Comparison of multiple metagenomes using phylogenetic networks based on ecological indices View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-10

AUTHORS

Suparna Mitra, Jack A Gilbert, Dawn Field, Daniel H Huson

ABSTRACT

Second-generation sequencing technologies are fueling a vast increase in the number and scope of metagenome projects. There is a great need for the development of new methods for visualizing the relationships between multiple metagenomic data sets. To address this, a novel approach is presented that combines the use of taxonomic analysis, ecological indices and non-hierarchical clustering to provide a network representation of the relationships between different metagenome data sets. The approach is illustrated using several published data sets of different types, including metagenomes, metatranscriptomes and 16S ribosomal profiles. Application of the approach to the same data summarized at different taxonomical levels gives rise to remarkably similar networks, indicating that the analysis is very robust. Importantly, the networks provide the both visual definition and metric quantification for the non-rooted relationship between samples, combining the desirable characteristics of other tools into one. More... »

PAGES

1236

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ismej.2010.51

DOI

http://dx.doi.org/10.1038/ismej.2010.51

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000387248

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20428222


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/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cluster Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ecosystem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metagenome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metagenomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phylogeny", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "RNA, Ribosomal, 16S", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of T\u00fcbingen", 
          "id": "https://www.grid.ac/institutes/grid.10392.39", 
          "name": [
            "Center for Bioinformatics ZBIT, T\u00fcbingen University, T\u00fcbingen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mitra", 
        "givenName": "Suparna", 
        "id": "sg:person.0616260414.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616260414.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Plymouth Marine Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.22319.3b", 
          "name": [
            "Plymouth Marine Laboratory, Prospect Place, Plymouth, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gilbert", 
        "givenName": "Jack A", 
        "id": "sg:person.0727626545.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727626545.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Ecology and Hydrology", 
          "id": "https://www.grid.ac/institutes/grid.494924.6", 
          "name": [
            "NERC Centre for Ecology and Hydrology, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Field", 
        "givenName": "Dawn", 
        "id": "sg:person.01223211445.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223211445.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of T\u00fcbingen", 
          "id": "https://www.grid.ac/institutes/grid.10392.39", 
          "name": [
            "Center for Bioinformatics ZBIT, T\u00fcbingen University, T\u00fcbingen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huson", 
        "givenName": "Daniel H", 
        "id": "sg:person.0766300744.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0766300744.30"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1073/pnas.0605127103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008547462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.0050077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012896623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-2836(05)80360-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013618994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm1000", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015047250"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1462-2920.2009.02017.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021785415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1462-2920.2009.02017.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021785415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.0050075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025987163"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.5969107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034259503"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msh018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037734324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0003042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040781551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1942268", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046249265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/2031098a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050466553", 
          "https://doi.org/10.1038/2031098a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1093857", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062449306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1931577", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069656007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2528080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069973928"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-10", 
    "datePublishedReg": "2010-10-01", 
    "description": "Second-generation sequencing technologies are fueling a vast increase in the number and scope of metagenome projects. There is a great need for the development of new methods for visualizing the relationships between multiple metagenomic data sets. To address this, a novel approach is presented that combines the use of taxonomic analysis, ecological indices and non-hierarchical clustering to provide a network representation of the relationships between different metagenome data sets. The approach is illustrated using several published data sets of different types, including metagenomes, metatranscriptomes and 16S ribosomal profiles. Application of the approach to the same data summarized at different taxonomical levels gives rise to remarkably similar networks, indicating that the analysis is very robust. Importantly, the networks provide the both visual definition and metric quantification for the non-rooted relationship between samples, combining the desirable characteristics of other tools into one.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/ismej.2010.51", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2758641", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1038436", 
        "issn": [
          "1751-7362", 
          "1751-7370"
        ], 
        "name": "The ISME Journal", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "10", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Comparison of multiple metagenomes using phylogenetic networks based on ecological indices", 
    "pagination": "1236", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "01d847a0c7532fc6031bf7a349cd39bee3c73ff997ac522f04f0c673afd58388"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "20428222"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101301086"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/ismej.2010.51"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1000387248"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/ismej.2010.51", 
      "https://app.dimensions.ai/details/publication/pub.1000387248"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T14:48", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8663_00000434.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/ismej201051"
  }
]
 

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/ismej.2010.51'

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/ismej.2010.51'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.51'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.51'


 

