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Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms

2014-01-10Journal article Open access
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dc.contributor.authorGreminger, Maja P
dc.contributor.authorStölting, Kai N
dc.contributor.authorNater, Alexander
dc.contributor.authorGoossens, Benoit
dc.contributor.authorArora, Natasha
dc.contributor.authorBruggmann, Rémy
dc.contributor.authorPatrignani, Andrea
dc.contributor.authorNussberger, Beatrice
dc.contributor.authorSharma, Reeta
dc.contributor.authorKraus, Robert H S
dc.contributor.authorAmbu, Laurentius N
dc.contributor.authorSingleton, Ian
dc.contributor.authorChikhi, Lounes
dc.contributor.authorvan Schaik, Carel P
dc.contributor.authorKrützen, Michael
dc.date.accessioned2015-10-05T11:25:27Z
dc.date.available2015-10-05T11:25:27Z
dc.date.issued2014-01-10
dc.description.abstractHigh-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.pt_PT
dc.description.sponsorshipSabah 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.pt_PT
dc.identifier10.1186/1471-2164-15-16
dc.identifier.citationGreminger 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.pt_PT
dc.identifier.doi10.1186/1471-2164-15-16
dc.identifier.doi10.1186/1471-2164-15-16
dc.identifier.doi10.1186/1471-2164-15-16
dc.identifier.urihttp://hdl.handle.net/10400.7/347
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBioMed Centralpt_PT
dc.relation.publisherversionhttp://www.biomedcentral.com/1471-2164/15/16pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNext-generation sequencingpt_PT
dc.subjectSingle-nucleotide polymorphismspt_PT
dc.subjectReduced-representation librariespt_PT
dc.subjectBioinformaticspt_PT
dc.subjectGATKpt_PT
dc.subjectSAMtoolspt_PT
dc.subjectCLC genomics workbenchpt_PT
dc.subjectGreat apespt_PT
dc.titleGeneration of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithmspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage15pt_PT
oaire.citation.issue16pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleBMC Genomicspt_PT
oaire.citation.volume15pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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