Species abundance information improves sequence taxonomy classification accuracy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-10-11

AUTHORS

Benjamin D. Kaehler, Nicholas A. Bokulich, Daniel McDonald, Rob Knight, J. Gregory Caporaso, Gavin A. Huttley

ABSTRACT

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments. More... »

PAGES

4643

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-019-12669-6

DOI

http://dx.doi.org/10.1038/s41467-019-12669-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1121649207

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31604942


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/2008/0502", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Science and Management", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Classification", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "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": "Phylogeny", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Population Density", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "School of Science, University of New South Wales, Canberra, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1005.4", 
          "name": [
            "Research School of Biology, Australian National University, Canberra, Australia", 
            "School of Science, University of New South Wales, Canberra, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kaehler", 
        "givenName": "Benjamin D.", 
        "id": "sg:person.01031324164.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01031324164.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA", 
          "id": "http://www.grid.ac/institutes/grid.261120.6", 
          "name": [
            "Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ USA", 
            "Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bokulich", 
        "givenName": "Nicholas A.", 
        "id": "sg:person.0577140345.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577140345.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Pediatrics, University of California San Diego, La Jolla, CA USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California San Diego, La Jolla, CA USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McDonald", 
        "givenName": "Daniel", 
        "id": "sg:person.01324411177.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324411177.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Microbiome Innovation, University of California San Diego, La Jolla, CA USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Pediatrics, University of California San Diego, La Jolla, CA USA", 
            "Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA", 
            "Center for Microbiome Innovation, University of California San Diego, 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"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA", 
          "id": "http://www.grid.ac/institutes/grid.261120.6", 
          "name": [
            "Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ USA", 
            "Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Caporaso", 
        "givenName": "J. Gregory", 
        "id": "sg:person.0624224157.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624224157.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Research School of Biology, Australian National University, Canberra, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1001.0", 
          "name": [
            "Research School of Biology, Australian National University, Canberra, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huttley", 
        "givenName": "Gavin A.", 
        "id": "sg:person.01247001327.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247001327.73"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/gb-2011-12-5-r50", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050643751", 
          "https://doi.org/10.1186/gb-2011-12-5-r50"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41587-019-0209-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1119809350", 
          "https://doi.org/10.1038/s41587-019-0209-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-13-s8-s17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019196236", 
          "https://doi.org/10.1186/1471-2164-13-s8-s17"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12864-015-2265-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051879010", 
          "https://doi.org/10.1186/s12864-015-2265-y"
        ], 
        "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.10.1038/s41564-017-0075-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092999920", 
          "https://doi.org/10.1038/s41564-017-0075-5"
        ], 
        "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.10.1186/s40168-018-0470-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104049103", 
          "https://doi.org/10.1186/s40168-018-0470-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature24621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092446313", 
          "https://doi.org/10.1038/nature24621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-16253-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092715039", 
          "https://doi.org/10.1038/s41598-017-16253-0"
        ], 
        "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/nmeth.3589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028162909", 
          "https://doi.org/10.1038/nmeth.3589"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2011.139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051863807", 
          "https://doi.org/10.1038/ismej.2011.139"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-10-11", 
    "datePublishedReg": "2019-10-11", 
    "description": "Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/s41467-019-12669-6", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5019008", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2439394", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7876585", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5019325", 
        "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": [
      "abundance information", 
      "species abundance information", 
      "amplicon sequences", 
      "species level", 
      "reference species", 
      "genus level", 
      "taxonomic classifiers", 
      "common environment", 
      "reference database", 
      "species", 
      "common sample type", 
      "sample types", 
      "sequence", 
      "levels", 
      "significant increase", 
      "straightforward alternative", 
      "environment", 
      "information", 
      "types", 
      "rate", 
      "increase", 
      "findings", 
      "database", 
      "degrades", 
      "degree", 
      "samples", 
      "most practical purposes", 
      "practical purposes", 
      "assumption", 
      "alternative", 
      "error rate", 
      "purpose", 
      "classification accuracy", 
      "average error rate", 
      "overall average error rate", 
      "classifier", 
      "classification accuracy degrades", 
      "accuracy", 
      "accuracy degrades", 
      "practice", 
      "Popular naive Bayes taxonomic classifiers", 
      "naive Bayes taxonomic classifiers", 
      "Bayes taxonomic classifiers", 
      "environment-specific taxonomic abundance information", 
      "taxonomic abundance information", 
      "species-level classification accuracy", 
      "q2-clawback", 
      "sequence taxonomy classification accuracy", 
      "taxonomy classification accuracy"
    ], 
    "name": "Species abundance information improves sequence taxonomy classification accuracy", 
    "pagination": "4643", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1121649207"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41467-019-12669-6"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "31604942"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41467-019-12669-6", 
      "https://app.dimensions.ai/details/publication/pub.1121649207"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:53", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_805.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/s41467-019-12669-6"
  }
]
 

