Establishing microbial composition measurement standards with reference frames View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-06-20

AUTHORS

James T. Morton, Clarisse Marotz, Alex Washburne, Justin Silverman, Livia S. Zaramela, Anna Edlund, Karsten Zengler, Rob Knight

ABSTRACT

Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of “reference frames”, which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays. More... »

PAGES

2719

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-019-10656-5

DOI

http://dx.doi.org/10.1038/s41467-019-10656-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1117296456

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31222023


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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0605", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Microbiology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacterial Load", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Datasets as Topic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Dermatitis, Atopic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Feasibility Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Healthy Volunteers", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metagenome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Microbiota", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "RNA, Ribosomal, 16S", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reference Standards", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Saliva", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Soil Microbiology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science & Engineering, University of California, San Diego, 92093, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA", 
            "Department of Computer Science & Engineering, University of California, San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Morton", 
        "givenName": "James T.", 
        "id": "sg:person.07611006373.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07611006373.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marotz", 
        "givenName": "Clarisse", 
        "id": "sg:person.012733432355.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733432355.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Microbiology and Immunology, Montana State University, 59717, Bozeman, MT, USA", 
          "id": "http://www.grid.ac/institutes/grid.41891.35", 
          "name": [
            "Department of Microbiology and Immunology, Montana State University, 59717, Bozeman, MT, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Washburne", 
        "givenName": "Alex", 
        "id": "sg:person.012656123637.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012656123637.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Genomic and Computational Biology, Duke University, 27708, Durham, USA", 
          "id": "http://www.grid.ac/institutes/grid.26009.3d", 
          "name": [
            "Program in Computational Biology and Bioinformatics, Duke University, 27708, Durham, USA", 
            "Medical Scientist Training Program, Duke University, 27708, Durham, USA", 
            "Center for Genomic and Computational Biology, Duke University, 27708, Durham, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Silverman", 
        "givenName": "Justin", 
        "id": "sg:person.012060543237.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012060543237.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zaramela", 
        "givenName": "Livia S.", 
        "id": "sg:person.01144414650.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01144414650.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "J. Craig Venter Institute, Genomic Medicine Group, 92037, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.469946.0", 
          "name": [
            "J. Craig Venter Institute, Genomic Medicine Group, 92037, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Edlund", 
        "givenName": "Anna", 
        "id": "sg:person.01323423765.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323423765.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA", 
            "Department of Bioengineering, University of California, San Diego, 92093, La Jolla, CA, USA", 
            "Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zengler", 
        "givenName": "Karsten", 
        "id": "sg:person.01112011323.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01112011323.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA", 
            "Department of Computer Science & Engineering, University of California, San Diego, 92093, La Jolla, CA, USA", 
            "Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Knight", 
        "givenName": "Rob", 
        "id": "sg:person.016311745377.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311745377.96"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "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"
      }, 
      {
        "id": "sg:pub.10.1186/s40168-018-0584-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109820176", 
          "https://doi.org/10.1186/s40168-018-0584-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-7358-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022132261", 
          "https://doi.org/10.1007/978-94-015-7358-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12864-018-5160-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1108058115", 
          "https://doi.org/10.1186/s12864-018-5160-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature24460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092687907", 
          "https://doi.org/10.1038/nature24460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/2049-2618-2-15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046874717", 
          "https://doi.org/10.1186/2049-2618-2-15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40168-018-0601-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110232184", 
          "https://doi.org/10.1186/s40168-018-0601-6"
        ], 
        "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/s41592-018-0141-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107129414", 
          "https://doi.org/10.1038/s41592-018-0141-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40168-018-0491-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104992927", 
          "https://doi.org/10.1186/s40168-018-0491-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.f.303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009032055", 
          "https://doi.org/10.1038/nmeth.f.303"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/2047-217x-1-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050567563", 
          "https://doi.org/10.1186/2047-217x-1-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2016.117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036110439", 
          "https://doi.org/10.1038/ismej.2016.117"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-06-20", 
    "datePublishedReg": "2019-06-20", 
    "description": "Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of \u201creference frames\u201d, which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/s41467-019-10656-5", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5019008", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7909949", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3131567", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2439394", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1043282", 
        "issn": [
          "2041-1723"
        ], 
        "name": "Nature Communications", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "keywords": [
      "relative abundance data", 
      "abundance data", 
      "total microbial load", 
      "microbial changes", 
      "differential abundance analysis", 
      "abundant microbes", 
      "taxonomic shifts", 
      "sequencing output", 
      "relative abundance", 
      "microbiome research", 
      "time series experiments", 
      "microbial load", 
      "microbiome data", 
      "series experiments", 
      "abundance analysis", 
      "new assay", 
      "microbes", 
      "abundance", 
      "microorganisms", 
      "assays", 
      "frame", 
      "most studies", 
      "laborious measurements", 
      "changes", 
      "compositional nature", 
      "false positives", 
      "data", 
      "gold standard approach", 
      "shift", 
      "analysis", 
      "number", 
      "absolute number", 
      "deep intuition", 
      "consistent results", 
      "experiments", 
      "reassessment", 
      "dataset", 
      "study", 
      "common pitfalls", 
      "nature", 
      "results", 
      "positives", 
      "samples", 
      "approach", 
      "pitfalls", 
      "standard approach", 
      "research", 
      "need", 
      "atopic dermatitis", 
      "notion", 
      "method", 
      "output", 
      "dermatitis", 
      "load", 
      "measurements", 
      "solution", 
      "subjects", 
      "standards", 
      "intuition", 
      "measurement standards", 
      "reference frame"
    ], 
    "name": "Establishing microbial composition measurement standards with reference frames", 
    "pagination": "2719", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1117296456"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41467-019-10656-5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "31222023"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41467-019-10656-5", 
      "https://app.dimensions.ai/details/publication/pub.1117296456"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T17:07", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_818.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/s41467-019-10656-5"
  }
]
 

