Experimental design and quantitative analysis of microbial community multiomics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-11-30

AUTHORS

Himel Mallick, Siyuan Ma, Eric A. Franzosa, Tommi Vatanen, Xochitl C. Morgan, Curtis Huttenhower

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. More... »

PAGES

228

References to SciGraph publications

  • 2017-01-03. Negative binomial mixed models for analyzing microbiome count data in BMC BIOINFORMATICS
  • 2016-03-21. Strain-level microbial epidemiology and population genomics from shotgun metagenomics in NATURE METHODS
  • 2012-05-09. Human gut microbiome viewed across age and geography in NATURE
  • 2012-11-08. Interpreting noncoding genetic variation in complex traits and human disease in NATURE BIOTECHNOLOGY
  • 2016. Analysis of RNA-Seq Data Using TopHat and Cufflinks in PLANT BIOINFORMATICS
  • 2013-07-04. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2014-10-17. Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2014-05-23. The mycobiota: interactions between commensal fungi and the host immune system in NATURE REVIEWS IMMUNOLOGY
  • 2012-06-13. Structure, function and diversity of the healthy human microbiome in NATURE
  • 2017-04-13. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort in GENOME BIOLOGY
  • 2015-05-03. Unraveling interactions in microbial communities - from co-cultures to microbiomes in JOURNAL OF MICROBIOLOGY
  • 2015-04-08. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease in GENOME BIOLOGY
  • 2016-02-08. Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks in SCIENTIFIC REPORTS
  • 2017-03-03. Normalization and microbial differential abundance strategies depend upon data characteristics in MICROBIOME
  • 2016-05-27. Gut microbiota, metabolites and host immunity in NATURE REVIEWS IMMUNOLOGY
  • 2016-01-26. A survey of best practices for RNA-seq data analysis in GENOME BIOLOGY
  • 2012-09-26. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment in GENOME BIOLOGY
  • 2015-03-10. BioMiCo: a supervised Bayesian model for inference of microbial community structure in MICROBIOME
  • 2014-09-17. Artificial sweeteners induce glucose intolerance by altering the gut microbiota in NATURE
  • 2012-07-16. Microbial interactions: from networks to models in NATURE REVIEWS MICROBIOLOGY
  • 2014-12-05. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 in GENOME BIOLOGY
  • 2014-02-03. The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women in MICROBIOME
  • 2015-11-09. Twenty years of bacterial genome sequencing in NATURE REVIEWS MICROBIOLOGY
  • 2010-05-06. Genome dynamics and its impact on evolution of Escherichia coli in MEDICAL MICROBIOLOGY AND IMMUNOLOGY
  • 2015-06-09. Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data in GENOME MEDICINE
  • 2014-10-22. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile in NATURE
  • 2011-04-20. Enterotypes of the human gut microbiome in NATURE
  • 2015-12-09. The microbiome quality control project: baseline study design and future directions in GENOME BIOLOGY
  • 2015-03-21. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies in BMC MICROBIOLOGY
  • 2016-11-25. MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles in BMC BIOINFORMATICS
  • 2012-05-30. Molecular ecological network analyses in BMC BIOINFORMATICS
  • 2016-08-11. COMAN: a web server for comprehensive metatranscriptomics analysis in BMC GENOMICS
  • 2016-05-23. DADA2: High-resolution sample inference from Illumina amplicon data in NATURE METHODS
  • 2016-11-25. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies in MICROBIOME
  • 2010-03-02. A scaling normalization method for differential expression analysis of RNA-seq data in GENOME BIOLOGY
  • 2016-01-25. Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics in BMC GENOMICS
  • 2014-09-16. 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles in MICROBIOME
  • 2016-04-21. Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome in GENOME MEDICINE
  • 2017. Linear Regression in NONE
  • 2016-10-10. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies in BMC BIOINFORMATICS
  • 2010-03. A human gut microbial gene catalogue established by metagenomic sequencing in NATURE
  • 2016-09-29. SAMSA: a comprehensive metatranscriptome analysis pipeline in BMC BIOINFORMATICS
  • 2012-06-13. A framework for human microbiome research in NATURE
  • 2010-10-27. Differential expression analysis for sequence count data in GENOME BIOLOGY
  • 2014-02-03. voom: precision weights unlock linear model analysis tools for RNA-seq read counts in GENOME BIOLOGY
  • 2014-07-11. Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2012-03-28. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes in GENOME BIOLOGY
  • 2012-12-05. Genomic variation landscape of the human gut microbiome in NATURE
  • 2011-06-24. Metagenomic biomarker discovery and explanation in GENOME BIOLOGY
  • 2015-04-27. Sequencing and beyond: integrating molecular 'omics' for microbial community profiling in NATURE REVIEWS MICROBIOLOGY
  • 2015-09-14. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning in NATURE BIOTECHNOLOGY
  • 2017-03-28. Why prokaryotes have pangenomes in NATURE MICROBIOLOGY
  • 2015-10-05. Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering in MICROBIOME
  • 2015-09-07. ConStrains identifies microbial strains in metagenomic datasets in NATURE BIOTECHNOLOGY
  • 2013-06-05. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships in MICROBIOME
  • 2013-09-29. Differential abundance analysis for microbial marker-gene surveys in NATURE METHODS
  • 2008-10-30. Shotgun metaproteomics of the human distal gut microbiota in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2012-10-31. Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes in BMC GENOMICS
  • 2016-12-16. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses in GENOME BIOLOGY
  • 2016-06-03. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-017-1359-z

    DOI

    http://dx.doi.org/10.1186/s13059-017-1359-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1093077335

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29187204


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Animals", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "DNA Barcoding, Taxonomic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Profiling", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metabolomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metagenomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Microbiota", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Proteomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Research Design", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Biostatistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA", 
                "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mallick", 
            "givenName": "Himel", 
            "id": "sg:person.01356136132.18", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01356136132.18"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Biostatistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA", 
                "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ma", 
            "givenName": "Siyuan", 
            "id": "sg:person.01161715246.43", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161715246.43"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Biostatistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA", 
