Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.7/347 |
Resumo: | High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithmsNext-generation sequencingSingle-nucleotide polymorphismsReduced-representation librariesBioinformaticsGATKSAMtoolsCLC genomics workbenchGreat apesHigh-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.Sabah Wildlife Department (SWD), Indonesian State Ministry for Research and Technology (RISTEK), Indonesian Institute of Sciences (LIPI), and Leuser International Foundation (LIF), Forschungskredit University of Zurich, A.H. Schultz Foundation, Swiss National Science Foundation grant no. 3100A-116848, Julius-Klaus Foundation, Leakey Foundation, and the Anthropological Institute & Museum at the University of Zurich.BioMed CentralARCAGreminger, Maja PStölting, Kai NNater, AlexanderGoossens, BenoitArora, NatashaBruggmann, RémyPatrignani, AndreaNussberger, BeatriceSharma, ReetaKraus, Robert H SAmbu, Laurentius NSingleton, IanChikhi, Lounesvan Schaik, Carel PKrützen, Michael2015-10-05T11:25:27Z2014-01-102014-01-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.7/347engGreminger et al. : Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct compariso ns of SNP calling algorithms. BMC Genomics 2014 15 :16.10.1186/1471-2164-15-16info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-29T14:34:45ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
title |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
spellingShingle |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms Greminger, Maja P Next-generation sequencing Single-nucleotide polymorphisms Reduced-representation libraries Bioinformatics GATK SAMtools CLC genomics workbench Great apes |
title_short |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
title_full |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
title_fullStr |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
title_full_unstemmed |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
title_sort |
Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms |
author |
Greminger, Maja P |
author_facet |
Greminger, Maja P Stölting, Kai N Nater, Alexander Goossens, Benoit Arora, Natasha Bruggmann, Rémy Patrignani, Andrea Nussberger, Beatrice Sharma, Reeta Kraus, Robert H S Ambu, Laurentius N Singleton, Ian Chikhi, Lounes van Schaik, Carel P Krützen, Michael |
author_role |
author |
author2 |
Stölting, Kai N Nater, Alexander Goossens, Benoit Arora, Natasha Bruggmann, Rémy Patrignani, Andrea Nussberger, Beatrice Sharma, Reeta Kraus, Robert H S Ambu, Laurentius N Singleton, Ian Chikhi, Lounes van Schaik, Carel P Krützen, Michael |
author2_role |
author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
ARCA |
dc.contributor.author.fl_str_mv |
Greminger, Maja P Stölting, Kai N Nater, Alexander Goossens, Benoit Arora, Natasha Bruggmann, Rémy Patrignani, Andrea Nussberger, Beatrice Sharma, Reeta Kraus, Robert H S Ambu, Laurentius N Singleton, Ian Chikhi, Lounes van Schaik, Carel P Krützen, Michael |
dc.subject.por.fl_str_mv |
Next-generation sequencing Single-nucleotide polymorphisms Reduced-representation libraries Bioinformatics GATK SAMtools CLC genomics workbench Great apes |
topic |
Next-generation sequencing Single-nucleotide polymorphisms Reduced-representation libraries Bioinformatics GATK SAMtools CLC genomics workbench Great apes |
description |
High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-10 2014-01-10T00:00:00Z 2015-10-05T11:25:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.7/347 |
url |
http://hdl.handle.net/10400.7/347 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Greminger et al. : Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct compariso ns of SNP calling algorithms. BMC Genomics 2014 15 :16. 10.1186/1471-2164-15-16 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
BioMed Central |
publisher.none.fl_str_mv |
BioMed Central |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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repository.mail.fl_str_mv |
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1777301592495620096 |