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.10.1038/s41467-019-10656-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-10656-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-10656-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-10656-5'


 

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

314 TRIPLES      21 PREDICATES      116 URIs      95 LITERALS      24 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41467-019-10656-5 schema:about N10119d7df8d54c468ddac33c1cfe271d
2 N328cd08976d24d1eb27315706811b35f
3 N4054132982ac43bb9771977340b6854c
4 N4474e19275544653b2c6a2e2e72ac708
5 N58c74e8a56914769b8f18cf421b83fbf
6 N58f0530894154fd782d5a6f321f4ff01
7 N58f10bc091ef4424950e1a237cab4cee
8 N5c9c3a19590f414db303cc0649c78dbd
9 N5fabdc13d09c4772b0e22eda76526afe
10 N804a2047d47b4fd39438ab89a9be98ff
11 N82859a7becca4dab9090ea6ed8097c53
12 N83e6a13a277a4926ac13ae493fdfb9a3
13 N95e25d97f6d94ff788e3c21def9aaabf
14 Na36b0d16b65d4e55adc01bce3f402e5f
15 Na9bed6cad128427c9fe66aca3cac0d98
16 Naa47e1fc109440acbb35d8d6d08dcfce
17 Ne27c10221fb948a893ed7dc041169819
18 anzsrc-for:06
19 anzsrc-for:0605
20 schema:author N780024a301734327905635f925de7cfc
21 schema:citation sg:pub.10.1007/978-94-015-7358-0
22 sg:pub.10.1038/ismej.2016.117
23 sg:pub.10.1038/nature24460
24 sg:pub.10.1038/nmeth.2658
25 sg:pub.10.1038/nmeth.f.303
26 sg:pub.10.1038/s41592-018-0141-9
27 sg:pub.10.1186/2047-217x-1-7
28 sg:pub.10.1186/2049-2618-2-15
29 sg:pub.10.1186/s12864-018-5160-5
30 sg:pub.10.1186/s13059-014-0550-8
31 sg:pub.10.1186/s40168-018-0491-7
32 sg:pub.10.1186/s40168-018-0584-3
33 sg:pub.10.1186/s40168-018-0601-6
34 schema:datePublished 2019-06-20
35 schema:datePublishedReg 2019-06-20
36 schema:description Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of “reference frames”, which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays.
37 schema:genre article
38 schema:isAccessibleForFree true
39 schema:isPartOf N7cbe79584e04496db90aa60de39d711f
40 Nc169d1496e2c4e1b9434464f456f234e
41 sg:journal.1043282
42 schema:keywords absolute number
43 abundance
44 abundance analysis
45 abundance data
46 abundant microbes
47 analysis
48 approach
49 assays
50 atopic dermatitis
51 changes
52 common pitfalls
53 compositional nature
54 consistent results
55 data
56 dataset
57 deep intuition
58 dermatitis
59 differential abundance analysis
60 experiments
61 false positives
62 frame
63 gold standard approach
64 intuition
65 laborious measurements
66 load
67 measurement standards
68 measurements
69 method
70 microbes
71 microbial changes
72 microbial load
73 microbiome data
74 microbiome research
75 microorganisms
76 most studies
77 nature
78 need
79 new assay
80 notion
81 number
82 output
83 pitfalls
84 positives
85 reassessment
86 reference frame
87 relative abundance
88 relative abundance data
89 research
90 results
91 samples
92 sequencing output
93 series experiments
94 shift
95 solution
96 standard approach
97 standards
98 study
99 subjects
100 taxonomic shifts
101 time series experiments
102 total microbial load
103 schema:name Establishing microbial composition measurement standards with reference frames
104 schema:pagination 2719
105 schema:productId N0041869eecb94be7a6b6e185e1a8b9f8
106 N72c7cffdae414fe1a886dc7a4453b04e
107 Nd7637e89096843a8bfde2644d552023d
108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1117296456
109 https://doi.org/10.1038/s41467-019-10656-5
110 schema:sdDatePublished 2022-08-04T17:07
111 schema:sdLicense https://scigraph.springernature.com/explorer/license/
112 schema:sdPublisher N48f7eff1540b4315bf320234c6a7abc9
113 schema:url https://doi.org/10.1038/s41467-019-10656-5