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/s41467-019-12669-6'

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/s41467-019-12669-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-12669-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-12669-6'


 

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

251 TRIPLES      22 PREDICATES      96 URIs      75 LITERALS      15 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41467-019-12669-6 schema:about N2de73073bdc44d799f23fcf4a5865b17
2 N34f475781db34964af6ffb03a0696884
3 N4eac8dc68b2d4f91b60da738d55dabb8
4 N554af72f49ad45d98bd8f85cacfe43d1
5 N93d4422537204f7c8e07ad18c69f6e3e
6 N9d3be3e9bbed4d44b62e016d968edb83
7 Na167b37077d940b8971a54651d852aed
8 Nbca48508772540c1b3e35663b57f3d3b
9 anzsrc-for:05
10 anzsrc-for:0502
11 schema:author N9818795850f94ede8012520305915b16
12 schema:citation sg:pub.10.1038/ismej.2011.139
13 sg:pub.10.1038/nature11234
14 sg:pub.10.1038/nature24621
15 sg:pub.10.1038/nmeth.3589
16 sg:pub.10.1038/nmeth.3869
17 sg:pub.10.1038/s41564-017-0075-5
18 sg:pub.10.1038/s41587-019-0209-9
19 sg:pub.10.1038/s41592-018-0141-9
20 sg:pub.10.1038/s41598-017-16253-0
21 sg:pub.10.1186/1471-2164-13-s8-s17
22 sg:pub.10.1186/gb-2011-12-5-r50
23 sg:pub.10.1186/s12864-015-2265-y
24 sg:pub.10.1186/s40168-018-0470-z
25 schema:datePublished 2019-10-11
26 schema:datePublishedReg 2019-10-11
27 schema:description Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.
28 schema:genre article
29 schema:inLanguage en
30 schema:isAccessibleForFree true
31 schema:isPartOf Na1ef3bc91b2e43df9d8cb7891a28a541
32 Nc639e18184f1412b81778ea30b3fe4ae
33 sg:journal.1043282
34 schema:keywords Bayes taxonomic classifiers
35 Popular naive Bayes taxonomic classifiers
36 abundance information
37 accuracy
38 accuracy degrades
39 alternative
40 amplicon sequences
41 assumption
42 average error rate
43 classification accuracy
44 classification accuracy degrades
45 classifier
46 common environment
47 common sample type
48 database
49 degrades
50 degree
51 environment
52 environment-specific taxonomic abundance information
53 error rate
54 findings
55 genus level
56 increase
57 information
58 levels
59 most practical purposes
60 naive Bayes taxonomic classifiers
61 overall average error rate
62 practical purposes
63 practice
64 purpose
65 q2-clawback
66 rate
67 reference database
68 reference species
69 sample types
70 samples
71 sequence
72 sequence taxonomy classification accuracy
73 significant increase
74 species
75 species abundance information
76 species level
77 species-level classification accuracy
78 straightforward alternative
79 taxonomic abundance information
80 taxonomic classifiers
81 taxonomy classification accuracy
82 types
83 schema:name Species abundance information improves sequence taxonomy classification accuracy
84 schema:pagination 4643
85 schema:productId N9243e0908eee446c837175ba902037f6
86 Ndb817b4ed5394c8ab2db79fca3a3abf9
87 Nfd0cb81e1f8348d3b64af88e295719e6
88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1121649207
89 https://doi.org/10.1038/s41467-019-12669-6
90 schema:sdDatePublished 2022-01-01T18:53
91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
92 schema:sdPublisher Na1b5bd08a7f44b01b42bf642e1672913
93 schema:url https://doi.org/10.1038/s41467-019-12669-6
94 sgo:license sg:explorer/license/
95 sgo:sdDataset articles
96 rdf:type schema:ScholarlyArticle
97 N2c5a7953ab1d4b7089245aa75489eba2 rdf:first sg:person.0624224157.70
98 rdf:rest Nf506ba7151d04bbdbd23b49f7ffa3ce9
99 N2de73073bdc44d799f23fcf4a5865b17 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Bacteria
101 rdf:type schema:DefinedTerm
102 N34f475781db34964af6ffb03a0696884 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Metagenomics
104 rdf:type schema:DefinedTerm
105 N3eca875a38b840d2887e459bdcc83f00 rdf:first sg:person.016311745377.96
106 rdf:rest N2c5a7953ab1d4b7089245aa75489eba2
