Improving saliva shotgun metagenomics by chemical host DNA depletion View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-02-27

AUTHORS

Clarisse A. Marotz, Jon G. Sanders, Cristal Zuniga, Livia S. Zaramela, Rob Knight, Karsten Zengler

ABSTRACT

BackgroundShotgun sequencing of microbial communities provides in-depth knowledge of the microbiome by cataloging bacterial, fungal, and viral gene content within a sample, providing an advantage over amplicon sequencing approaches that assess taxonomy but not function and are taxonomically limited. However, mammalian DNA can dominate host-derived samples, obscuring changes in microbial populations because few DNA sequence reads are from the microbial component. We developed and optimized a novel method for enriching microbial DNA from human oral samples and compared its efficiency and potential taxonomic bias with commercially available kits.ResultsThree commercially available host depletion kits were directly compared with size filtration and a novel method involving osmotic lysis and treatment with propidium monoazide (lyPMA) in human saliva samples. We evaluated the percentage of shotgun metagenomic sequencing reads aligning to the human genome, and taxonomic biases of those not aligning, compared to untreated samples. lyPMA was the most efficient method of removing host-derived sequencing reads compared to untreated sample (8.53 ± 0.10% versus 89.29 ± 0.03%). Furthermore, lyPMA-treated samples exhibit the lowest taxonomic bias compared to untreated samples.ConclusionOsmotic lysis followed by PMA treatment is a cost-effective, rapid, and robust method for enriching microbial sequence data in shotgun metagenomics from fresh and frozen saliva samples and may be extensible to other host-derived sample types. More... »

PAGES

42

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40168-018-0426-3

DOI

http://dx.doi.org/10.1186/s40168-018-0426-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101229060