                "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Franzosa", 
            "givenName": "Eric A.", 
            "id": "sg:person.0772302366.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772302366.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vatanen", 
            "givenName": "Tommi", 
            "id": "sg:person.0767040254.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767040254.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand", 
              "id": "http://www.grid.ac/institutes/grid.29980.3a", 
              "name": [
                "Department of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Morgan", 
            "givenName": "Xochitl C.", 
            "id": "sg:person.0654646332.90", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0654646332.90"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Biostatistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA", 
                "Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Huttenhower", 
            "givenName": "Curtis", 
            "id": "sg:person.01214462502.85", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214462502.85"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/s13059-015-0841-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052977184", 
              "https://doi.org/10.1186/s13059-015-0841-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-016-0980-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000278738", 
              "https://doi.org/10.1186/s13059-016-0980-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2014-15-2-r29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045312009", 
              "https://doi.org/10.1186/gb-2014-15-2-r29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-015-0637-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019300544", 
              "https://doi.org/10.1186/s13059-015-0637-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nri.2016.42", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026888087", 
              "https://doi.org/10.1038/nri.2016.42"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-015-0105-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011282839", 
              "https://doi.org/10.1186/s40168-015-0105-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2014.195", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053327775", 
              "https://doi.org/10.1038/ismej.2014.195"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3319", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019116134", 
              "https://doi.org/10.1038/nbt.3319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1359-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015147157", 
              "https://doi.org/10.1186/s12859-016-1359-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature08821", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050498034", 
              "https://doi.org/10.1038/nature08821"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2049-2618-1-17", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008346347", 
              "https://doi.org/10.1186/2049-2618-1-17"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-2964-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016270288", 
              "https://doi.org/10.1186/s12864-016-2964-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2010-11-3-r25", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050509557", 
              "https://doi.org/10.1186/gb-2010-11-3-r25"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3329", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044309870", 
              "https://doi.org/10.1038/nbt.3329"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1278-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037408767", 
              "https://doi.org/10.1186/s12859-016-1278-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12866-015-0351-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001744296", 
              "https://doi.org/10.1186/s12866-015-0351-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-015-0176-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032716746", 
              "https://doi.org/10.1186/s13073-015-0176-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-016-1116-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011759184", 
              "https://doi.org/10.1186/s13059-016-1116-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro3565", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021453336", 
              "https://doi.org/10.1038/nrmicro3565"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-016-0208-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019428991", 
              "https://doi.org/10.1186/s40168-016-0208-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.3869", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016631324", 
              "https://doi.org/10.1038/nmeth.3869"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12275-015-5060-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032349807", 
              "https://doi.org/10.1007/s12275-015-5060-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-13-577", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003854763", 
              "https://doi.org/10.1186/1471-2164-13-577"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep20359", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003843427", 
              "https://doi.org/10.1038/srep20359"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmicrobiol.2017.40", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084129362", 
              "https://doi.org/10.1038/nmicrobiol.2017.40"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-13-113", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032970670", 
              "https://doi.org/10.1186/1471-2105-13-113"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4939-3167-5_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042092761", 
              "https://doi.org/10.1007/978-1-4939-3167-5_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11234", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007740093", 
              "https://doi.org/10.1038/nature11234"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro3451", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000287747", 
              "https://doi.org/10.1038/nrmicro3451"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-015-0073-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031266707", 
              "https://doi.org/10.1186/s40168-015-0073-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1441-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019274698", 
              "https://doi.org/10.1186/s12859-016-1441-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00430-010-0161-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044190009", 
              "https://doi.org/10.1007/s00430-010-0161-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature13793", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003490001", 
              "https://doi.org/10.1038/nature13793"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2014.117", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012531530", 
              "https://doi.org/10.1038/ismej.2014.117"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.3802", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044732511", 
