On Assigning Individuals from Cryptic Population Structures to Optimal Predicted Subpopulations: An Empirical Evaluation of Non-parametric Population Structure Analysis Techniques View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Pornchalearm Deejai , Anunchai Assawamakin , Pongsakorn Wangkumhang , Kanokwan Poomputsa , Sissades Tongsima

ABSTRACT

Many algorithms have been proposed to analyze population structures from the single nucleotide polymorphism (SNP) genotyping data of some number of individuals and try to assign individuals to genetically similar groups. These algorithms can be categorized into two computational paradigms: parametric and non-parametric approaches. Although the parametric-based approach is a gold standard for population structure analysis, the computational burden incurred by running these algorithms is unacceptable for large complex dataset. As genotyping platforms incorporating more SNPs, analyzing ever larger and more complex datasets are becoming a standard practice. Hence, the computationally efficient non-parametric methods for analysis of genotypic datasets are needed to reveal the population structure. In this study, we evaluated two leading non-parametric population structure analysis techniques, namely ipPCA and AWclust, on their abilities to characterize the genetic diversity and population structure of two complex SNP genotype datasets (as many as 243855 SNPs). The head-to-head comparisons were conducted on two major aspects: ability to infer the number of genetically related subpopulations (K) and ability to correctly assign individuals to these subpopulations. The experimental results suggested that AWclust could be more suitable when applying to a small and less complex dataset. However, with a large and more complex dataset, ipPCA is a much better choice yielding higher accuracy on assigning genetically similar individuals to the inferred groups. More... »

PAGES

58-70

Book

TITLE

Computational Systems-Biology and Bioinformatics

ISBN

978-3-642-16749-2
978-3-642-16750-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-16750-8_6

DOI

http://dx.doi.org/10.1007/978-3-642-16750-8_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014085462