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29482639


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "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": "Azides", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fungi", 
        "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": "Metagenomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Microbiota", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Propidium", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Saliva", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Viruses", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "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": "Marotz", 
        "givenName": "Clarisse A.", 
        "id": "sg:person.012733432355.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733432355.50"
        ], 
        "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": "Sanders", 
        "givenName": "Jon G.", 
        "id": "sg:person.0763606526.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763606526.69"
        ], 
        "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": "Zuniga", 
        "givenName": "Cristal", 
        "id": "sg:person.01341525662.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341525662.10"
        ], 
        "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": "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": "Department of Computer Science and Engineering, 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", 
            "Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA", 
            "Department of Computer Science and Engineering, 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": "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", 
            "Center for Microbiome Innovation, University of California San Diego, 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"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/ncomms14306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074207832", 
          "https://doi.org/10.1038/ncomms14306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13059-016-0904-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014078914", 
          "https://doi.org/10.1186/s13059-016-0904-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2009-10-3-r25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049583368", 
          "https://doi.org/10.1186/gb-2009-10-3-r25"
        ], 
        "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.10.1007/978-1-60327-353-4_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037079166", 
          "https://doi.org/10.1007/978-1-60327-353-4_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12223-015-0400-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037638837", 
          "https://doi.org/10.1007/s12223-015-0400-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00784-014-1297-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034527508", 
          "https://doi.org/10.1007/s00784-014-1297-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmicrobiol.2016.242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043217898", 
          "https://doi.org/10.1038/nmicrobiol.2016.242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1472-6750-11-124", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002481127", 
          "https://doi.org/10.1186/1472-6750-11-124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2010.133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042916034", 
          "https://doi.org/10.1038/ismej.2010.133"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-02-27", 
    "datePublishedReg": "2018-02-27", 
    "description": "BackgroundShotgun sequencing of microbial communities provides in-depth knowledge of the microbiome by cataloging bacterial, fungal, and viral gene content within a sample, providing an advantage over amplicon sequencing approaches that assess taxonomy but not function and are taxonomically limited. However, mammalian DNA can dominate host-derived samples, obscuring changes in microbial populations because few DNA sequence reads are from the microbial component. We developed and optimized a novel method for enriching microbial DNA from human oral samples and compared its efficiency and potential taxonomic bias with commercially available kits.ResultsThree commercially available host depletion kits were directly compared with size filtration and a novel method involving osmotic lysis and treatment with propidium monoazide (lyPMA) in human saliva samples. We evaluated the percentage of shotgun metagenomic sequencing reads aligning to the human genome, and taxonomic biases of those not aligning, compared to untreated samples. lyPMA was the most efficient method of removing host-derived sequencing reads compared to untreated sample (8.53\u2009\u00b1\u20090.10% versus 89.29\u2009\u00b1\u20090.03%). Furthermore, lyPMA-treated samples exhibit the lowest taxonomic bias compared to untreated samples.ConclusionOsmotic lysis followed by PMA treatment is a cost-effective, rapid, and robust method for enriching microbial sequence data in shotgun metagenomics from fresh and frozen saliva samples and may be extensible to other host-derived sample types.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s40168-018-0426-3", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6618224", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1048878", 
        "issn": [
          "2049-2618"
        ], 
        "name": "Microbiome", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "keywords": [
      "taxonomic bias", 
      "shotgun metagenomics", 
      "sequencing reads", 
      "DNA sequence reads", 
      "microbial sequence data", 
      "amplicon sequencing approach", 
      "human oral samples", 
      "metagenomic sequencing reads", 
      "host DNA depletion", 
      "gene content", 
      "microbial communities", 
      "human genome", 
      "taxonomic biases", 
      "mammalian DNA", 
      "sequence data", 
      "sequence reads", 
      "frozen saliva samples", 
      "sequencing approach", 
      "microbial DNA", 
      "microbial populations", 
      "microbial components", 
      "PMA treatment", 
      "DNA depletion", 
      "metagenomics", 
      "osmotic lysis", 
      "reads", 
      "DNA", 
      "size filtration", 
      "propidium monoazide", 
      "genome", 
      "fungal", 
      "sequencing", 
      "lysis", 
      "microbiome", 
      "sample types", 
      "taxonomy", 
      "oral samples", 
      "available kits", 
      "monoazide", 
      "depth knowledge", 
      "depletion", 
      "population", 
      "community", 
      "kit", 
      "function", 
      "robust method", 
      "untreated samples", 
      "novel method", 
      "components", 
      "changes", 
      "efficient method", 
      "content", 
      "samples", 
      "filtration", 
      "types", 
      "human saliva samples", 
      "treatment", 
      "knowledge", 
      "data", 
      "percentage", 
      "biases", 
      "approach", 
      "bias", 
      "ResultsThree", 
      "saliva samples", 
      "method", 
      "efficiency", 
      "advantages"
    ], 
    "name": "Improving saliva shotgun metagenomics by chemical host DNA depletion", 
    "pagination": "42", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101229060"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40168-018-0426-3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29482639"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40168-018-0426-3", 
      "https://app.dimensions.ai/details/publication/pub.1101229060"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_787.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s40168-018-0426-3"
  }
]
 

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/s40168-018-0426-3'

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/s40168-018-0426-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40168-018-0426-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40168-018-0426-3'


 