              "https://doi.org/10.1038/nmeth.3802"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-017-1194-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084807339", 
              "https://doi.org/10.1186/s13059-017-1194-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2049-2618-2-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042220655", 
              "https://doi.org/10.1186/2049-2618-2-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro2832", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030236624", 
              "https://doi.org/10.1038/nrmicro2832"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2422", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012814278", 
              "https://doi.org/10.1038/nbt.2422"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nri3684", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009181969", 
              "https://doi.org/10.1038/nri3684"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-2386-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020096515", 
              "https://doi.org/10.1186/s12864-016-2386-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-017-0237-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084252802", 
              "https://doi.org/10.1186/s40168-017-0237-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2008.108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001388414", 
              "https://doi.org/10.1038/ismej.2008.108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052378845", 
              "https://doi.org/10.1038/nature11053"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-016-0293-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005283564", 
              "https://doi.org/10.1186/s13073-016-0293-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2012-13-9-r79", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029450096", 
              "https://doi.org/10.1186/gb-2012-13-9-r79"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature09944", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026204536", 
              "https://doi.org/10.1038/nature09944"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11209", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027248000", 
              "https://doi.org/10.1038/nature11209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2011-12-6-r60", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000243423", 
              "https://doi.org/10.1186/gb-2011-12-6-r60"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1270-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013175693", 
              "https://doi.org/10.1186/s12859-016-1270-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-016-0881-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041902478", 
              "https://doi.org/10.1186/s13059-016-0881-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2010-11-10-r106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031289083", 
              "https://doi.org/10.1186/gb-2010-11-10-r106"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-55252-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084914659", 
              "https://doi.org/10.1007/978-3-319-55252-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2049-2618-2-31", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003755394", 
              "https://doi.org/10.1186/2049-2618-2-31"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2012-13-3-r23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036305770", 
              "https://doi.org/10.1186/gb-2012-13-3-r23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature13828", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004780748", 
              "https://doi.org/10.1038/nature13828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11711", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004024856", 
              "https://doi.org/10.1038/nature11711"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2013.102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027740731", 
              "https://doi.org/10.1038/ismej.2013.102"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-014-0550-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015222646", 
              "https://doi.org/10.1186/s13059-014-0550-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2658", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002139060", 
              "https://doi.org/10.1038/nmeth.2658"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-11-30", 
        "datePublishedReg": "2017-11-30", 
        "description": "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.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s13059-017-1359-z", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2529382", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3806830", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2699180", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1023439", 
            "issn": [
              "1474-760X", 
              "1465-6906"
            ], 
            "name": "Genome Biology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "18"
          }
        ], 
        "keywords": [
          "translational microbiome research", 
          "high-throughput target discovery", 
          "target discovery", 
          "multiomics data", 
          "microbiome research", 
          "experimental design considerations", 
          "culture-based methods", 
          "human health", 
          "molecular methods", 
          "microbiome profiles", 
          "molecular epidemiology", 
          "multiomics", 
          "large population", 
          "microbiome", 
          "data analysis challenges", 
          "host", 
          "experimental design", 
          "discovery", 
          "population", 
          "epidemiological approach", 
          "data types", 
          "analysis challenges", 
          "quantitative analysis", 
          "bioactives", 
          "study", 
          "profile", 
          "step", 
          "analysis", 
          "types", 
          "health", 
          "data", 
          "approach", 
          "epidemiology", 
          "challenges", 
          "research", 
          "method", 
          "current best practice", 
          "technology", 
          "design", 
          "consideration", 
          "analyzing", 
          "practice", 
          "best practices", 
          "design considerations"
        ], 
        "name": "Experimental design and quantitative analysis of microbial community multiomics", 
        "pagination": "228", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1093077335"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13059-017-1359-z"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "29187204"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13059-017-1359-z", 
          "https://app.dimensions.ai/details/publication/pub.1093077335"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-09-02T16:00", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_722.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s13059-017-1359-z"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13059-017-1359-z'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13059-017-1359-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13059-017-1359-z'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13059-017-1359-z'


     

    This table displays all metadata directly associated to this object as RDF triples.

