SNP calling by sequencing pooled samples View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-12

AUTHORS

Emanuele Raineri, Luca Ferretti, Anna Esteve-Codina, Bruno Nevado, Simon Heath, Miguel Pérez-Enciso

ABSTRACT

BACKGROUND: Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) ∝ 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read - or, more likely, none - from a true singleton. RESULTS: To improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools (and implemented it in a program called snape): the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages. CONCLUSIONS: We present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of f for every SNP. In order to test the behaviour of our software, we generated (through simulated coalescence) artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power ≈ 35%and FDR ≈ 2.5%. snape is available at http://code.google.com/p/snape-pooled/ (source code and precompiled binaries). More... »

PAGES

239

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-13-239

DOI

http://dx.doi.org/10.1186/1471-2105-13-239

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036852204

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22992255


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": "Alleles", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bayes Theorem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Frequency", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Polymorphism, Single Nucleotide", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Barcelona", 
          "id": "https://www.grid.ac/institutes/grid.5841.8", 
          "name": [
            "Centro Nacional de An\u00e1lisis Gen\u00f3mico (CNAG), Parc Cient\u00edfic de Barcelona, 08028, Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raineri", 
        "givenName": "Emanuele", 
        "id": "sg:person.01034067302.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034067302.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Centre for Research in Agricultural Genomics (CRAG), Universitat Aut\u00f2nonoma de Barcelona, 08193, Bellaterra, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ferretti", 
        "givenName": "Luca", 
        "id": "sg:person.0604607310.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604607310.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Centre for Research in Agricultural Genomics (CRAG), Universitat Aut\u00f2nonoma de Barcelona, 08193, Bellaterra, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Esteve-Codina", 
        "givenName": "Anna", 
        "id": "sg:person.01331776746.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331776746.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Centre for Research in Agricultural Genomics (CRAG), Universitat Aut\u00f2nonoma de Barcelona, 08193, Bellaterra, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nevado", 
        "givenName": "Bruno", 
        "id": "sg:person.0576211540.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576211540.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Barcelona", 
          "id": "https://www.grid.ac/institutes/grid.5841.8", 
          "name": [
            "Centro Nacional de An\u00e1lisis Gen\u00f3mico (CNAG), Parc Cient\u00edfic de Barcelona, 08028, Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heath", 
        "givenName": "Simon", 
        "id": "sg:person.01276712002.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276712002.66"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats", 
          "id": "https://www.grid.ac/institutes/grid.425902.8", 
          "name": [
            "Centre for Research in Agricultural Genomics (CRAG), Universitat Aut\u00f2nonoma de Barcelona, 08193, Bellaterra, Spain", 
            "Institut Catal\u00e0 de Recerca i Estudis Avan\u00e7ats (ICREA), Passeig Llu\u00eds Companys 23, 08010, Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "P\u00e9rez-Enciso", 
        "givenName": "Miguel", 
        "id": "sg:person.01041433710.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041433710.67"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr708", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008059544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp373", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008270984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.24.7.253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008937833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0014782", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011124548"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.139949", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011603631"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.139949", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011603631"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp698", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012031985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.2.337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016789793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0370164600044886", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019189995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp352", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023014918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2052.2010.02057.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026192892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2052.2010.02057.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026192892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0015925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032227304"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.110.114397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045585344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.110.114397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045585344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3211856", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070226100"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1081021735", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-12", 
    "datePublishedReg": "2012-12-01", 
    "description": "BACKGROUND: Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) \u221d 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read - or, more likely, none - from a true singleton.\nRESULTS: To improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools (and implemented it in a program called snape): the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages.\nCONCLUSIONS: We present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of f for every SNP. In order to test the behaviour of our software, we generated (through simulated coalescence) artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power \u2248 35%and FDR \u2248 2.5%. snape is available at http://code.google.com/p/snape-pooled/ (source code and precompiled binaries).", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-13-239", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "name": "SNP calling by sequencing pooled samples", 
    "pagination": "239", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "af8cd71d21b1a035a413654f032b4d05adcc641d60fabacc3b11cd2270327cfc"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "22992255"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-13-239"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1036852204"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-13-239", 
      "https://app.dimensions.ai/details/publication/pub.1036852204"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:32", 
    "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/v90/0000000349_0000000349/records_113658_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2F1471-2105-13-239"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-239'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-239'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-239'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-239'


 