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

261 TRIPLES      21 PREDICATES      116 URIs      97 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40168-018-0426-3 schema:about N02db3f978d494f24a544badef24b3df2
2 N21e0fb71b7d04272ae20b5b1a1bf29f2
3 N2ef9cdc799df4f8a85bae62bb0f14959
4 N3601cd1f864f41a6a30bb2b4fbbc4208
5 N3eacf6e9eecf49aea934071b6be3971c
6 N466b6a37268144668c6da449f5474f63
7 N6fc220c7a4134bf1b04686c8aee56904
8 N73c55ab960644bd39fe6282c108e82f7
9 N79f4c6441f944e11bc94ab2c7420d56a
10 N8c499870658d40afaf41d132a9c7d5b1
11 N97f63ade007c42d29d1b1ab2240d9ccf
12 Ne5d6d10a3f5f4e65bdb5fb23301617eb
13 anzsrc-for:06
14 anzsrc-for:0604
15 anzsrc-for:0605
16 schema:author N75707eb5789444039ccf423974d90285
17 schema:citation sg:pub.10.1007/978-1-60327-353-4_4
18 sg:pub.10.1007/s00784-014-1297-z
19 sg:pub.10.1007/s12223-015-0400-4
20 sg:pub.10.1038/ismej.2010.133
21 sg:pub.10.1038/ncomms14306
22 sg:pub.10.1038/nmeth.3589
23 sg:pub.10.1038/nmicrobiol.2016.242
24 sg:pub.10.1186/1472-6750-11-124
25 sg:pub.10.1186/gb-2009-10-3-r25
26 sg:pub.10.1186/s13059-016-0904-5
27 schema:datePublished 2018-02-27
28 schema:datePublishedReg 2018-02-27
29 schema:description BackgroundShotgun sequencing of microbial communities provides in-depth knowledge of the microbiome by cataloging bacterial, fungal, and viral gene content within a sample, providing an advantage over amplicon sequencing approaches that assess taxonomy but not function and are taxonomically limited. However, mammalian DNA can dominate host-derived samples, obscuring changes in microbial populations because few DNA sequence reads are from the microbial component. We developed and optimized a novel method for enriching microbial DNA from human oral samples and compared its efficiency and potential taxonomic bias with commercially available kits.ResultsThree commercially available host depletion kits were directly compared with size filtration and a novel method involving osmotic lysis and treatment with propidium monoazide (lyPMA) in human saliva samples. We evaluated the percentage of shotgun metagenomic sequencing reads aligning to the human genome, and taxonomic biases of those not aligning, compared to untreated samples. lyPMA was the most efficient method of removing host-derived sequencing reads compared to untreated sample (8.53 ± 0.10% versus 89.29 ± 0.03%). Furthermore, lyPMA-treated samples exhibit the lowest taxonomic bias compared to untreated samples.ConclusionOsmotic lysis followed by PMA treatment is a cost-effective, rapid, and robust method for enriching microbial sequence data in shotgun metagenomics from fresh and frozen saliva samples and may be extensible to other host-derived sample types.
30 schema:genre article
31 schema:isAccessibleForFree true
32 schema:isPartOf N5dae1f4609064a4e9695b276c86c04ae
33 Ne77d178f7ac44613a6417e9b90d6a0a6
34 sg:journal.1048878
35 schema:keywords DNA
36 DNA depletion
37 DNA sequence reads
38 PMA treatment
39 ResultsThree
40 advantages
41 amplicon sequencing approach
42 approach
43 available kits
44 bias
45 biases
46 changes
47 community
48 components
49 content
50 data
51 depletion
52 depth knowledge
53 efficiency
54 efficient method
55 filtration
56 frozen saliva samples
57 function
58 fungal
59 gene content
60 genome
61 host DNA depletion
62 human genome
63 human oral samples
64 human saliva samples
65 kit
66 knowledge
67 lysis
68 mammalian DNA
69 metagenomic sequencing reads
70 metagenomics
71 method
72 microbial DNA
73 microbial communities
74 microbial components
75 microbial populations
76 microbial sequence data
77 microbiome
78 monoazide
79 novel method
80 oral samples
81 osmotic lysis
82 percentage
83 population
84 propidium monoazide
85 reads
86 robust method
87 saliva samples
88 sample types
89 samples
90 sequence data
91 sequence reads
92 sequencing
93 sequencing approach
94 sequencing reads
95 shotgun metagenomics
96 size filtration
97 taxonomic bias
98 taxonomic biases
99 taxonomy
100 treatment