    434 TRIPLES      21 PREDICATES      140 URIs      71 LITERALS      17 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13059-017-1359-z schema:about N09c98f7b814949a38308178835d019e9
    2 N0c42c8816da3409cba952381a43a4df9
    3 N0d401b4d0efc43dba4525afb4697d985
    4 N1830278032e54e25a9eb7704e8bba46d
    5 N1863103290be4bc891292fc2928f86c4
    6 N8a4997b6afaf4c2b8dc71fc492656e4b
    7 Na4798ae98e7246058b40ab26acd9d1f7
    8 Nc10e6603303445a4a80f132d06b1413d
    9 Nec413174ae7346bb81286fd1afc2bbd5
    10 Nf01a2cfa64b64f38a7016fa877fca824
    11 anzsrc-for:05
    12 anzsrc-for:06
    13 anzsrc-for:08
    14 schema:author Nf5fefd25ef3a429ea6010a5e1872059b
    15 schema:citation sg:pub.10.1007/978-1-4939-3167-5_18
    16 sg:pub.10.1007/978-3-319-55252-1
    17 sg:pub.10.1007/s00430-010-0161-2
    18 sg:pub.10.1007/s12275-015-5060-1
    19 sg:pub.10.1038/ismej.2008.108
    20 sg:pub.10.1038/ismej.2013.102
    21 sg:pub.10.1038/ismej.2014.117
    22 sg:pub.10.1038/ismej.2014.195
    23 sg:pub.10.1038/nature08821
    24 sg:pub.10.1038/nature09944
    25 sg:pub.10.1038/nature11053
    26 sg:pub.10.1038/nature11209
    27 sg:pub.10.1038/nature11234
    28 sg:pub.10.1038/nature11711
    29 sg:pub.10.1038/nature13793
    30 sg:pub.10.1038/nature13828
    31 sg:pub.10.1038/nbt.2422
    32 sg:pub.10.1038/nbt.3319
    33 sg:pub.10.1038/nbt.3329
    34 sg:pub.10.1038/nmeth.2658
    35 sg:pub.10.1038/nmeth.3802
    36 sg:pub.10.1038/nmeth.3869
    37 sg:pub.10.1038/nmicrobiol.2017.40
    38 sg:pub.10.1038/nri.2016.42
    39 sg:pub.10.1038/nri3684
    40 sg:pub.10.1038/nrmicro2832
    41 sg:pub.10.1038/nrmicro3451
    42 sg:pub.10.1038/nrmicro3565
    43 sg:pub.10.1038/srep20359
    44 sg:pub.10.1186/1471-2105-13-113
    45 sg:pub.10.1186/1471-2164-13-577
    46 sg:pub.10.1186/2049-2618-1-17
    47 sg:pub.10.1186/2049-2618-2-31
    48 sg:pub.10.1186/2049-2618-2-4
    49 sg:pub.10.1186/gb-2010-11-10-r106
    50 sg:pub.10.1186/gb-2010-11-3-r25
    51 sg:pub.10.1186/gb-2011-12-6-r60
    52 sg:pub.10.1186/gb-2012-13-3-r23
    53 sg:pub.10.1186/gb-2012-13-9-r79
    54 sg:pub.10.1186/gb-2014-15-2-r29
    55 sg:pub.10.1186/s12859-016-1270-8
    56 sg:pub.10.1186/s12859-016-1278-0
    57 sg:pub.10.1186/s12859-016-1359-0
    58 sg:pub.10.1186/s12859-016-1441-7
    59 sg:pub.10.1186/s12864-016-2386-y
    60 sg:pub.10.1186/s12864-016-2964-z
    61 sg:pub.10.1186/s12866-015-0351-6
    62 sg:pub.10.1186/s13059-014-0550-8
    63 sg:pub.10.1186/s13059-015-0637-x
    64 sg:pub.10.1186/s13059-015-0841-8
    65 sg:pub.10.1186/s13059-016-0881-8
    66 sg:pub.10.1186/s13059-016-0980-6
    67 sg:pub.10.1186/s13059-016-1116-8
    68 sg:pub.10.1186/s13059-017-1194-2
    69 sg:pub.10.1186/s13073-015-0176-9
    70 sg:pub.10.1186/s13073-016-0293-0
    71 sg:pub.10.1186/s40168-015-0073-x
    72 sg:pub.10.1186/s40168-015-0105-6
    73 sg:pub.10.1186/s40168-016-0208-8
    74 sg:pub.10.1186/s40168-017-0237-y
    75 schema:datePublished 2017-11-30
    76 schema:datePublishedReg 2017-11-30
    77 schema:description 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.