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

169 TRIPLES      21 PREDICATES      50 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/ismej.2010.51 schema:about N0dbac26cf2c24d78af7290f2cc16425a
2 N3e25b6a24a8b4c0389dd0e5bb0e66898
3 N6a0b96dee1564f08a27844fed67dc576
4 N842c13c1d4d249948300b2220a2d6970
5 N916ebd7d455f4d498ba04d0eae7ee973
6 N9db5518f13c4476c8cb8b6d5ff6c50ac
7 Nf458a4a092024cb99f77092c114183b7
8 anzsrc-for:08
9 anzsrc-for:0806
10 schema:author N096cdbe9150c44b9853a4c51dda34bb1
11 schema:citation sg:pub.10.1038/2031098a0
12 https://doi.org/10.1016/s0022-2836(05)80360-2
13 https://doi.org/10.1073/pnas.0605127103
14 https://doi.org/10.1093/molbev/msh018
15 https://doi.org/10.1093/nar/gkm1000
16 https://doi.org/10.1101/gr.5969107
17 https://doi.org/10.1111/j.1462-2920.2009.02017.x
18 https://doi.org/10.1126/science.1093857
19 https://doi.org/10.1371/journal.pbio.0050075
20 https://doi.org/10.1371/journal.pbio.0050077
21 https://doi.org/10.1371/journal.pone.0003042
22 https://doi.org/10.2307/1931577
23 https://doi.org/10.2307/1942268
24 https://doi.org/10.2307/2528080
25 schema:datePublished 2010-10
26 schema:datePublishedReg 2010-10-01
27 schema:description Second-generation sequencing technologies are fueling a vast increase in the number and scope of metagenome projects. There is a great need for the development of new methods for visualizing the relationships between multiple metagenomic data sets. To address this, a novel approach is presented that combines the use of taxonomic analysis, ecological indices and non-hierarchical clustering to provide a network representation of the relationships between different metagenome data sets. The approach is illustrated using several published data sets of different types, including metagenomes, metatranscriptomes and 16S ribosomal profiles. Application of the approach to the same data summarized at different taxonomical levels gives rise to remarkably similar networks, indicating that the analysis is very robust. Importantly, the networks provide the both visual definition and metric quantification for the non-rooted relationship between samples, combining the desirable characteristics of other tools into one.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree true
31 schema:isPartOf N6aca9f16a68a4c4ea0755a19a88c6517
32 Nef2010804673408185f57a94e39f2d71
33 sg:journal.1038436
34 schema:name Comparison of multiple metagenomes using phylogenetic networks based on ecological indices
35 schema:pagination 1236
36 schema:productId N8d941afb43eb4fb9b3d9af9b2809584d
37 Nc815c86ea2fe48a7adc9584e7152aad6
38 Nce4926efedc04c31bad999519b4b1aa9
39 Nd5c93f4f59fd4173ab0f6d150edd6ba6
40 Nfcd9f724d50d45818fc563db62be4ef8
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000387248
42 https://doi.org/10.1038/ismej.2010.51
43 schema:sdDatePublished 2019-04-10T14:48
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher Nd74d684485934e54a9d6af18d350392b
46 schema:url https://www.nature.com/articles/ismej201051
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N00c9b66f515d4b95b0984e1e4f3f40c8 rdf:first sg:person.0727626545.37
51 rdf:rest Nd0cba64419324dbaa85d211f1084e058
52 N096cdbe9150c44b9853a4c51dda34bb1 rdf:first sg:person.0616260414.02
53 rdf:rest N00c9b66f515d4b95b0984e1e4f3f40c8
54 N0c5f2be016cc44e0964fed7298f4a48d rdf:first sg:person.0766300744.30
55 rdf:rest rdf:nil
56 N0dbac26cf2c24d78af7290f2cc16425a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
57 schema:name Metagenome
58 rdf:type schema:DefinedTerm
59 N3e25b6a24a8b4c0389dd0e5bb0e66898 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
60 schema:name RNA, Ribosomal, 16S
61 rdf:type schema:DefinedTerm
62 N6a0b96dee1564f08a27844fed67dc576 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
63 schema:name Ecosystem
64 rdf:type schema:DefinedTerm
65 N6aca9f16a68a4c4ea0755a19a88c6517 schema:volumeNumber 4
66 rdf:type schema:PublicationVolume
67 N842c13c1d4d249948300b2220a2d6970 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Gene Expression Profiling
69 rdf:type schema:DefinedTerm
70 N8d941afb43eb4fb9b3d9af9b2809584d schema:name dimensions_id
71 schema:value pub.1000387248
72 rdf:type schema:PropertyValue
73 N916ebd7d455f4d498ba04d0eae7ee973 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Cluster Analysis
75 rdf:type schema:DefinedTerm
76 N9db5518f13c4476c8cb8b6d5ff6c50ac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Phylogeny
78 rdf:type schema:DefinedTerm
79 Nc815c86ea2fe48a7adc9584e7152aad6 schema:name doi
80 schema:value 10.1038/ismej.2010.51
81 rdf:type schema:PropertyValue
82 Nce4926efedc04c31bad999519b4b1aa9 schema:name nlm_unique_id
83 schema:value 101301086
84 rdf:type schema:PropertyValue
85 Nd0cba64419324dbaa85d211f1084e058 rdf:first sg:person.01223211445.99
