Species Tree Estimation from Genome-Wide Data with guenomu View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Leonardo de Oliveira Martins , David Posada

ABSTRACT

The history of particular genes and that of the species that carry them can be different for a variety of reasons. In particular, gene trees and species trees can differ due to well-known evolutionary processes such as gene duplication and loss, lateral gene transfer, or incomplete lineage sorting. Species tree reconstruction methods have been developed to take this incongruence into account; these can be divided grossly into supertree and supermatrix approaches. Here we introduce a new Bayesian hierarchical model that we have recently developed and implemented in the program guenomu. The new model considers multiple sources of gene tree/species tree disagreement. Guenomu takes as input posterior distributions of unrooted gene tree topologies for multiple gene families, in order to estimate the posterior distribution of rooted species tree topologies. More... »

PAGES

461-478

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4939-6622-6_18

DOI

http://dx.doi.org/10.1007/978-1-4939-6622-6_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017957146

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27896732


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bayes Theorem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Evolution, Molecular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Duplication", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Transfer, Horizontal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phylogeny", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Vigo", 
          "id": "https://www.grid.ac/institutes/grid.6312.6", 
          "name": [
            "Department of Biochemistry, Genetics and Immunology, University of Vigo", 
            "Department of Materials, Imperial College London"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Oliveira Martins", 
        "givenName": "Leonardo", 
        "id": "sg:person.01116016500.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116016500.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Vigo", 
          "id": "https://www.grid.ac/institutes/grid.6312.6", 
          "name": [
            "Department of Biochemistry, Genetics and Immunology, University of Vigo"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Posada", 
        "givenName": "David", 
        "id": "sg:person.01012245367.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012245367.32"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000824893"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/27.21.4218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002742894"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1211733109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002867638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tree.2006.10.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003167190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msm034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003600610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-11-574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004196612", 
          "https://doi.org/10.1186/1471-2105-11-574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btu648", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005533570"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.10.7.991", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008893190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bti720", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009747156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012372705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/mss075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013930907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.genom.9.081307.164407", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016212459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01894195", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017615483", 
          "https://doi.org/10.1007/bf01894195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01894195", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017615483", 
          "https://doi.org/10.1007/bf01894195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0002651", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020215003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msp274", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020622462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/molbev/msp274", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020622462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0806251106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020703135"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sysbio/syu082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020815994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/cmb.2012.0042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023325964"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btq228", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024328010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sysbio/syu128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031069378"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.spa.2008.09.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031581149"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sysbio/syu023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034243280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10635150500354928", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035709606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/5052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038696050", 
          "https://doi.org/10.1038/5052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/5052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038696050", 
          "https://doi.org/10.1038/5052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/1055-7903(92)90035-f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039765394"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01734359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044065382", 
          "https://doi.org/10.1007/bf01734359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01734359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044065382", 
          "https://doi.org/10.1007/bf01734359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sysbio/sys029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049138722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.141978.112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049516098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg412", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051607942"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/jb.184.8.2072-2080.2002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053266515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10635150701429982", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058369700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/106351599260030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058369897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sysbio/syp031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060048375"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcbb.2008.66", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061540678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2992432", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070162015"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075127736", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078837863", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083188048", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017", 
    "datePublishedReg": "2017-01-01", 
    "description": "The history of particular genes and that of the species that carry them can be different for a variety of reasons. In particular, gene trees and species trees can differ due to well-known evolutionary processes such as gene duplication and loss, lateral gene transfer, or incomplete lineage sorting. Species tree reconstruction methods have been developed to take this incongruence into account; these can be divided grossly into supertree and supermatrix approaches. Here we introduce a new Bayesian hierarchical model that we have recently developed and implemented in the program guenomu. The new model considers multiple sources of gene tree/species tree disagreement. Guenomu takes as input posterior distributions of unrooted gene tree topologies for multiple gene families, in order to estimate the posterior distribution of rooted species tree topologies.", 
    "editor": [
      {
        "familyName": "Keith", 
        "givenName": "Jonathan M.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4939-6622-6_18", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4939-6620-2", 
        "978-1-4939-6622-6"
      ], 
      "name": "Bioinformatics", 
      "type": "Book"
    }, 
    "name": "Species Tree Estimation from Genome-Wide Data with guenomu", 
    "pagination": "461-478", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4939-6622-6_18"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "558f493d1920905ae05402395877a99e1fad541214aa654379d5a1e7f883bd57"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1017957146"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27896732"
        ]
      }
    ], 
    "publisher": {
      "location": "New York, NY", 
      "name": "Springer New York", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4939-6622-6_18", 
      "https://app.dimensions.ai/details/publication/pub.1017957146"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T18:21", 
    "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_8681_00000346.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-4939-6622-6_18"
  }
]
 