    78 schema:genre article
    79 schema:isAccessibleForFree true
    80 schema:isPartOf N367ad63dde184469b065902d6086fdc8
    81 Nc93c61dc735445e18a23cca079e9c043
    82 sg:journal.1023439
    83 schema:keywords analysis
    84 analysis challenges
    85 analyzing
    86 approach
    87 best practices
    88 bioactives
    89 challenges
    90 consideration
    91 culture-based methods
    92 current best practice
    93 data
    94 data analysis challenges
    95 data types
    96 design
    97 design considerations
    98 discovery
    99 epidemiological approach
    100 epidemiology
    101 experimental design
    102 experimental design considerations
    103 health
    104 high-throughput target discovery
    105 host
    106 human health
    107 large population
    108 method
    109 microbiome
    110 microbiome profiles
    111 microbiome research
    112 molecular epidemiology
    113 molecular methods
    114 multiomics
    115 multiomics data
    116 population
    117 practice
    118 profile
    119 quantitative analysis
    120 research
    121 step
    122 study
    123 target discovery
    124 technology
    125 translational microbiome research
    126 types
    127 schema:name Experimental design and quantitative analysis of microbial community multiomics
    128 schema:pagination 228
    129 schema:productId N05627a0dd4e84cff8da05177640810aa
    130 N06750cbaf2904fe0be25cd491e23170d
    131 Nbe8f465796944d4f8cccc50760dac6b0
    132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093077335
    133 https://doi.org/10.1186/s13059-017-1359-z
    134 schema:sdDatePublished 2022-09-02T16:00
    135 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    136 schema:sdPublisher Nb1fadd291559449694851e2b90383e60
    137 schema:url https://doi.org/10.1186/s13059-017-1359-z
    138 sgo:license sg:explorer/license/
    139 sgo:sdDataset articles
    140 rdf:type schema:ScholarlyArticle
    141 N05627a0dd4e84cff8da05177640810aa schema:name dimensions_id
    142 schema:value pub.1093077335
    143 rdf:type schema:PropertyValue
    144 N06750cbaf2904fe0be25cd491e23170d schema:name doi
    145 schema:value 10.1186/s13059-017-1359-z
    146 rdf:type schema:PropertyValue
    147 N09c98f7b814949a38308178835d019e9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    148 schema:name Gene Expression Profiling
    149 rdf:type schema:DefinedTerm
    150 N0c42c8816da3409cba952381a43a4df9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    151 schema:name Metabolomics
    152 rdf:type schema:DefinedTerm
    153 N0d401b4d0efc43dba4525afb4697d985 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    154 schema:name DNA Barcoding, Taxonomic
    155 rdf:type schema:DefinedTerm
    156 N1830278032e54e25a9eb7704e8bba46d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    157 schema:name Humans
    158 rdf:type schema:DefinedTerm
    159 N1863103290be4bc891292fc2928f86c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    160 schema:name Proteomics
    161 rdf:type schema:DefinedTerm
    162 N1c9a514a777a4c84846f2c02367ede66 rdf:first sg:person.01161715246.43
    163 rdf:rest N6fe0d3d7954540d481540f2e038cfc60
    164 N367ad63dde184469b065902d6086fdc8 schema:volumeNumber 18
    165 rdf:type schema:PublicationVolume
    166 N3e12863bfd354f838778c8085e847c8d rdf:first sg:person.01214462502.85
    167 rdf:rest rdf:nil
    168 N3f294fce2d7847cebb90decb81cc8429 rdf:first sg:person.0767040254.55
    169 rdf:rest N709ec3d688a14e76a3e2a6c14e95f9a7
    170 N6fe0d3d7954540d481540f2e038cfc60 rdf:first sg:person.0772302366.11
    171 rdf:rest N3f294fce2d7847cebb90decb81cc8429
    172 N709ec3d688a14e76a3e2a6c14e95f9a7 rdf:first sg:person.0654646332.90
    173 rdf:rest N3e12863bfd354f838778c8085e847c8d
    174 N8a4997b6afaf4c2b8dc71fc492656e4b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    175 schema:name Animals
    176 rdf:type schema:DefinedTerm
    177 Na4798ae98e7246058b40ab26acd9d1f7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    178 schema:name Microbiota
    179 rdf:type schema:DefinedTerm
    180 Nb1fadd291559449694851e2b90383e60 schema:name Springer Nature - SN SciGraph project
    181 rdf:type schema:Organization
    182 Nbe8f465796944d4f8cccc50760dac6b0 schema:name pubmed_id
    183 schema:value 29187204
    184 rdf:type schema:PropertyValue
    185 Nc10e6603303445a4a80f132d06b1413d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    186 schema:name Research Design
    187 rdf:type schema:DefinedTerm
    188 Nc93c61dc735445e18a23cca079e9c043 schema:issueNumber 1
    189 rdf:type schema:PublicationIssue
    190 Nec413174ae7346bb81286fd1afc2bbd5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    191 schema:name Computational Biology
    192 rdf:type schema:DefinedTerm