107 N4eac8dc68b2d4f91b60da738d55dabb8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Phylogeny
109 rdf:type schema:DefinedTerm
110 N554af72f49ad45d98bd8f85cacfe43d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Population Density
112 rdf:type schema:DefinedTerm
113 N7437ff9ffb99400da64c79685b90cdf7 rdf:first sg:person.01324411177.44
114 rdf:rest N3eca875a38b840d2887e459bdcc83f00
115 N9243e0908eee446c837175ba902037f6 schema:name doi
116 schema:value 10.1038/s41467-019-12669-6
117 rdf:type schema:PropertyValue
118 N93d4422537204f7c8e07ad18c69f6e3e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Microbiota
120 rdf:type schema:DefinedTerm
121 N9818795850f94ede8012520305915b16 rdf:first sg:person.01031324164.14
122 rdf:rest Nf22074719a23444fa9dc7fa56b412fa9
123 N9d3be3e9bbed4d44b62e016d968edb83 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Classification
125 rdf:type schema:DefinedTerm
126 Na167b37077d940b8971a54651d852aed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Software
128 rdf:type schema:DefinedTerm
129 Na1b5bd08a7f44b01b42bf642e1672913 schema:name Springer Nature - SN SciGraph project
130 rdf:type schema:Organization
131 Na1ef3bc91b2e43df9d8cb7891a28a541 schema:volumeNumber 10
132 rdf:type schema:PublicationVolume
133 Nbca48508772540c1b3e35663b57f3d3b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Computational Biology
135 rdf:type schema:DefinedTerm
136 Nc639e18184f1412b81778ea30b3fe4ae schema:issueNumber 1
137 rdf:type schema:PublicationIssue
138 Ndb817b4ed5394c8ab2db79fca3a3abf9 schema:name pubmed_id
139 schema:value 31604942
140 rdf:type schema:PropertyValue
141 Nf22074719a23444fa9dc7fa56b412fa9 rdf:first sg:person.0577140345.32
142 rdf:rest N7437ff9ffb99400da64c79685b90cdf7
143 Nf506ba7151d04bbdbd23b49f7ffa3ce9 rdf:first sg:person.01247001327.73
144 rdf:rest rdf:nil
145 Nfd0cb81e1f8348d3b64af88e295719e6 schema:name dimensions_id
146 schema:value pub.1121649207
147 rdf:type schema:PropertyValue
148 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
149 schema:name Environmental Sciences
150 rdf:type schema:DefinedTerm
151 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
152 schema:name Environmental Science and Management
153 rdf:type schema:DefinedTerm
154 sg:grant.2439394 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-12669-6
155 rdf:type schema:MonetaryGrant
156 sg:grant.5019008 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-12669-6
157 rdf:type schema:MonetaryGrant
158 sg:grant.5019325 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-12669-6
159 rdf:type schema:MonetaryGrant
160 sg:grant.7876585 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-12669-6
161 rdf:type schema:MonetaryGrant
162 sg:journal.1043282 schema:issn 2041-1723
163 schema:name Nature Communications
164 schema:publisher Springer Nature
165 rdf:type schema:Periodical
166 sg:person.01031324164.14 schema:affiliation grid-institutes:grid.1005.4
167 schema:familyName Kaehler
168 schema:givenName Benjamin D.
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01031324164.14
170 rdf:type schema:Person
171 sg:person.01247001327.73 schema:affiliation grid-institutes:grid.1001.0
172 schema:familyName Huttley
173 schema:givenName Gavin A.
174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247001327.73
175 rdf:type schema:Person
176 sg:person.01324411177.44 schema:affiliation grid-institutes:grid.266100.3
177 schema:familyName McDonald
178 schema:givenName Daniel
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324411177.44
180 rdf:type schema:Person
181 sg:person.016311745377.96 schema:affiliation grid-institutes:grid.266100.3
182 schema:familyName Knight
183 schema:givenName Rob
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311745377.96
185 rdf:type schema:Person
186 sg:person.0577140345.32 schema:affiliation grid-institutes:grid.261120.6
187 schema:familyName Bokulich
188 schema:givenName Nicholas A.
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577140345.32