101 types
102 untreated samples
103 schema:name Improving saliva shotgun metagenomics by chemical host DNA depletion
104 schema:pagination 42
105 schema:productId N3d542aac9c404bb48d310ba6cffd4539
106 N772a4536cedc43c59a03818df50ebfb6
107 Ndea02b3f959f412899e06088093c503e
108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101229060
109 https://doi.org/10.1186/s40168-018-0426-3
110 schema:sdDatePublished 2022-11-24T21:04
111 schema:sdLicense https://scigraph.springernature.com/explorer/license/
112 schema:sdPublisher Nba4d1de7d7884713ad7bf5be76015fb5
113 schema:url https://doi.org/10.1186/s40168-018-0426-3
114 sgo:license sg:explorer/license/
115 sgo:sdDataset articles
116 rdf:type schema:ScholarlyArticle
117 N02db3f978d494f24a544badef24b3df2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Humans
119 rdf:type schema:DefinedTerm
120 N20bcae4b99fa47ba999912dd3d9ea498 rdf:first sg:person.0763606526.69
121 rdf:rest Nf2ffb19b1356468c8674ab3890fd4e0a
122 N21e0fb71b7d04272ae20b5b1a1bf29f2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Microbiota
124 rdf:type schema:DefinedTerm
125 N2ef9cdc799df4f8a85bae62bb0f14959 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name DNA
127 rdf:type schema:DefinedTerm
128 N3601cd1f864f41a6a30bb2b4fbbc4208 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Saliva
130 rdf:type schema:DefinedTerm
131 N3d542aac9c404bb48d310ba6cffd4539 schema:name pubmed_id
132 schema:value 29482639
133 rdf:type schema:PropertyValue
134 N3eacf6e9eecf49aea934071b6be3971c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Sequence Analysis, DNA
136 rdf:type schema:DefinedTerm
137 N466b6a37268144668c6da449f5474f63 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Azides
139 rdf:type schema:DefinedTerm
140 N5dae1f4609064a4e9695b276c86c04ae schema:issueNumber 1
141 rdf:type schema:PublicationIssue
142 N6fc220c7a4134bf1b04686c8aee56904 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Viruses
144 rdf:type schema:DefinedTerm
145 N73c55ab960644bd39fe6282c108e82f7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Bacteria
147 rdf:type schema:DefinedTerm
148 N75707eb5789444039ccf423974d90285 rdf:first sg:person.012733432355.50
149 rdf:rest N20bcae4b99fa47ba999912dd3d9ea498
150 N772a4536cedc43c59a03818df50ebfb6 schema:name doi
151 schema:value 10.1186/s40168-018-0426-3
152 rdf:type schema:PropertyValue
153 N7746003e31d543af8aced127651700b6 rdf:first sg:person.01112011323.94
154 rdf:rest rdf:nil
155 N79f4c6441f944e11bc94ab2c7420d56a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Fungi
157 rdf:type schema:DefinedTerm
158 N8c499870658d40afaf41d132a9c7d5b1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Propidium
160 rdf:type schema:DefinedTerm
161 N97f63ade007c42d29d1b1ab2240d9ccf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Metagenomics
163 rdf:type schema:DefinedTerm
164 Nba4d1de7d7884713ad7bf5be76015fb5 schema:name Springer Nature - SN SciGraph project
165 rdf:type schema:Organization
166 Ncfd72265e1a946a6b6cd1c4ad1f0c8fd rdf:first sg:person.01144414650.58
167 rdf:rest Nfff6b65af7ca44c594766de963f663b7
168 Ndea02b3f959f412899e06088093c503e schema:name dimensions_id
169 schema:value pub.1101229060
170 rdf:type schema:PropertyValue
171 Ne5d6d10a3f5f4e65bdb5fb23301617eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name High-Throughput Nucleotide Sequencing
173 rdf:type schema:DefinedTerm
174 Ne77d178f7ac44613a6417e9b90d6a0a6 schema:volumeNumber 6
175 rdf:type schema:PublicationVolume
176 Nf2ffb19b1356468c8674ab3890fd4e0a rdf:first sg:person.01341525662.10
177 rdf:rest Ncfd72265e1a946a6b6cd1c4ad1f0c8fd
178 Nfff6b65af7ca44c594766de963f663b7 rdf:first sg:person.016311745377.96
179 rdf:rest N7746003e31d543af8aced127651700b6
180 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
181 schema:name Biological Sciences
182 rdf:type schema:DefinedTerm
183 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
184 schema:name Genetics
185 rdf:type schema:DefinedTerm
