Show simple item record

dc.contributor.authorWright, Belinda R.
dc.contributor.authorFarquharson, Katherine A.
dc.contributor.authorMcLennan, Elspeth A.
dc.contributor.authorBelov, Katherine
dc.contributor.authorHogg, Carolyn J.
dc.contributor.authorGrueber, Catherine E.
dc.date.accessioned2020-04-29T21:30:31Z
dc.date.available2020-04-29T21:30:31Z
dc.date.issued2019
dc.identifier1471-2164
dc.identifier.doi10.1186/s12864-019-5806-y
dc.identifier.urihttp://hdl.handle.net/20.500.12634/92
dc.description.abstractRecent advances in genomics have greatly increased research opportunities for non-model species. For wildlife, a growing availability of reference genomes means that population genetics is no longer restricted to a small set of anonymous loci. When used in conjunction with a reference genome, reduced-representation sequencing (RRS) provides a cost-effective method for obtaining reliable diversity information for population genetics. Many software tools have been developed to process RRS data, though few studies of non-model species incorporate genome alignment in calling loci. A commonly-used RRS analysis pipeline, Stacks, has this capacity and so it is timely to compare its utility with existing software originally designed for alignment and analysis of whole genome sequencing data. Here we examine population genetic inferences from two species for which reference-aligned reduced-representation data have been collected. Our two study species are a threatened Australian marsupial (Tasmanian devil Sarcophilus harrisii; declining population) and an Arctic-circle migrant bird (pink-footed goose Anser brachyrhynchus; expanding population). Analyses of these data are compared using Stacks versus two widely-used genomics packages, SAMtools and GATK. We also introduce a custom R script to improve the reliability of single nucleotide polymorphism (SNP) calls in all pipelines and conduct population genetic inferences for non-model species with reference genomes.
dc.language.isoen
dc.relation.urlhttps://doi.org/10.1186/s12864-019-5806-y
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectTASMANIAN DEVILS
dc.subjectGENOMICS
dc.subjectGEESE
dc.subjectTECHNOLOGY
dc.titleFrom reference genomes to population genomics: comparing three reference-aligned reduced-representation sequencing pipelines in two wildlife species
dc.typeArticle
dc.source.journaltitleBMC Genomics
dc.source.volume20
dc.source.issue1
dc.source.beginpage453
refterms.dateFOA2020-05-01T17:51:23Z
html.description.abstractRecent advances in genomics have greatly increased research opportunities for non-model species. For wildlife, a growing availability of reference genomes means that population genetics is no longer restricted to a small set of anonymous loci. When used in conjunction with a reference genome, reduced-representation sequencing (RRS) provides a cost-effective method for obtaining reliable diversity information for population genetics. Many software tools have been developed to process RRS data, though few studies of non-model species incorporate genome alignment in calling loci. A commonly-used RRS analysis pipeline, Stacks, has this capacity and so it is timely to compare its utility with existing software originally designed for alignment and analysis of whole genome sequencing data. Here we examine population genetic inferences from two species for which reference-aligned reduced-representation data have been collected. Our two study species are a threatened Australian marsupial (Tasmanian devil Sarcophilus harrisii; declining population) and an Arctic-circle migrant bird (pink-footed goose Anser brachyrhynchus; expanding population). Analyses of these data are compared using Stacks versus two widely-used genomics packages, SAMtools and GATK. We also introduce a custom R script to improve the reliability of single nucleotide polymorphism (SNP) calls in all pipelines and conduct population genetic inferences for non-model species with reference genomes.


Files in this item

Thumbnail
Name:
Wright_2019_BMC Genomics.pdf
Size:
1.105Mb
Format:
PDF

This item appears in the following Collection(s)

  • SDZWA Research Publications
    Peer reviewed and scientific works by San Diego Zoo Wildlife Alliance staff. Includes books, book sections, articles and conference publications and presentations.

Show simple item record

https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/