Simultaneous Phasing of Multiple Polyploids View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2020-01-23

AUTHORS

Laxmi Parida , Filippo Utro

ABSTRACT

We address the problem of phasing polyploids specifically with polyploidy larger than two. We consider the scenario where the input is the genotype of samples along a genic chromosomal segment. In this setting, instead of NGS reads of the segments of a sample, genotype data from multiple individuals is available for simultaneous phasing. For this mathematically interesting problem, with application in plant genomics, we design and test two algorithms under a parsimony model. The first is a linear time greedy algorithm and the second is a more carefully crafted algebraic algorithm. We show that both the methods work reasonably well (with accuracy on an average larger than 80%). The former is very time-efficient and the latter improves the accuracy further. More... »

PAGES

50-68

Book

TITLE

Computational Intelligence Methods for Bioinformatics and Biostatistics

ISBN

978-3-030-34584-6
978-3-030-34585-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-34585-3_5

DOI

http://dx.doi.org/10.1007/978-3-030-34585-3_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1124228555


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Parida", 
        "givenName": "Laxmi", 
        "id": "sg:person.01336557015.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336557015.68"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Utro", 
        "givenName": "Filippo", 
        "id": "sg:person.01176571007.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176571007.38"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2020-01-23", 
    "datePublishedReg": "2020-01-23", 
    "description": "We address the problem of phasing polyploids specifically with polyploidy larger than two. We consider the scenario where the input is the genotype of samples along a genic chromosomal segment. In this setting, instead of NGS reads of the segments of a sample, genotype data from multiple individuals is available for simultaneous phasing. For this mathematically interesting problem, with application in plant genomics, we design and test two algorithms under a parsimony model. The first is a linear time greedy algorithm and the second is a more carefully crafted algebraic algorithm. We show that both the methods work reasonably well (with accuracy on an average larger than 80%). The former is very time-efficient and the latter improves the accuracy further.", 
    "editor": [
      {
        "familyName": "Raposo", 
        "givenName": "Maria", 
        "type": "Person"
      }, 
      {
        "familyName": "Ribeiro", 
        "givenName": "Paulo", 
        "type": "Person"
      }, 
      {
        "familyName": "S\u00e9rio", 
        "givenName": "Susana", 
        "type": "Person"
      }, 
      {
        "familyName": "Staiano", 
        "givenName": "Antonino", 
        "type": "Person"
      }, 
      {
        "familyName": "Ciaramella", 
        "givenName": "Angelo", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-34585-3_5", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-030-34584-6", 
        "978-3-030-34585-3"
      ], 
      "name": "Computational Intelligence Methods for Bioinformatics and Biostatistics", 
      "type": "Book"
    }, 
    "keywords": [
      "linear time greedy algorithm", 
      "algebraic algorithm", 
      "time greedy algorithm", 
      "parsimony model", 
      "interesting problem", 
      "greedy algorithm", 
      "algorithm", 
      "problem", 
      "accuracy", 
      "model", 
      "genotype data", 
      "input", 
      "applications", 
      "phasing", 
      "NGS reads", 
      "scenarios", 
      "multiple individuals", 
      "data", 
      "segments", 
      "samples", 
      "setting", 
      "reads", 
      "genomics", 
      "method", 
      "chromosomal segments", 
      "plant genomics", 
      "individuals", 
      "polyploids", 
      "genotypes", 
      "polyploidy", 
      "genotypes of samples", 
      "simultaneous phasing"
    ], 
    "name": "Simultaneous Phasing of Multiple Polyploids", 
    "pagination": "50-68", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1124228555"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-34585-3_5"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-34585-3_5", 
      "https://app.dimensions.ai/details/publication/pub.1124228555"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-09-02T16:12", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/chapter/chapter_258.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-34585-3_5"
  }
]
 

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.10.1007/978-3-030-34585-3_5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-34585-3_5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-34585-3_5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-34585-3_5'


 