186 anzsrc-for:0605 schema:inDefinedTermSet anzsrc-for:
187 schema:name Microbiology
188 rdf:type schema:DefinedTerm
189 sg:grant.6618224 http://pending.schema.org/fundedItem sg:pub.10.1186/s40168-018-0426-3
190 rdf:type schema:MonetaryGrant
191 sg:journal.1048878 schema:issn 2049-2618
192 schema:name Microbiome
193 schema:publisher Springer Nature
194 rdf:type schema:Periodical
195 sg:person.01112011323.94 schema:affiliation grid-institutes:grid.266100.3
196 schema:familyName Zengler
197 schema:givenName Karsten
198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01112011323.94
199 rdf:type schema:Person
200 sg:person.01144414650.58 schema:affiliation grid-institutes:grid.266100.3
201 schema:familyName Zaramela
202 schema:givenName Livia S.
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01144414650.58
204 rdf:type schema:Person
205 sg:person.012733432355.50 schema:affiliation grid-institutes:grid.266100.3
206 schema:familyName Marotz
207 schema:givenName Clarisse A.
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733432355.50
209 rdf:type schema:Person
210 sg:person.01341525662.10 schema:affiliation grid-institutes:grid.266100.3
211 schema:familyName Zuniga
212 schema:givenName Cristal
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341525662.10
214 rdf:type schema:Person
215 sg:person.016311745377.96 schema:affiliation grid-institutes:grid.266100.3
216 schema:familyName Knight
217 schema:givenName Rob
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311745377.96
219 rdf:type schema:Person
220 sg:person.0763606526.69 schema:affiliation grid-institutes:grid.266100.3
221 schema:familyName Sanders
222 schema:givenName Jon G.
223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763606526.69
224 rdf:type schema:Person
225 sg:pub.10.1007/978-1-60327-353-4_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037079166
226 https://doi.org/10.1007/978-1-60327-353-4_4
227 rdf:type schema:CreativeWork
228 sg:pub.10.1007/s00784-014-1297-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1034527508
229 https://doi.org/10.1007/s00784-014-1297-z
230 rdf:type schema:CreativeWork
231 sg:pub.10.1007/s12223-015-0400-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037638837
232 https://doi.org/10.1007/s12223-015-0400-4
233 rdf:type schema:CreativeWork
234 sg:pub.10.1038/ismej.2010.133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042916034
235 https://doi.org/10.1038/ismej.2010.133
236 rdf:type schema:CreativeWork
237 sg:pub.10.1038/ncomms14306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074207832
238 https://doi.org/10.1038/ncomms14306
239 rdf:type schema:CreativeWork
240 sg:pub.10.1038/nmeth.3589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028162909
241 https://doi.org/10.1038/nmeth.3589
242 rdf:type schema:CreativeWork
243 sg:pub.10.1038/nmicrobiol.2016.242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043217898
244 https://doi.org/10.1038/nmicrobiol.2016.242
245 rdf:type schema:CreativeWork
246 sg:pub.10.1186/1472-6750-11-124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002481127
247 https://doi.org/10.1186/1472-6750-11-124
248 rdf:type schema:CreativeWork
249 sg:pub.10.1186/gb-2009-10-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049583368
250 https://doi.org/10.1186/gb-2009-10-3-r25
251 rdf:type schema:CreativeWork
252 sg:pub.10.1186/s13059-016-0904-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014078914
253 https://doi.org/10.1186/s13059-016-0904-5
254 rdf:type schema:CreativeWork
255 grid-institutes:grid.266100.3 schema:alternateName Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
256 Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
257 Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
258 schema:name Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
259 Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
260 Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
261 rdf:type schema:Organization
 




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


...