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/2008/0802", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computation Theory and Mathematics", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "King Mongkut's University of Technology Thonburi", 
          "id": "https://www.grid.ac/institutes/grid.412151.2", 
          "name": [
            "Bioinformatics and Systems Biology Program, King Mongkut University of Technology, Thonburi, 10140, Bangkok, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Deejai", 
        "givenName": "Pornchalearm", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Center for Genetic Engineering and Biotechnology", 
          "id": "https://www.grid.ac/institutes/grid.419250.b", 
          "name": [
            "Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology, Thailand Science Park, Paholyothin Road, 12120, Pathumthani, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Assawamakin", 
        "givenName": "Anunchai", 
        "id": "sg:person.0711546035.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0711546035.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Center for Genetic Engineering and Biotechnology", 
          "id": "https://www.grid.ac/institutes/grid.419250.b", 
          "name": [
            "Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology, Thailand Science Park, Paholyothin Road, 12120, Pathumthani, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wangkumhang", 
        "givenName": "Pongsakorn", 
        "id": "sg:person.01140376600.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140376600.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "King Mongkut's University of Technology Thonburi", 
          "id": "https://www.grid.ac/institutes/grid.412151.2", 
          "name": [
            "School of Bioresources and Technology, King Mongkut University of Technology, Thonburi, 10140, Bangkok, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Poomputsa", 
        "givenName": "Kanokwan", 
        "id": "sg:person.0656517205.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656517205.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Center for Genetic Engineering and Biotechnology", 
          "id": "https://www.grid.ac/institutes/grid.419250.b", 
          "name": [
            "Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology, Thailand Science Park, Paholyothin Road, 12120, Pathumthani, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tongsima", 
        "givenName": "Sissades", 
        "id": "sg:person.01236662624.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236662624.14"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/ng1337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002008100", 
          "https://doi.org/10.1038/ng1337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002008100", 
          "https://doi.org/10.1038/ng1337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1078311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005962126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1167936", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010221272"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1333", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011014033", 
          "https://doi.org/10.1038/ng1333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1333", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011014033", 
          "https://doi.org/10.1038/ng1333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-0450-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011935162", 
          "https://doi.org/10.1007/978-1-4757-0450-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-0450-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011935162", 
          "https://doi.org/10.1007/978-1-4757-0450-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/302959", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022630576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000083030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027004433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1007730.1007731", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028225414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.0020190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029170965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1479-7364-1-4-274", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030594734", 
          "https://doi.org/10.1186/1479-7364-1-4-274"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042783804"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/368455a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046878300", 
          "https://doi.org/10.1038/368455a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35015718", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051065086", 
          "https://doi.org/10.1038/35015718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35015718", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051065086", 
          "https://doi.org/10.1038/35015718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.085589.108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052929427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/515510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058789899"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.8091226", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062652231"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010", 
    "datePublishedReg": "2010-01-01", 
    "description": "Many algorithms have been proposed to analyze population structures from the single nucleotide polymorphism (SNP) genotyping data of some number of individuals and try to assign individuals to genetically similar groups. These algorithms can be categorized into two computational paradigms: parametric and non-parametric approaches. Although the parametric-based approach is a gold standard for population structure analysis, the computational burden incurred by running these algorithms is unacceptable for large complex dataset. As genotyping platforms incorporating more SNPs, analyzing ever larger and more complex datasets are becoming a standard practice. Hence, the computationally efficient non-parametric methods for analysis of genotypic datasets are needed to reveal the population structure. In this study, we evaluated two leading non-parametric population structure analysis techniques, namely ipPCA and AWclust, on their abilities to characterize the genetic diversity and population structure of two complex SNP genotype datasets (as many as 243855 SNPs). The head-to-head comparisons were conducted on two major aspects: ability to infer the number of genetically related subpopulations (K) and ability to correctly assign individuals to these subpopulations. The experimental results suggested that AWclust could be more suitable when applying to a small and less complex dataset. However, with a large and more complex dataset, ipPCA is a much better choice yielding higher accuracy on assigning genetically similar individuals to the inferred groups.", 
    "editor": [
      {
        "familyName": "Chan", 
        "givenName": "Jonathan H.", 
        "type": "Person"
      }, 
      {
        "familyName": "Ong", 
        "givenName": "Yew-Soon", 
        "type": "Person"
      }, 
      {
        "familyName": "Cho", 
        "givenName": "Sung-Bae", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-16750-8_6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-16749-2", 
        "978-3-642-16750-8"
      ], 
      "name": "Computational Systems-Biology and Bioinformatics", 
      "type": "Book"
    }, 
    "name": "On Assigning Individuals from Cryptic Population Structures to Optimal Predicted Subpopulations: An Empirical Evaluation of Non-parametric Population Structure Analysis Techniques", 
    "pagination": "58-70", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1014085462"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-16750-8_6"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "431a17e849c1b2fc8c5d67bd5bedf13869a7de4bd28a8b9e5bb98c51eff19506"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-16750-8_6", 
      "https://app.dimensions.ai/details/publication/pub.1014085462"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T08:32", 
    "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/v90/0000000364_0000000364/records_72856_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-642-16750-8_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.10.1007/978-3-642-16750-8_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.10.1007/978-3-642-16750-8_6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-16750-8_6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-16750-8_6'


 