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

190 TRIPLES      21 PREDICATES      52 URIs      30 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-13-239 schema:about N05387c163f324df59ae6eab134a8b886
2 N0fa88ac379674d6bb553e350368937eb
3 N21ff81392ac6492cb232edc1965a3d9a
4 N99f73c5b4ab84281b8f240ab137cfa04
5 Na1f18325a0d3446cbf7cd4ba1b735af5
6 Nbe0bf9b234344ee0a4f3968c66a4bfcb
7 Ne8254504691641409ecba000d76aca81
8 Nec798c639c064611b338ba680e629da4
9 Nfe30b837b3a94b5587aa696a9c4b77e8
10 anzsrc-for:06
11 anzsrc-for:0604
12 schema:author N0d4318dc713649f591a786119be552e0
13 schema:citation https://app.dimensions.ai/details/publication/pub.1081021735
14 https://doi.org/10.1017/s0370164600044886
15 https://doi.org/10.1073/pnas.24.7.253
16 https://doi.org/10.1093/bioinformatics/18.2.337
17 https://doi.org/10.1093/bioinformatics/btp352
18 https://doi.org/10.1093/bioinformatics/btp373
19 https://doi.org/10.1093/bioinformatics/btp698
20 https://doi.org/10.1093/bioinformatics/btr708
21 https://doi.org/10.1111/j.1365-2052.2010.02057.x
22 https://doi.org/10.1371/journal.pone.0014782
23 https://doi.org/10.1371/journal.pone.0015925
24 https://doi.org/10.1534/genetics.110.114397
25 https://doi.org/10.1534/genetics.112.139949
26 https://doi.org/10.2307/3211856
27 schema:datePublished 2012-12
28 schema:datePublishedReg 2012-12-01
29 schema:description BACKGROUND: Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) ∝ 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read - or, more likely, none - from a true singleton. RESULTS: To improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools (and implemented it in a program called snape): the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages. CONCLUSIONS: We present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of f for every SNP. In order to test the behaviour of our software, we generated (through simulated coalescence) artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power ≈ 35%and FDR ≈ 2.5%. snape is available at http://code.google.com/p/snape-pooled/ (source code and precompiled binaries).
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf N1252c483ea4f46bc9c94f18759aca250
34 N55505abd008f4d34823e095e0a360562
35 sg:journal.1023786
36 schema:name SNP calling by sequencing pooled samples
37 schema:pagination 239
38 schema:productId N121f20ab5d384138b34623851b4a3cfe
39 N27d5f11abc8740bc8ee734f4a66e7e32
40 N72719809fd094c5fb111f54631b3d7d9
41 N83f1870e46a54115b63cab3e715ccc9f
42 N851a787c70ca468d99852a6866b0acc0
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036852204
44 https://doi.org/10.1186/1471-2105-13-239
45 schema:sdDatePublished 2019-04-11T10:32
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N87021582d9684eb496015ec455c9506b
48 schema:url https://link.springer.com/10.1186%2F1471-2105-13-239
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N05387c163f324df59ae6eab134a8b886 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
53 schema:name Polymorphism, Single Nucleotide
54 rdf:type schema:DefinedTerm
55 N0d4318dc713649f591a786119be552e0 rdf:first sg:person.01034067302.26
56 rdf:rest N18df913c7ee6434ba88e16a04e82017f
57 N0fa88ac379674d6bb553e350368937eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
58 schema:name Gene Frequency
59 rdf:type schema:DefinedTerm
60 N11ceb6d5681d4276b5ec31a292760d3b rdf:first sg:person.0576211540.39
61 rdf:rest N50731525b71047769203e9df2efb55d3
62 N121f20ab5d384138b34623851b4a3cfe schema:name readcube_id
63 schema:value af8cd71d21b1a035a413654f032b4d05adcc641d60fabacc3b11cd2270327cfc
64 rdf:type schema:PropertyValue
65 N1252c483ea4f46bc9c94f18759aca250 schema:volumeNumber 13
66 rdf:type schema:PublicationVolume
67 N18df913c7ee6434ba88e16a04e82017f rdf:first sg:person.0604607310.31
68 rdf:rest Nc95c0c36e45f46b9bf428b0ee6101187
69 N21ff81392ac6492cb232edc1965a3d9a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Sequence Analysis, DNA
71 rdf:type schema:DefinedTerm
72 N27d5f11abc8740bc8ee734f4a66e7e32 schema:name dimensions_id
73 schema:value pub.1036852204
74 rdf:type schema:PropertyValue
75 N3fdc532f3dae454fbdbb68a337d15f41 schema:name Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain
76 rdf:type schema:Organization
77 N4e3657b10c9a4bb79b2ed216bfd6f39d schema:name Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain
78 rdf:type schema:Organization
79 N50731525b71047769203e9df2efb55d3 rdf:first sg:person.01276712002.66
80 rdf:rest N98c0ffad0a1c4dcb935cacadd3331691
81 N55505abd008f4d34823e095e0a360562 schema:issueNumber 1
82 rdf:type schema:PublicationIssue
83 N72719809fd094c5fb111f54631b3d7d9 schema:name nlm_unique_id
84 schema:value 100965194
85 rdf:type schema:PropertyValue
86 N83f1870e46a54115b63cab3e715ccc9f schema:name doi
87 schema:value 10.1186/1471-2105-13-239
88 rdf:type schema:PropertyValue
89 N851a787c70ca468d99852a6866b0acc0 schema:name pubmed_id
90 schema:value 22992255
91 rdf:type schema:PropertyValue
92 N87021582d9684eb496015ec455c9506b schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N98c0ffad0a1c4dcb935cacadd3331691 rdf:first sg:person.01041433710.67
95 rdf:rest rdf:nil
96 N99f73c5b4ab84281b8f240ab137cfa04 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Humans