114 sgo:license sg:explorer/license/
115 sgo:sdDataset articles
116 rdf:type schema:ScholarlyArticle
117 N0041869eecb94be7a6b6e185e1a8b9f8 schema:name pubmed_id
118 schema:value 31222023
119 rdf:type schema:PropertyValue
120 N10119d7df8d54c468ddac33c1cfe271d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name High-Throughput Nucleotide Sequencing
122 rdf:type schema:DefinedTerm
123 N2da03c14425a4103a543c4fe7a114ce6 rdf:first sg:person.012656123637.25
124 rdf:rest Na3a5b6d5d01f4d79893a4bc2a5b19560
125 N328cd08976d24d1eb27315706811b35f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Datasets as Topic
127 rdf:type schema:DefinedTerm
128 N4054132982ac43bb9771977340b6854c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Humans
130 rdf:type schema:DefinedTerm
131 N43138463764446e1aeca11b5750047da rdf:first sg:person.016311745377.96
132 rdf:rest rdf:nil
133 N4474e19275544653b2c6a2e2e72ac708 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Computer Simulation
135 rdf:type schema:DefinedTerm
136 N48f7eff1540b4315bf320234c6a7abc9 schema:name Springer Nature - SN SciGraph project
137 rdf:type schema:Organization
138 N4e04db11dd2246fdb1770b193e53959a rdf:first sg:person.01323423765.55
139 rdf:rest N6c84dd8d94f64f588c443b85363db977
140 N58c74e8a56914769b8f18cf421b83fbf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Data Analysis
142 rdf:type schema:DefinedTerm
143 N58f0530894154fd782d5a6f321f4ff01 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Feasibility Studies
145 rdf:type schema:DefinedTerm
146 N58f10bc091ef4424950e1a237cab4cee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name RNA, Ribosomal, 16S
148 rdf:type schema:DefinedTerm
149 N5c9c3a19590f414db303cc0649c78dbd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Dermatitis, Atopic
151 rdf:type schema:DefinedTerm
152 N5fabdc13d09c4772b0e22eda76526afe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Reference Standards
154 rdf:type schema:DefinedTerm
155 N65fd52ba7a0b42b3b10a99a34a24efd4 rdf:first sg:person.012733432355.50
156 rdf:rest N2da03c14425a4103a543c4fe7a114ce6
157 N6c84dd8d94f64f588c443b85363db977 rdf:first sg:person.01112011323.94
158 rdf:rest N43138463764446e1aeca11b5750047da
159 N72c7cffdae414fe1a886dc7a4453b04e schema:name dimensions_id
160 schema:value pub.1117296456
161 rdf:type schema:PropertyValue
162 N780024a301734327905635f925de7cfc rdf:first sg:person.07611006373.15
163 rdf:rest N65fd52ba7a0b42b3b10a99a34a24efd4
164 N7cbe79584e04496db90aa60de39d711f schema:volumeNumber 10
165 rdf:type schema:PublicationVolume
166 N804a2047d47b4fd39438ab89a9be98ff schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Models, Biological
168 rdf:type schema:DefinedTerm
169 N82859a7becca4dab9090ea6ed8097c53 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Metagenome
171 rdf:type schema:DefinedTerm
172 N83e6a13a277a4926ac13ae493fdfb9a3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Saliva
174 rdf:type schema:DefinedTerm
175 N95e25d97f6d94ff788e3c21def9aaabf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
176 schema:name Bacteria
177 rdf:type schema:DefinedTerm
178 Na36b0d16b65d4e55adc01bce3f402e5f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Healthy Volunteers
180 rdf:type schema:DefinedTerm
181 Na3a5b6d5d01f4d79893a4bc2a5b19560 rdf:first sg:person.012060543237.43
182 rdf:rest Nc954352d0bf349ecb178a6b6c5e6e788
183 Na9bed6cad128427c9fe66aca3cac0d98 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
184 schema:name Soil Microbiology
185 rdf:type schema:DefinedTerm
186 Naa47e1fc109440acbb35d8d6d08dcfce schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