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.10.1007/978-1-4939-6622-6_18'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-6622-6_18'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-6622-6_18'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-6622-6_18'


 

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

220 TRIPLES      23 PREDICATES      73 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4939-6622-6_18 schema:about N2e0b867b610a47beb445794c3f971d75
2 N605beae1d5b546619aaf54189ccf09b6
3 N7677f917de534b60acd90c4eb1fd68d3
4 N7e2fedee4db044dd895fb5cb3a264514
5 N8f9186656d1848bb9e7f61d45d90c729
6 N94f91dafc8ee4787b3e670e79b02f815
7 Ne2e10e4aaf5c49638203cd383cbab4f4
8 anzsrc-for:06
9 anzsrc-for:0604
10 schema:author N5da6c21c82f742db953c1f580bea3f45
11 schema:citation sg:pub.10.1007/bf01734359
12 sg:pub.10.1007/bf01894195
13 sg:pub.10.1038/5052
14 sg:pub.10.1186/1471-2105-11-574
15 https://app.dimensions.ai/details/publication/pub.1075127736
16 https://app.dimensions.ai/details/publication/pub.1078837863
17 https://app.dimensions.ai/details/publication/pub.1083188048
18 https://doi.org/10.1016/1055-7903(92)90035-f
19 https://doi.org/10.1016/j.spa.2008.09.007
20 https://doi.org/10.1016/j.tree.2006.10.002
21 https://doi.org/10.1073/pnas.0806251106
22 https://doi.org/10.1073/pnas.1211733109
23 https://doi.org/10.1080/10635150500354928
24 https://doi.org/10.1080/10635150701429982
25 https://doi.org/10.1080/106351599260030
26 https://doi.org/10.1089/cmb.2012.0042
27 https://doi.org/10.1093/bioinformatics/btg412
28 https://doi.org/10.1093/bioinformatics/bti720
29 https://doi.org/10.1093/bioinformatics/btn148
30 https://doi.org/10.1093/bioinformatics/btp368
31 https://doi.org/10.1093/bioinformatics/btq228
32 https://doi.org/10.1093/bioinformatics/btu648
33 https://doi.org/10.1093/molbev/msm034
34 https://doi.org/10.1093/molbev/msp274
35 https://doi.org/10.1093/molbev/mss075
36 https://doi.org/10.1093/nar/27.21.4218
37 https://doi.org/10.1093/sysbio/syp031
38 https://doi.org/10.1093/sysbio/sys029
39 https://doi.org/10.1093/sysbio/syu023
40 https://doi.org/10.1093/sysbio/syu082
41 https://doi.org/10.1093/sysbio/syu128
42 https://doi.org/10.1101/gr.10.7.991
43 https://doi.org/10.1101/gr.141978.112
44 https://doi.org/10.1109/tcbb.2008.66
45 https://doi.org/10.1128/jb.184.8.2072-2080.2002
46 https://doi.org/10.1146/annurev.genom.9.081307.164407
47 https://doi.org/10.1371/journal.pone.0002651
48 https://doi.org/10.2307/2992432
49 schema:datePublished 2017
50 schema:datePublishedReg 2017-01-01
51 schema:description The history of particular genes and that of the species that carry them can be different for a variety of reasons. In particular, gene trees and species trees can differ due to well-known evolutionary processes such as gene duplication and loss, lateral gene transfer, or incomplete lineage sorting. Species tree reconstruction methods have been developed to take this incongruence into account; these can be divided grossly into supertree and supermatrix approaches. Here we introduce a new Bayesian hierarchical model that we have recently developed and implemented in the program guenomu. The new model considers multiple sources of gene tree/species tree disagreement. Guenomu takes as input posterior distributions of unrooted gene tree topologies for multiple gene families, in order to estimate the posterior distribution of rooted species tree topologies.
52 schema:editor Na191b99e9238499eb3c31742a9fe1fd4
53 schema:genre chapter
54 schema:inLanguage en
55 schema:isAccessibleForFree false
56 schema:isPartOf N6e6b8e854a4f4ac6828784c794c2c241
57 schema:name Species Tree Estimation from Genome-Wide Data with guenomu
58 schema:pagination 461-478
59 schema:productId N26569964c68e49aab260c40d4e112589
60 N40470b7a8136467ba329588b0a826e30
61 N42021831ff72441ba7569712f3932230
62 Na329d7f7bf37489d9dce4367ad108e49
63 schema:publisher Ne8d8e62bafd34d089be81c0313bb68f1
64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017957146
65 https://doi.org/10.1007/978-1-4939-6622-6_18
66 schema:sdDatePublished 2019-04-15T18:21
67 schema:sdLicense https://scigraph.springernature.com/explorer/license/
68 schema:sdPublisher N9104f3ae763f46dcb8fdb5ccb97f362e
69 schema:url http://link.springer.com/10.1007/978-1-4939-6622-6_18
70 sgo:license sg:explorer/license/