190 rdf:type schema:Person
191 sg:person.0624224157.70 schema:affiliation grid-institutes:grid.261120.6
192 schema:familyName Caporaso
193 schema:givenName J. Gregory
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624224157.70
195 rdf:type schema:Person
196 sg:pub.10.1038/ismej.2011.139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051863807
197 https://doi.org/10.1038/ismej.2011.139
198 rdf:type schema:CreativeWork
199 sg:pub.10.1038/nature11234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007740093
200 https://doi.org/10.1038/nature11234
201 rdf:type schema:CreativeWork
202 sg:pub.10.1038/nature24621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092446313
203 https://doi.org/10.1038/nature24621
204 rdf:type schema:CreativeWork
205 sg:pub.10.1038/nmeth.3589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028162909
206 https://doi.org/10.1038/nmeth.3589
207 rdf:type schema:CreativeWork
208 sg:pub.10.1038/nmeth.3869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016631324
209 https://doi.org/10.1038/nmeth.3869
210 rdf:type schema:CreativeWork
211 sg:pub.10.1038/s41564-017-0075-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092999920
212 https://doi.org/10.1038/s41564-017-0075-5
213 rdf:type schema:CreativeWork
214 sg:pub.10.1038/s41587-019-0209-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1119809350
215 https://doi.org/10.1038/s41587-019-0209-9
216 rdf:type schema:CreativeWork
217 sg:pub.10.1038/s41592-018-0141-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107129414
218 https://doi.org/10.1038/s41592-018-0141-9
219 rdf:type schema:CreativeWork
220 sg:pub.10.1038/s41598-017-16253-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092715039
221 https://doi.org/10.1038/s41598-017-16253-0
222 rdf:type schema:CreativeWork
223 sg:pub.10.1186/1471-2164-13-s8-s17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019196236
224 https://doi.org/10.1186/1471-2164-13-s8-s17
225 rdf:type schema:CreativeWork
226 sg:pub.10.1186/gb-2011-12-5-r50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050643751
227 https://doi.org/10.1186/gb-2011-12-5-r50
228 rdf:type schema:CreativeWork
229 sg:pub.10.1186/s12864-015-2265-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1051879010
230 https://doi.org/10.1186/s12864-015-2265-y
231 rdf:type schema:CreativeWork
232 sg:pub.10.1186/s40168-018-0470-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1104049103
233 https://doi.org/10.1186/s40168-018-0470-z
234 rdf:type schema:CreativeWork
235 grid-institutes:grid.1001.0 schema:alternateName Research School of Biology, Australian National University, Canberra, Australia
236 schema:name Research School of Biology, Australian National University, Canberra, Australia
237 rdf:type schema:Organization
238 grid-institutes:grid.1005.4 schema:alternateName School of Science, University of New South Wales, Canberra, Australia
239 schema:name Research School of Biology, Australian National University, Canberra, Australia
240 School of Science, University of New South Wales, Canberra, Australia
241 rdf:type schema:Organization
242 grid-institutes:grid.261120.6 schema:alternateName Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA
243 schema:name Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ USA
244 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ USA
245 rdf:type schema:Organization
246 grid-institutes:grid.266100.3 schema:alternateName Center for Microbiome Innovation, University of California San Diego, La Jolla, CA USA
247 Department of Pediatrics, University of California San Diego, La Jolla, CA USA
248 schema:name Center for Microbiome Innovation, University of California San Diego, La Jolla, CA USA
249 Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
250 Department of Pediatrics, University of California San Diego, La Jolla, CA USA
251 rdf:type schema:Organization
 




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


...