187 schema:name Microbiota
188 rdf:type schema:DefinedTerm
189 Nc169d1496e2c4e1b9434464f456f234e schema:issueNumber 1
190 rdf:type schema:PublicationIssue
191 Nc954352d0bf349ecb178a6b6c5e6e788 rdf:first sg:person.01144414650.58
192 rdf:rest N4e04db11dd2246fdb1770b193e53959a
193 Nd7637e89096843a8bfde2644d552023d schema:name doi
194 schema:value 10.1038/s41467-019-10656-5
195 rdf:type schema:PropertyValue
196 Ne27c10221fb948a893ed7dc041169819 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
197 schema:name Bacterial Load
198 rdf:type schema:DefinedTerm
199 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
200 schema:name Biological Sciences
201 rdf:type schema:DefinedTerm
202 anzsrc-for:0605 schema:inDefinedTermSet anzsrc-for:
203 schema:name Microbiology
204 rdf:type schema:DefinedTerm
205 sg:grant.2439394 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-10656-5
206 rdf:type schema:MonetaryGrant
207 sg:grant.3131567 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-10656-5
208 rdf:type schema:MonetaryGrant
209 sg:grant.5019008 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-10656-5
210 rdf:type schema:MonetaryGrant
211 sg:grant.7909949 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-10656-5
212 rdf:type schema:MonetaryGrant
213 sg:journal.1043282 schema:issn 2041-1723
214 schema:name Nature Communications
215 schema:publisher Springer Nature
216 rdf:type schema:Periodical
217 sg:person.01112011323.94 schema:affiliation grid-institutes:grid.266100.3
218 schema:familyName Zengler
219 schema:givenName Karsten
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01112011323.94
221 rdf:type schema:Person
222 sg:person.01144414650.58 schema:affiliation grid-institutes:grid.266100.3
223 schema:familyName Zaramela
224 schema:givenName Livia S.
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01144414650.58
226 rdf:type schema:Person
227 sg:person.012060543237.43 schema:affiliation grid-institutes:grid.26009.3d
228 schema:familyName Silverman
229 schema:givenName Justin
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012060543237.43
231 rdf:type schema:Person
232 sg:person.012656123637.25 schema:affiliation grid-institutes:grid.41891.35
233 schema:familyName Washburne
234 schema:givenName Alex
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012656123637.25
236 rdf:type schema:Person
237 sg:person.012733432355.50 schema:affiliation grid-institutes:grid.266100.3
238 schema:familyName Marotz
239 schema:givenName Clarisse
240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733432355.50
241 rdf:type schema:Person
242 sg:person.01323423765.55 schema:affiliation grid-institutes:grid.469946.0
243 schema:familyName Edlund
244 schema:givenName Anna
245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323423765.55
246 rdf:type schema:Person
247 sg:person.016311745377.96 schema:affiliation grid-institutes:grid.266100.3
248 schema:familyName Knight
249 schema:givenName Rob
250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311745377.96
251 rdf:type schema:Person
252 sg:person.07611006373.15 schema:affiliation grid-institutes:grid.266100.3
253 schema:familyName Morton
254 schema:givenName James T.
255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07611006373.15
256 rdf:type schema:Person
257 sg:pub.10.1007/978-94-015-7358-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022132261
258 https://doi.org/10.1007/978-94-015-7358-0
259 rdf:type schema:CreativeWork
260 sg:pub.10.1038/ismej.2016.117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036110439
261 https://doi.org/10.1038/ismej.2016.117
262 rdf:type schema:CreativeWork