    193 Nf01a2cfa64b64f38a7016fa877fca824 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    194 schema:name Metagenomics
    195 rdf:type schema:DefinedTerm
    196 Nf5fefd25ef3a429ea6010a5e1872059b rdf:first sg:person.01356136132.18
    197 rdf:rest N1c9a514a777a4c84846f2c02367ede66
    198 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
    199 schema:name Environmental Sciences
    200 rdf:type schema:DefinedTerm
    201 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    202 schema:name Biological Sciences
    203 rdf:type schema:DefinedTerm
    204 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    205 schema:name Information and Computing Sciences
    206 rdf:type schema:DefinedTerm
    207 sg:grant.2529382 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-017-1359-z
    208 rdf:type schema:MonetaryGrant
    209 sg:grant.2699180 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-017-1359-z
    210 rdf:type schema:MonetaryGrant
    211 sg:grant.3806830 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-017-1359-z
    212 rdf:type schema:MonetaryGrant
    213 sg:journal.1023439 schema:issn 1465-6906
    214 1474-760X
    215 schema:name Genome Biology
    216 schema:publisher Springer Nature
    217 rdf:type schema:Periodical
    218 sg:person.01161715246.43 schema:affiliation grid-institutes:grid.66859.34
    219 schema:familyName Ma
    220 schema:givenName Siyuan
    221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161715246.43
    222 rdf:type schema:Person
    223 sg:person.01214462502.85 schema:affiliation grid-institutes:grid.66859.34
    224 schema:familyName Huttenhower
    225 schema:givenName Curtis
    226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214462502.85
    227 rdf:type schema:Person
    228 sg:person.01356136132.18 schema:affiliation grid-institutes:grid.66859.34
    229 schema:familyName Mallick
    230 schema:givenName Himel
    231 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01356136132.18
    232 rdf:type schema:Person
    233 sg:person.0654646332.90 schema:affiliation grid-institutes:grid.29980.3a
    234 schema:familyName Morgan
    235 schema:givenName Xochitl C.
    236 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0654646332.90
    237 rdf:type schema:Person
    238 sg:person.0767040254.55 schema:affiliation grid-institutes:grid.66859.34
    239 schema:familyName Vatanen
    240 schema:givenName Tommi
    241 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767040254.55
    242 rdf:type schema:Person
    243 sg:person.0772302366.11 schema:affiliation grid-institutes:grid.66859.34
    244 schema:familyName Franzosa
    245 schema:givenName Eric A.
    246 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772302366.11
    247 rdf:type schema:Person
    248 sg:pub.10.1007/978-1-4939-3167-5_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042092761
    249 https://doi.org/10.1007/978-1-4939-3167-5_18
    250 rdf:type schema:CreativeWork
    251 sg:pub.10.1007/978-3-319-55252-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084914659
    252 https://doi.org/10.1007/978-3-319-55252-1
    253 rdf:type schema:CreativeWork
    254 sg:pub.10.1007/s00430-010-0161-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044190009
    255 https://doi.org/10.1007/s00430-010-0161-2
    256 rdf:type schema:CreativeWork
    257 sg:pub.10.1007/s12275-015-5060-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032349807
    258 https://doi.org/10.1007/s12275-015-5060-1
    259 rdf:type schema:CreativeWork
    260 sg:pub.10.1038/ismej.2008.108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001388414
    261 https://doi.org/10.1038/ismej.2008.108
    262 rdf:type schema:CreativeWork
    263 sg:pub.10.1038/ismej.2013.102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027740731
    264 https://doi.org/10.1038/ismej.2013.102
    265 rdf:type schema:CreativeWork
    266 sg:pub.10.1038/ismej.2014.117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012531530
    267 https://doi.org/10.1038/ismej.2014.117
    268 rdf:type schema:CreativeWork
    269 sg:pub.10.1038/ismej.2014.195 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053327775
    270 https://doi.org/10.1038/ismej.2014.195
    271 rdf:type schema:CreativeWork
    272 sg:pub.10.1038/nature08821 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050498034
    273 https://doi.org/10.1038/nature08821
    274 rdf:type schema:CreativeWork
    275 sg:pub.10.1038/nature09944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026204536
    276 https://doi.org/10.1038/nature09944
    277 rdf:type schema:CreativeWork
    278 sg:pub.10.1038/nature11053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052378845
    279 https://doi.org/10.1038/nature11053
    280 rdf:type schema:CreativeWork