98 rdf:type schema:DefinedTerm
99 Na1f18325a0d3446cbf7cd4ba1b735af5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Software
101 rdf:type schema:DefinedTerm
102 Na3221840c1134f4488ab5a581d1005ee schema:name Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain
103 rdf:type schema:Organization
104 Nbe0bf9b234344ee0a4f3968c66a4bfcb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Bayes Theorem
106 rdf:type schema:DefinedTerm
107 Nc95c0c36e45f46b9bf428b0ee6101187 rdf:first sg:person.01331776746.94
108 rdf:rest N11ceb6d5681d4276b5ec31a292760d3b
109 Ne8254504691641409ecba000d76aca81 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Genome
111 rdf:type schema:DefinedTerm
112 Nec798c639c064611b338ba680e629da4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name High-Throughput Nucleotide Sequencing
114 rdf:type schema:DefinedTerm
115 Nfe30b837b3a94b5587aa696a9c4b77e8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Alleles
117 rdf:type schema:DefinedTerm
118 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
119 schema:name Biological Sciences
120 rdf:type schema:DefinedTerm
121 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
122 schema:name Genetics
123 rdf:type schema:DefinedTerm
124 sg:journal.1023786 schema:issn 1471-2105
125 schema:name BMC Bioinformatics
126 rdf:type schema:Periodical
127 sg:person.01034067302.26 schema:affiliation https://www.grid.ac/institutes/grid.5841.8
128 schema:familyName Raineri
129 schema:givenName Emanuele
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034067302.26
131 rdf:type schema:Person
132 sg:person.01041433710.67 schema:affiliation https://www.grid.ac/institutes/grid.425902.8
133 schema:familyName Pérez-Enciso
134 schema:givenName Miguel
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041433710.67
136 rdf:type schema:Person
137 sg:person.01276712002.66 schema:affiliation https://www.grid.ac/institutes/grid.5841.8
138 schema:familyName Heath
139 schema:givenName Simon
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276712002.66
141 rdf:type schema:Person
142 sg:person.01331776746.94 schema:affiliation Na3221840c1134f4488ab5a581d1005ee
143 schema:familyName Esteve-Codina
144 schema:givenName Anna
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331776746.94
146 rdf:type schema:Person
147 sg:person.0576211540.39 schema:affiliation N4e3657b10c9a4bb79b2ed216bfd6f39d
148 schema:familyName Nevado
149 schema:givenName Bruno
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576211540.39
151 rdf:type schema:Person
152 sg:person.0604607310.31 schema:affiliation N3fdc532f3dae454fbdbb68a337d15f41
153 schema:familyName Ferretti
154 schema:givenName Luca
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604607310.31
156 rdf:type schema:Person
157 https://app.dimensions.ai/details/publication/pub.1081021735 schema:CreativeWork
158 https://doi.org/10.1017/s0370164600044886 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019189995
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1073/pnas.24.7.253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008937833
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1093/bioinformatics/18.2.337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016789793
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1093/bioinformatics/btp352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023014918
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1093/bioinformatics/btp373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008270984
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1093/bioinformatics/btp698 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012031985
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1093/bioinformatics/btr708 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008059544
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1111/j.1365-2052.2010.02057.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026192892
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1371/journal.pone.0014782 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011124548
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1371/journal.pone.0015925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032227304
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1534/genetics.110.114397 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045585344
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1534/genetics.112.139949 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011603631
181 rdf:type schema:CreativeWork
182 https://doi.org/10.2307/3211856 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070226100
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.425902.8 schema:alternateName Institució Catalana de Recerca i Estudis Avançats
185 schema:name Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain
186 Institut Català de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.5841.8 schema:alternateName University of Barcelona
189 schema:name Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, 08028, Barcelona, Spain
190 rdf:type schema:Organization
 




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


...