263 sg:pub.10.1038/nature24460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092687907
264 https://doi.org/10.1038/nature24460
265 rdf:type schema:CreativeWork
266 sg:pub.10.1038/nmeth.2658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002139060
267 https://doi.org/10.1038/nmeth.2658
268 rdf:type schema:CreativeWork
269 sg:pub.10.1038/nmeth.f.303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009032055
270 https://doi.org/10.1038/nmeth.f.303
271 rdf:type schema:CreativeWork
272 sg:pub.10.1038/s41592-018-0141-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107129414
273 https://doi.org/10.1038/s41592-018-0141-9
274 rdf:type schema:CreativeWork
275 sg:pub.10.1186/2047-217x-1-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050567563
276 https://doi.org/10.1186/2047-217x-1-7
277 rdf:type schema:CreativeWork
278 sg:pub.10.1186/2049-2618-2-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046874717
279 https://doi.org/10.1186/2049-2618-2-15
280 rdf:type schema:CreativeWork
281 sg:pub.10.1186/s12864-018-5160-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1108058115
282 https://doi.org/10.1186/s12864-018-5160-5
283 rdf:type schema:CreativeWork
284 sg:pub.10.1186/s13059-014-0550-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015222646
285 https://doi.org/10.1186/s13059-014-0550-8
286 rdf:type schema:CreativeWork
287 sg:pub.10.1186/s40168-018-0491-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104992927
288 https://doi.org/10.1186/s40168-018-0491-7
289 rdf:type schema:CreativeWork
290 sg:pub.10.1186/s40168-018-0584-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109820176
291 https://doi.org/10.1186/s40168-018-0584-3
292 rdf:type schema:CreativeWork
293 sg:pub.10.1186/s40168-018-0601-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110232184
294 https://doi.org/10.1186/s40168-018-0601-6
295 rdf:type schema:CreativeWork
296 grid-institutes:grid.26009.3d schema:alternateName Center for Genomic and Computational Biology, Duke University, 27708, Durham, USA
297 schema:name Center for Genomic and Computational Biology, Duke University, 27708, Durham, USA
298 Medical Scientist Training Program, Duke University, 27708, Durham, USA
299 Program in Computational Biology and Bioinformatics, Duke University, 27708, Durham, USA
300 rdf:type schema:Organization
301 grid-institutes:grid.266100.3 schema:alternateName Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA
302 Department of Computer Science & Engineering, University of California, San Diego, 92093, La Jolla, CA, USA
303 Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA
304 schema:name Center for Microbiome Innovation, University of California, San Diego, 92093, La Jolla, CA, USA
305 Department of Bioengineering, University of California, San Diego, 92093, La Jolla, CA, USA
306 Department of Computer Science & Engineering, University of California, San Diego, 92093, La Jolla, CA, USA
307 Department of Pediatrics, University of California, San Diego, 92093, La Jolla, CA, USA
308 rdf:type schema:Organization
309 grid-institutes:grid.41891.35 schema:alternateName Department of Microbiology and Immunology, Montana State University, 59717, Bozeman, MT, USA
310 schema:name Department of Microbiology and Immunology, Montana State University, 59717, Bozeman, MT, USA
311 rdf:type schema:Organization
312 grid-institutes:grid.469946.0 schema:alternateName J. Craig Venter Institute, Genomic Medicine Group, 92037, La Jolla, CA, USA
313 schema:name J. Craig Venter Institute, Genomic Medicine Group, 92037, La Jolla, CA, USA
314 rdf:type schema:Organization
 




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


...