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

160 TRIPLES      23 PREDICATES      43 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-16750-8_6 schema:about anzsrc-for:08
2 anzsrc-for:0802
3 schema:author N6a28241cee024feab6704518a00d8e87
4 schema:citation sg:pub.10.1007/978-1-4757-0450-1
5 sg:pub.10.1038/35015718
6 sg:pub.10.1038/368455a0
7 sg:pub.10.1038/ng1333
8 sg:pub.10.1038/ng1337
9 sg:pub.10.1186/1479-7364-1-4-274
10 https://doi.org/10.1086/302959
11 https://doi.org/10.1086/515510
12 https://doi.org/10.1093/bioinformatics/btm138
13 https://doi.org/10.1101/gr.085589.108
14 https://doi.org/10.1126/science.1078311
15 https://doi.org/10.1126/science.1167936
16 https://doi.org/10.1126/science.8091226
17 https://doi.org/10.1145/1007730.1007731
18 https://doi.org/10.1159/000083030
19 https://doi.org/10.1371/journal.pgen.0020190
20 schema:datePublished 2010
21 schema:datePublishedReg 2010-01-01
22 schema:description Many algorithms have been proposed to analyze population structures from the single nucleotide polymorphism (SNP) genotyping data of some number of individuals and try to assign individuals to genetically similar groups. These algorithms can be categorized into two computational paradigms: parametric and non-parametric approaches. Although the parametric-based approach is a gold standard for population structure analysis, the computational burden incurred by running these algorithms is unacceptable for large complex dataset. As genotyping platforms incorporating more SNPs, analyzing ever larger and more complex datasets are becoming a standard practice. Hence, the computationally efficient non-parametric methods for analysis of genotypic datasets are needed to reveal the population structure. In this study, we evaluated two leading non-parametric population structure analysis techniques, namely ipPCA and AWclust, on their abilities to characterize the genetic diversity and population structure of two complex SNP genotype datasets (as many as 243855 SNPs). The head-to-head comparisons were conducted on two major aspects: ability to infer the number of genetically related subpopulations (K) and ability to correctly assign individuals to these subpopulations. The experimental results suggested that AWclust could be more suitable when applying to a small and less complex dataset. However, with a large and more complex dataset, ipPCA is a much better choice yielding higher accuracy on assigning genetically similar individuals to the inferred groups.
23 schema:editor N5e9a10dd46724d21b002735f1f333ef3
24 schema:genre chapter
25 schema:inLanguage en
26 schema:isAccessibleForFree false
27 schema:isPartOf N5e0f8a5bc01c4cf2bd03e36b63bc765e
28 schema:name On Assigning Individuals from Cryptic Population Structures to Optimal Predicted Subpopulations: An Empirical Evaluation of Non-parametric Population Structure Analysis Techniques
29 schema:pagination 58-70
30 schema:productId N13b04655f40d4171a7ade5959fed1c61
31 N64991c56d0b7442d89971fa78c2d9fff
32 N67c3f101c37b4a0486e931e5dece65d6
33 schema:publisher Ne6a6f7cfc4264820bf11245b546a72cb
34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014085462
35 https://doi.org/10.1007/978-3-642-16750-8_6
36 schema:sdDatePublished 2019-04-16T08:32
37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
38 schema:sdPublisher Nb8e6ecc33e7d47768ce32dd4837d6959
39 schema:url https://link.springer.com/10.1007%2F978-3-642-16750-8_6
40 sgo:license sg:explorer/license/
41 sgo:sdDataset chapters
42 rdf:type schema:Chapter
43 N13b04655f40d4171a7ade5959fed1c61 schema:name readcube_id
44 schema:value 431a17e849c1b2fc8c5d67bd5bedf13869a7de4bd28a8b9e5bb98c51eff19506
45 rdf:type schema:PropertyValue
46 N1bead4d8530e4810891eb944d321c09e schema:familyName Ong
47 schema:givenName Yew-Soon
48 rdf:type schema:Person
49 N3ce25b6a674843c3a06c632ed13ee584 schema:familyName Chan
50 schema:givenName Jonathan H.
51 rdf:type schema:Person
52 N4dc38fd248624c74aafd8bc4b9ebf424 rdf:first sg:person.0711546035.14
53 rdf:rest N7c917e9474c94dfabcfd530b5006e283
54 N58ce735be91e42dca4cbfcb75a29a200 schema:affiliation https://www.grid.ac/institutes/grid.412151.2
55 schema:familyName Deejai
56 schema:givenName Pornchalearm
57 rdf:type schema:Person
58 N59e00116b39044b9a2ad668bad6fc2cc rdf:first Nc6ceaea3b994484780c1c9106b189f41
59 rdf:rest rdf:nil
60 N5e0f8a5bc01c4cf2bd03e36b63bc765e schema:isbn 978-3-642-16749-2
61 978-3-642-16750-8
62 schema:name Computational Systems-Biology and Bioinformatics
63 rdf:type schema:Book
64 N5e9a10dd46724d21b002735f1f333ef3 rdf:first N3ce25b6a674843c3a06c632ed13ee584
65 rdf:rest N848083760b714f928105a26dfa56b18e
66 N64991c56d0b7442d89971fa78c2d9fff schema:name doi
67 schema:value 10.1007/978-3-642-16750-8_6
68 rdf:type schema:PropertyValue
69 N67c3f101c37b4a0486e931e5dece65d6 schema:name dimensions_id
70 schema:value pub.1014085462
71 rdf:type schema:PropertyValue
72 N6a28241cee024feab6704518a00d8e87 rdf:first N58ce735be91e42dca4cbfcb75a29a200
73 rdf:rest N4dc38fd248624c74aafd8bc4b9ebf424
74 N7c917e9474c94dfabcfd530b5006e283 rdf:first sg:person.01140376600.10