71 sgo:sdDataset chapters
72 rdf:type schema:Chapter
73 N26569964c68e49aab260c40d4e112589 schema:name doi
74 schema:value 10.1007/978-1-4939-6622-6_18
75 rdf:type schema:PropertyValue
76 N2e0b867b610a47beb445794c3f971d75 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Bayes Theorem
78 rdf:type schema:DefinedTerm
79 N40470b7a8136467ba329588b0a826e30 schema:name readcube_id
80 schema:value 558f493d1920905ae05402395877a99e1fad541214aa654379d5a1e7f883bd57
81 rdf:type schema:PropertyValue
82 N42021831ff72441ba7569712f3932230 schema:name pubmed_id
83 schema:value 27896732
84 rdf:type schema:PropertyValue
85 N5da6c21c82f742db953c1f580bea3f45 rdf:first sg:person.01116016500.00
86 rdf:rest N75146baae94340908cbc664d80db24bc
87 N605beae1d5b546619aaf54189ccf09b6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Gene Transfer, Horizontal
89 rdf:type schema:DefinedTerm
90 N6e6b8e854a4f4ac6828784c794c2c241 schema:isbn 978-1-4939-6620-2
91 978-1-4939-6622-6
92 schema:name Bioinformatics
93 rdf:type schema:Book
94 N75146baae94340908cbc664d80db24bc rdf:first sg:person.01012245367.32
95 rdf:rest rdf:nil
96 N7677f917de534b60acd90c4eb1fd68d3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Algorithms
98 rdf:type schema:DefinedTerm
99 N7e2fedee4db044dd895fb5cb3a264514 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Phylogeny
101 rdf:type schema:DefinedTerm
102 N8f9186656d1848bb9e7f61d45d90c729 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Evolution, Molecular
104 rdf:type schema:DefinedTerm
105 N9104f3ae763f46dcb8fdb5ccb97f362e schema:name Springer Nature - SN SciGraph project
106 rdf:type schema:Organization
107 N94f91dafc8ee4787b3e670e79b02f815 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Gene Duplication
109 rdf:type schema:DefinedTerm
110 Na191b99e9238499eb3c31742a9fe1fd4 rdf:first Ne1c8d067c7a147709f453ddb14899eee
111 rdf:rest rdf:nil
112 Na329d7f7bf37489d9dce4367ad108e49 schema:name dimensions_id
113 schema:value pub.1017957146
114 rdf:type schema:PropertyValue
115 Ne1c8d067c7a147709f453ddb14899eee schema:familyName Keith
116 schema:givenName Jonathan M.
117 rdf:type schema:Person
118 Ne2e10e4aaf5c49638203cd383cbab4f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Computational Biology
120 rdf:type schema:DefinedTerm
121 Ne8d8e62bafd34d089be81c0313bb68f1 schema:location New York, NY
122 schema:name Springer New York
123 rdf:type schema:Organisation
124 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
125 schema:name Biological Sciences
126 rdf:type schema:DefinedTerm
127 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
128 schema:name Genetics
129 rdf:type schema:DefinedTerm
130 sg:person.01012245367.32 schema:affiliation https://www.grid.ac/institutes/grid.6312.6
131 schema:familyName Posada
132 schema:givenName David
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012245367.32
134 rdf:type schema:Person
135 sg:person.01116016500.00 schema:affiliation https://www.grid.ac/institutes/grid.6312.6
136 schema:familyName de Oliveira Martins
137 schema:givenName Leonardo
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116016500.00
139 rdf:type schema:Person
140 sg:pub.10.1007/bf01734359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044065382
141 https://doi.org/10.1007/bf01734359
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/bf01894195 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017615483
144 https://doi.org/10.1007/bf01894195
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/5052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038696050
147 https://doi.org/10.1038/5052
148 rdf:type schema:CreativeWork
149 sg:pub.10.1186/1471-2105-11-574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004196612
150 https://doi.org/10.1186/1471-2105-11-574
151 rdf:type schema:CreativeWork
152 https://app.dimensions.ai/details/publication/pub.1075127736 schema:CreativeWork
153 https://app.dimensions.ai/details/publication/pub.1078837863 schema:CreativeWork
154 https://app.dimensions.ai/details/publication/pub.1083188048 schema:CreativeWork
155 https://doi.org/10.1016/1055-7903(92)90035-f schema:sameAs https://app.dimensions.ai/details/publication/pub.1039765394
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.spa.2008.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031581149
