sequencing data PLoS Comput Biol 2016 12 e1004957 10.1371/journal.pcbi.1004957 27327495 PMC4915700 · DOI · Paper page
  120. [137]Nayfach S Bradley PH Wyman SK Laurent TJ Williams A Eisen JA Automated and accurate estimation of gene family abundance from shotgun metagenomes PLoS Comput Biol 2015 11 e1004573 10.1371/journal.pcbi.1004573 26565399 PMC4643905 · DOI
  121. [138]Morgan XC Kabakchiev B Waldron L Tyler AD Tickle TL Milgrom R 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 10.1186/s13059-015-0637-x 25887922 PMC4414286 · DOI
  122. [139]Ban Y An L Jiang H Investigating microbial co-occurrence patterns based on metagenomic compositional data Bioinformatics 2015 31 3322 9 10.1093/bioinformatics/btv364 26079350 PMC4795632 · DOI
  123. [140]Deng Y Jiang YH Yang Y He Z Luo F Zhou J Molecular ecological network analyses BMC Bioinformatics 2012 13 113 10.1186/1471-2105-13-113 22646978 PMC3428680 · DOI
  124. [141]Biswas S McDonald M Lundberg DS Dangl JL Jojic V Learning microbial interaction networks from metagenomic count data J Comput Biol 2016 23 526 35 10.1089/cmb.2016.0061 27267776 · DOI
  125. [142]Shaw GT Pao YY Wang D MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles BMC Bioinformatics 2016 17 488 10.1186/s12859-016-1359-0 27887570 PMC5124289 · DOI
  126. [143]Shafiei M Dunn KA Boon E MacDonald SM Walsh DA Gu H Bielawski JP BioMiCo: a supervised Bayesian model for inference of microbial community structure Microbiome 2015 3 8 10.1186/s40168-015-0073-x 25774293 PMC4359585 · DOI
  127. [144]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 10.1371/journal.pcbi.1003918 25412107 PMC4238953 · DOI
  128. [145]McGeachie MJ Sordillo JE Gibson T Weinstock GM Liu YY Gold DR Longitudinal prediction of the infant gut microbiome with dynamic Bayesian networks Sci Rep 2016 6 20359 10.1038/srep20359 26853461 PMC4745046 · DOI

Cited by

Sorted by citations

Authors
Mallick, H., Ma, S., Franzosa, E. A., Vatanen, T., Morgan, X. C., & Huttenhower, C.
Affiliations
  • Broad Institute (US)
  • University of Otago (NZ)
Corresponding author
Huttenhower, Curtis
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2017-11-30
Volume
18
Issue
1
Pages
228-228
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-017-1359-z
PMC
PMC5708111
OpenAlex
W2768511889
Cited by
192
References
15
Citations by year
2026: 7 · 2025: 13 · 2024: 20 · 2023: 25 · 2022: 23 · 2021: 34 · 2020: 36 · 2019: 19 · 2018: 14 · 2017: 1
FWCI
1.79
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • ResearchSpace (University of Auckland) (submittedVersion, cc-by)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (R01HG005220)
  • National Institute of Diabetes and Digestive and Kidney Diseases (U54DK102557)
  • National Institute of Dental and Craniofacial Research (U54DE023798)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
Keywords
microbiome, experimental design, multi-omics integration, molecular epidemiology, microbial community analysis, host-microbe interactions, target identification
MeSH terms
Animals, Humans, Research Design, Computational Biology, Gene Expression Profiling, Proteomics, Metabolomics, Metagenomics, DNA Barcoding, Taxonomic, Microbiota

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