    281 sg:pub.10.1038/nature11209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027248000
    282 https://doi.org/10.1038/nature11209
    283 rdf:type schema:CreativeWork
    284 sg:pub.10.1038/nature11234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007740093
    285 https://doi.org/10.1038/nature11234
    286 rdf:type schema:CreativeWork
    287 sg:pub.10.1038/nature11711 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004024856
    288 https://doi.org/10.1038/nature11711
    289 rdf:type schema:CreativeWork
    290 sg:pub.10.1038/nature13793 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003490001
    291 https://doi.org/10.1038/nature13793
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1038/nature13828 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004780748
    294 https://doi.org/10.1038/nature13828
    295 rdf:type schema:CreativeWork
    296 sg:pub.10.1038/nbt.2422 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012814278
    297 https://doi.org/10.1038/nbt.2422
    298 rdf:type schema:CreativeWork
    299 sg:pub.10.1038/nbt.3319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019116134
    300 https://doi.org/10.1038/nbt.3319
    301 rdf:type schema:CreativeWork
    302 sg:pub.10.1038/nbt.3329 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044309870
    303 https://doi.org/10.1038/nbt.3329
    304 rdf:type schema:CreativeWork
    305 sg:pub.10.1038/nmeth.2658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002139060
    306 https://doi.org/10.1038/nmeth.2658
    307 rdf:type schema:CreativeWork
    308 sg:pub.10.1038/nmeth.3802 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044732511
    309 https://doi.org/10.1038/nmeth.3802
    310 rdf:type schema:CreativeWork
    311 sg:pub.10.1038/nmeth.3869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016631324
    312 https://doi.org/10.1038/nmeth.3869
    313 rdf:type schema:CreativeWork
    314 sg:pub.10.1038/nmicrobiol.2017.40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084129362
    315 https://doi.org/10.1038/nmicrobiol.2017.40
    316 rdf:type schema:CreativeWork
    317 sg:pub.10.1038/nri.2016.42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026888087
    318 https://doi.org/10.1038/nri.2016.42
    319 rdf:type schema:CreativeWork
    320 sg:pub.10.1038/nri3684 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009181969
    321 https://doi.org/10.1038/nri3684
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1038/nrmicro2832 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030236624
    324 https://doi.org/10.1038/nrmicro2832
    325 rdf:type schema:CreativeWork
    326 sg:pub.10.1038/nrmicro3451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000287747
    327 https://doi.org/10.1038/nrmicro3451
    328 rdf:type schema:CreativeWork
    329 sg:pub.10.1038/nrmicro3565 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021453336
    330 https://doi.org/10.1038/nrmicro3565
    331 rdf:type schema:CreativeWork
    332 sg:pub.10.1038/srep20359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003843427
    333 https://doi.org/10.1038/srep20359
    334 rdf:type schema:CreativeWork
    335 sg:pub.10.1186/1471-2105-13-113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032970670
    336 https://doi.org/10.1186/1471-2105-13-113
    337 rdf:type schema:CreativeWork
    338 sg:pub.10.1186/1471-2164-13-577 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003854763
    339 https://doi.org/10.1186/1471-2164-13-577
    340 rdf:type schema:CreativeWork
    341 sg:pub.10.1186/2049-2618-1-17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008346347
    342 https://doi.org/10.1186/2049-2618-1-17
    343 rdf:type schema:CreativeWork
    344 sg:pub.10.1186/2049-2618-2-31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003755394
    345 https://doi.org/10.1186/2049-2618-2-31
    346 rdf:type schema:CreativeWork
    347 sg:pub.10.1186/2049-2618-2-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042220655
    348 https://doi.org/10.1186/2049-2618-2-4
    349 rdf:type schema:CreativeWork
    350 sg:pub.10.1186/gb-2010-11-10-r106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031289083
    351 https://doi.org/10.1186/gb-2010-11-10-r106
    352 rdf:type schema:CreativeWork
    353 sg:pub.10.1186/gb-2010-11-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050509557
    354 https://doi.org/10.1186/gb-2010-11-3-r25
    355 rdf:type schema:CreativeWork
    356 sg:pub.10.1186/gb-2011-12-6-r60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000243423
    357 https://doi.org/10.1186/gb-2011-12-6-r60
    358 rdf:type schema:CreativeWork
    359 sg:pub.10.1186/gb-2012-13-3-r23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036305770
    360 https://doi.org/10.1186/gb-2012-13-3-r23