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

118 TRIPLES      22 PREDICATES      56 URIs      49 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-34585-3_5 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N331a0d6c3f4043b5aaec3ccd61e22dbd
4 schema:datePublished 2020-01-23
5 schema:datePublishedReg 2020-01-23
6 schema:description We address the problem of phasing polyploids specifically with polyploidy larger than two. We consider the scenario where the input is the genotype of samples along a genic chromosomal segment. In this setting, instead of NGS reads of the segments of a sample, genotype data from multiple individuals is available for simultaneous phasing. For this mathematically interesting problem, with application in plant genomics, we design and test two algorithms under a parsimony model. The first is a linear time greedy algorithm and the second is a more carefully crafted algebraic algorithm. We show that both the methods work reasonably well (with accuracy on an average larger than 80%). The former is very time-efficient and the latter improves the accuracy further.
7 schema:editor N4baef231ae3043698eef8d07d40e6f10
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N5bfa61f7571b42bbb39636c9b4854eab
11 schema:keywords NGS reads
12 accuracy
13 algebraic algorithm
14 algorithm
15 applications
16 chromosomal segments
17 data
18 genomics
19 genotype data
20 genotypes
21 genotypes of samples
22 greedy algorithm
23 individuals
24 input
25 interesting problem
26 linear time greedy algorithm
27 method
28 model
29 multiple individuals
30 parsimony model
31 phasing
32 plant genomics
33 polyploids
34 polyploidy
35 problem
36 reads
37 samples
38 scenarios
39 segments
40 setting
41 simultaneous phasing
42 time greedy algorithm
43 schema:name Simultaneous Phasing of Multiple Polyploids
44 schema:pagination 50-68
45 schema:productId Nd59f4e09f69743aa8be0a80980173661
46 Nf5c1f3b2b18746998969c4e6bd19dbe8
47 schema:publisher N2cc6fae013b14de3b55a76adfe9d4251
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124228555
49 https://doi.org/10.1007/978-3-030-34585-3_5
50 schema:sdDatePublished 2022-09-02T16:12
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher Nb78c3a66561f4f1dae7fcfab3c12c89c
53 schema:url https://doi.org/10.1007/978-3-030-34585-3_5
54 sgo:license sg:explorer/license/
55 sgo:sdDataset chapters
56 rdf:type schema:Chapter
57 N15dd5ee51a1a4337bbe4b4a95189b1ef schema:familyName Sério
58 schema:givenName Susana
59 rdf:type schema:Person
60 N2cc6fae013b14de3b55a76adfe9d4251 schema:name Springer Nature
61 rdf:type schema:Organisation
62 N331a0d6c3f4043b5aaec3ccd61e22dbd rdf:first sg:person.01336557015.68
63 rdf:rest Nb032c1ae05eb4dd484dffab3593b849d
64 N38102b3f471c471482eb9a5fd044570b rdf:first N60d363e6182e475d80e7ac36d3a277c2
65 rdf:rest N5265f1b88e084867861da3d7046bd930
66 N3c34cbf8514247598e4801225b3113ff rdf:first N15dd5ee51a1a4337bbe4b4a95189b1ef
67 rdf:rest N38102b3f471c471482eb9a5fd044570b
68 N3f1234376bbf4266a7c964c2df9f5134 schema:familyName Ciaramella
69 schema:givenName Angelo
70 rdf:type schema:Person
71 N4baef231ae3043698eef8d07d40e6f10 rdf:first Nfd46c47c45c7402ca689ebe7e3ced1d0
72 rdf:rest N83e8b710487448da8094ebc493e1fc1d
73 N5265f1b88e084867861da3d7046bd930 rdf:first N3f1234376bbf4266a7c964c2df9f5134
74 rdf:rest rdf:nil
75 N5bfa61f7571b42bbb39636c9b4854eab schema:isbn 978-3-030-34584-6
76 978-3-030-34585-3
77 schema:name Computational Intelligence Methods for Bioinformatics and Biostatistics
78 rdf:type schema:Book
79 N60d363e6182e475d80e7ac36d3a277c2 schema:familyName Staiano
80 schema:givenName Antonino
81 rdf:type schema:Person
82 N83e8b710487448da8094ebc493e1fc1d rdf:first Na1871e526eee4aa6a01566e714c57799
83 rdf:rest N3c34cbf8514247598e4801225b3113ff
84 Na1871e526eee4aa6a01566e714c57799 schema:familyName Ribeiro
85 schema:givenName Paulo
86 rdf:type schema:Person
87 Nb032c1ae05eb4dd484dffab3593b849d rdf:first sg:person.01176571007.38
88 rdf:rest rdf:nil
89 Nb78c3a66561f4f1dae7fcfab3c12c89c schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 Nd59f4e09f69743aa8be0a80980173661 schema:name doi
92 schema:value 10.1007/978-3-030-34585-3_5
93 rdf:type schema:PropertyValue
94 Nf5c1f3b2b18746998969c4e6bd19dbe8 schema:name dimensions_id
95 schema:value pub.1124228555
96 rdf:type schema:PropertyValue
97 Nfd46c47c45c7402ca689ebe7e3ced1d0 schema:familyName Raposo
98 schema:givenName Maria
99 rdf:type schema:Person
100 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
101 schema:name Information and Computing Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
104 schema:name Artificial Intelligence and Image Processing
105 rdf:type schema:DefinedTerm
106 sg:person.01176571007.38 schema:affiliation grid-institutes:None
107 schema:familyName Utro
108 schema:givenName Filippo
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176571007.38
110 rdf:type schema:Person
111 sg:person.01336557015.68 schema:affiliation grid-institutes:None
112 schema:familyName Parida
113 schema:givenName Laxmi
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336557015.68
115 rdf:type schema:Person
116 grid-institutes:None schema:alternateName Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA
117 schema:name Computational Biology Center, IBM T. J. Watson Research, 10598, Yorktown Heights, NY, USA
118 rdf:type schema:Organization
 




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


...