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.tree.2006.10.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003167190
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1073/pnas.0806251106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020703135
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1073/pnas.1211733109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002867638
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1080/10635150500354928 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035709606
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1080/10635150701429982 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058369700
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1080/106351599260030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058369897
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1089/cmb.2012.0042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023325964
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1093/bioinformatics/btg412 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051607942
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1093/bioinformatics/bti720 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009747156
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1093/bioinformatics/btn148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000824893
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1093/bioinformatics/btp368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012372705
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/bioinformatics/btq228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024328010
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1093/bioinformatics/btu648 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005533570
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1093/molbev/msm034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003600610
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1093/molbev/msp274 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020622462
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1093/molbev/mss075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013930907
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1093/nar/27.21.4218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002742894
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1093/sysbio/syp031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060048375
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1093/sysbio/sys029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049138722
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1093/sysbio/syu023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034243280
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1093/sysbio/syu082 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020815994
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1093/sysbio/syu128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031069378
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1101/gr.10.7.991 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008893190
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1101/gr.141978.112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049516098
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1109/tcbb.2008.66 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061540678
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1128/jb.184.8.2072-2080.2002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053266515
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1146/annurev.genom.9.081307.164407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016212459
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1371/journal.pone.0002651 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020215003
214 rdf:type schema:CreativeWork
215 https://doi.org/10.2307/2992432 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070162015
216 rdf:type schema:CreativeWork
217 https://www.grid.ac/institutes/grid.6312.6 schema:alternateName University of Vigo
218 schema:name Department of Biochemistry, Genetics and Immunology, University of Vigo
219 Department of Materials, Imperial College London
220 rdf:type schema:Organization
 




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


...