86 rdf:rest N0c5f2be016cc44e0964fed7298f4a48d
87 Nd5c93f4f59fd4173ab0f6d150edd6ba6 schema:name readcube_id
88 schema:value 01d847a0c7532fc6031bf7a349cd39bee3c73ff997ac522f04f0c673afd58388
89 rdf:type schema:PropertyValue
90 Nd74d684485934e54a9d6af18d350392b schema:name Springer Nature - SN SciGraph project
91 rdf:type schema:Organization
92 Nef2010804673408185f57a94e39f2d71 schema:issueNumber 10
93 rdf:type schema:PublicationIssue
94 Nf458a4a092024cb99f77092c114183b7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Metagenomics
96 rdf:type schema:DefinedTerm
97 Nfcd9f724d50d45818fc563db62be4ef8 schema:name pubmed_id
98 schema:value 20428222
99 rdf:type schema:PropertyValue
100 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
101 schema:name Information and Computing Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
104 schema:name Information Systems
105 rdf:type schema:DefinedTerm
106 sg:grant.2758641 http://pending.schema.org/fundedItem sg:pub.10.1038/ismej.2010.51
107 rdf:type schema:MonetaryGrant
108 sg:journal.1038436 schema:issn 1751-7362
109 1751-7370
110 schema:name The ISME Journal
111 rdf:type schema:Periodical
112 sg:person.01223211445.99 schema:affiliation https://www.grid.ac/institutes/grid.494924.6
113 schema:familyName Field
114 schema:givenName Dawn
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223211445.99
116 rdf:type schema:Person
117 sg:person.0616260414.02 schema:affiliation https://www.grid.ac/institutes/grid.10392.39
118 schema:familyName Mitra
119 schema:givenName Suparna
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616260414.02
121 rdf:type schema:Person
122 sg:person.0727626545.37 schema:affiliation https://www.grid.ac/institutes/grid.22319.3b
123 schema:familyName Gilbert
124 schema:givenName Jack A
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727626545.37
126 rdf:type schema:Person
127 sg:person.0766300744.30 schema:affiliation https://www.grid.ac/institutes/grid.10392.39
128 schema:familyName Huson
129 schema:givenName Daniel H
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0766300744.30
131 rdf:type schema:Person
132 sg:pub.10.1038/2031098a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050466553
133 https://doi.org/10.1038/2031098a0
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/s0022-2836(05)80360-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013618994
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1073/pnas.0605127103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008547462
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1093/molbev/msh018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037734324
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1093/nar/gkm1000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015047250
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1101/gr.5969107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034259503
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1111/j.1462-2920.2009.02017.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021785415
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1126/science.1093857 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062449306
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1371/journal.pbio.0050075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025987163
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1371/journal.pbio.0050077 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012896623
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1371/journal.pone.0003042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040781551
154 rdf:type schema:CreativeWork
155 https://doi.org/10.2307/1931577 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069656007
156 rdf:type schema:CreativeWork
157 https://doi.org/10.2307/1942268 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046249265
158 rdf:type schema:CreativeWork
159 https://doi.org/10.2307/2528080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069973928
160 rdf:type schema:CreativeWork
161 https://www.grid.ac/institutes/grid.10392.39 schema:alternateName University of Tübingen
162 schema:name Center for Bioinformatics ZBIT, Tübingen University, Tübingen, Germany
163 rdf:type schema:Organization
164 https://www.grid.ac/institutes/grid.22319.3b schema:alternateName Plymouth Marine Laboratory
165 schema:name Plymouth Marine Laboratory, Prospect Place, Plymouth, UK
166 rdf:type schema:Organization
167 https://www.grid.ac/institutes/grid.494924.6 schema:alternateName Centre for Ecology and Hydrology
168 schema:name NERC Centre for Ecology and Hydrology, Oxford, UK
169 rdf:type schema:Organization
 




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


...