    361 rdf:type schema:CreativeWork
    362 sg:pub.10.1186/gb-2012-13-9-r79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029450096
    363 https://doi.org/10.1186/gb-2012-13-9-r79
    364 rdf:type schema:CreativeWork
    365 sg:pub.10.1186/gb-2014-15-2-r29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045312009
    366 https://doi.org/10.1186/gb-2014-15-2-r29
    367 rdf:type schema:CreativeWork
    368 sg:pub.10.1186/s12859-016-1270-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013175693
    369 https://doi.org/10.1186/s12859-016-1270-8
    370 rdf:type schema:CreativeWork
    371 sg:pub.10.1186/s12859-016-1278-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037408767
    372 https://doi.org/10.1186/s12859-016-1278-0
    373 rdf:type schema:CreativeWork
    374 sg:pub.10.1186/s12859-016-1359-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015147157
    375 https://doi.org/10.1186/s12859-016-1359-0
    376 rdf:type schema:CreativeWork
    377 sg:pub.10.1186/s12859-016-1441-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019274698
    378 https://doi.org/10.1186/s12859-016-1441-7
    379 rdf:type schema:CreativeWork
    380 sg:pub.10.1186/s12864-016-2386-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1020096515
    381 https://doi.org/10.1186/s12864-016-2386-y
    382 rdf:type schema:CreativeWork
    383 sg:pub.10.1186/s12864-016-2964-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1016270288
    384 https://doi.org/10.1186/s12864-016-2964-z
    385 rdf:type schema:CreativeWork
    386 sg:pub.10.1186/s12866-015-0351-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001744296
    387 https://doi.org/10.1186/s12866-015-0351-6
    388 rdf:type schema:CreativeWork
    389 sg:pub.10.1186/s13059-014-0550-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015222646
    390 https://doi.org/10.1186/s13059-014-0550-8
    391 rdf:type schema:CreativeWork
    392 sg:pub.10.1186/s13059-015-0637-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019300544
    393 https://doi.org/10.1186/s13059-015-0637-x
    394 rdf:type schema:CreativeWork
    395 sg:pub.10.1186/s13059-015-0841-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052977184
    396 https://doi.org/10.1186/s13059-015-0841-8
    397 rdf:type schema:CreativeWork
    398 sg:pub.10.1186/s13059-016-0881-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041902478
    399 https://doi.org/10.1186/s13059-016-0881-8
    400 rdf:type schema:CreativeWork
    401 sg:pub.10.1186/s13059-016-0980-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000278738
    402 https://doi.org/10.1186/s13059-016-0980-6
    403 rdf:type schema:CreativeWork
    404 sg:pub.10.1186/s13059-016-1116-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011759184
    405 https://doi.org/10.1186/s13059-016-1116-8
    406 rdf:type schema:CreativeWork
    407 sg:pub.10.1186/s13059-017-1194-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084807339
    408 https://doi.org/10.1186/s13059-017-1194-2
    409 rdf:type schema:CreativeWork
    410 sg:pub.10.1186/s13073-015-0176-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032716746
    411 https://doi.org/10.1186/s13073-015-0176-9
    412 rdf:type schema:CreativeWork
    413 sg:pub.10.1186/s13073-016-0293-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005283564
    414 https://doi.org/10.1186/s13073-016-0293-0
    415 rdf:type schema:CreativeWork
    416 sg:pub.10.1186/s40168-015-0073-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031266707
    417 https://doi.org/10.1186/s40168-015-0073-x
    418 rdf:type schema:CreativeWork
    419 sg:pub.10.1186/s40168-015-0105-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011282839
    420 https://doi.org/10.1186/s40168-015-0105-6
    421 rdf:type schema:CreativeWork
    422 sg:pub.10.1186/s40168-016-0208-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019428991
    423 https://doi.org/10.1186/s40168-016-0208-8
    424 rdf:type schema:CreativeWork
    425 sg:pub.10.1186/s40168-017-0237-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1084252802
    426 https://doi.org/10.1186/s40168-017-0237-y
    427 rdf:type schema:CreativeWork
    428 grid-institutes:grid.29980.3a schema:alternateName Department of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand
    429 schema:name Department of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand
    430 rdf:type schema:Organization
    431 grid-institutes:grid.66859.34 schema:alternateName Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
    432 schema:name Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
    433 Department of Biostatistics, Harvard T.H. Chan School of Public Health, 02115, Boston, MA, USA
    434 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...