75 rdf:rest Nc377952dfa1c4bcd94bdb362c352fbe1
76 N848083760b714f928105a26dfa56b18e rdf:first N1bead4d8530e4810891eb944d321c09e
77 rdf:rest N59e00116b39044b9a2ad668bad6fc2cc
78 N96094eade534492c9e5ce7f4efa9c7f8 rdf:first sg:person.01236662624.14
79 rdf:rest rdf:nil
80 Nb8e6ecc33e7d47768ce32dd4837d6959 schema:name Springer Nature - SN SciGraph project
81 rdf:type schema:Organization
82 Nc377952dfa1c4bcd94bdb362c352fbe1 rdf:first sg:person.0656517205.79
83 rdf:rest N96094eade534492c9e5ce7f4efa9c7f8
84 Nc6ceaea3b994484780c1c9106b189f41 schema:familyName Cho
85 schema:givenName Sung-Bae
86 rdf:type schema:Person
87 Ne6a6f7cfc4264820bf11245b546a72cb schema:location Berlin, Heidelberg
88 schema:name Springer Berlin Heidelberg
89 rdf:type schema:Organisation
90 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
91 schema:name Information and Computing Sciences
92 rdf:type schema:DefinedTerm
93 anzsrc-for:0802 schema:inDefinedTermSet anzsrc-for:
94 schema:name Computation Theory and Mathematics
95 rdf:type schema:DefinedTerm
96 sg:person.01140376600.10 schema:affiliation https://www.grid.ac/institutes/grid.419250.b
97 schema:familyName Wangkumhang
98 schema:givenName Pongsakorn
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140376600.10
100 rdf:type schema:Person
101 sg:person.01236662624.14 schema:affiliation https://www.grid.ac/institutes/grid.419250.b
102 schema:familyName Tongsima
103 schema:givenName Sissades
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01236662624.14
105 rdf:type schema:Person
106 sg:person.0656517205.79 schema:affiliation https://www.grid.ac/institutes/grid.412151.2
107 schema:familyName Poomputsa
108 schema:givenName Kanokwan
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656517205.79
110 rdf:type schema:Person
111 sg:person.0711546035.14 schema:affiliation https://www.grid.ac/institutes/grid.419250.b
112 schema:familyName Assawamakin
113 schema:givenName Anunchai
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0711546035.14
115 rdf:type schema:Person
116 sg:pub.10.1007/978-1-4757-0450-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011935162
117 https://doi.org/10.1007/978-1-4757-0450-1
118 rdf:type schema:CreativeWork
119 sg:pub.10.1038/35015718 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051065086
120 https://doi.org/10.1038/35015718
121 rdf:type schema:CreativeWork
122 sg:pub.10.1038/368455a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046878300
123 https://doi.org/10.1038/368455a0
124 rdf:type schema:CreativeWork
125 sg:pub.10.1038/ng1333 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011014033
126 https://doi.org/10.1038/ng1333
127 rdf:type schema:CreativeWork
128 sg:pub.10.1038/ng1337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002008100
129 https://doi.org/10.1038/ng1337
130 rdf:type schema:CreativeWork
131 sg:pub.10.1186/1479-7364-1-4-274 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030594734
132 https://doi.org/10.1186/1479-7364-1-4-274
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1086/302959 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022630576
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1086/515510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058789899
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1093/bioinformatics/btm138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042783804
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1101/gr.085589.108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052929427
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1126/science.1078311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005962126
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1126/science.1167936 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010221272
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1126/science.8091226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062652231
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1145/1007730.1007731 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028225414
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1159/000083030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027004433
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1371/journal.pgen.0020190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029170965
153 rdf:type schema:CreativeWork
154 https://www.grid.ac/institutes/grid.412151.2 schema:alternateName King Mongkut's University of Technology Thonburi
155 schema:name Bioinformatics and Systems Biology Program, King Mongkut University of Technology, Thonburi, 10140, Bangkok, Thailand
156 School of Bioresources and Technology, King Mongkut University of Technology, Thonburi, 10140, Bangkok, Thailand
157 rdf:type schema:Organization
158 https://www.grid.ac/institutes/grid.419250.b schema:alternateName National Center for Genetic Engineering and Biotechnology
159 schema:name Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology, Thailand Science Park, Paholyothin Road, 12120, Pathumthani, Thailand
160 rdf:type schema:Organization
 




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


...