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AdmixPipe: population analyses in Admixture for non-model organisms

BACKGROUND: Research on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed a...

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Autores principales: Mussmann, Steven M., Douglas, Marlis R., Chafin, Tyler K., Douglas, Michael E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391514/
https://www.ncbi.nlm.nih.gov/pubmed/32727359
http://dx.doi.org/10.1186/s12859-020-03701-4
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author Mussmann, Steven M.
Douglas, Marlis R.
Chafin, Tyler K.
Douglas, Michael E.
author_facet Mussmann, Steven M.
Douglas, Marlis R.
Chafin, Tyler K.
Douglas, Michael E.
author_sort Mussmann, Steven M.
collection PubMed
description BACKGROUND: Research on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed available, then limited by the necessity of a comparable reference genome as an adjunct. This, of course, can be difficult when working with non-model organisms. Fortunately, pipelines are currently available that avoid this prerequisite, thus allowing data to be a priori parsed. An oft-used molecular ecology program (i.e., Structure), for example, is facilitated by such pipelines, yet they are surprisingly absent for a second program that is similarly popular and computationally more efficient (i.e., Admixture). The two programs differ in that Admixture employs a maximum-likelihood framework whereas Structure uses a Bayesian approach, yet both produce similar results. Given these issues, there is an overriding (and recognized) need among researchers in molecular ecology for bioinformatic software that will not only condense output from replicated Admixture runs, but also infer from these data the optimal number of population clusters (K). RESULTS: Here we provide such a program (i.e., AdmixPipe) that (a) filters SNPs to allow the delineation of population structure in Admixture, then (b) parses the output for summarization and graphical representation via Clumpak. Our benchmarks effectively demonstrate how efficient the pipeline is for processing large, non-model datasets generated via double digest restriction-site associated DNA sequencing (ddRAD). Outputs not only parallel those from Structure, but also visualize the variation among individual Admixture runs, so as to facilitate selection of the most appropriate K-value. CONCLUSIONS: AdmixPipe successfully integrates Admixture analysis with popular variant call format (VCF) filtering software to yield file types readily analyzed by Clumpak. Large population genomic datasets derived from non-model organisms are efficiently analyzed via the parallel-processing capabilities of Admixture. AdmixPipe is distributed under the GNU Public License and freely available for Mac OSX and Linux platforms at: https://github.com/stevemussmann/admixturePipeline.
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spelling pubmed-73915142020-07-31 AdmixPipe: population analyses in Admixture for non-model organisms Mussmann, Steven M. Douglas, Marlis R. Chafin, Tyler K. Douglas, Michael E. BMC Bioinformatics Software BACKGROUND: Research on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed available, then limited by the necessity of a comparable reference genome as an adjunct. This, of course, can be difficult when working with non-model organisms. Fortunately, pipelines are currently available that avoid this prerequisite, thus allowing data to be a priori parsed. An oft-used molecular ecology program (i.e., Structure), for example, is facilitated by such pipelines, yet they are surprisingly absent for a second program that is similarly popular and computationally more efficient (i.e., Admixture). The two programs differ in that Admixture employs a maximum-likelihood framework whereas Structure uses a Bayesian approach, yet both produce similar results. Given these issues, there is an overriding (and recognized) need among researchers in molecular ecology for bioinformatic software that will not only condense output from replicated Admixture runs, but also infer from these data the optimal number of population clusters (K). RESULTS: Here we provide such a program (i.e., AdmixPipe) that (a) filters SNPs to allow the delineation of population structure in Admixture, then (b) parses the output for summarization and graphical representation via Clumpak. Our benchmarks effectively demonstrate how efficient the pipeline is for processing large, non-model datasets generated via double digest restriction-site associated DNA sequencing (ddRAD). Outputs not only parallel those from Structure, but also visualize the variation among individual Admixture runs, so as to facilitate selection of the most appropriate K-value. CONCLUSIONS: AdmixPipe successfully integrates Admixture analysis with popular variant call format (VCF) filtering software to yield file types readily analyzed by Clumpak. Large population genomic datasets derived from non-model organisms are efficiently analyzed via the parallel-processing capabilities of Admixture. AdmixPipe is distributed under the GNU Public License and freely available for Mac OSX and Linux platforms at: https://github.com/stevemussmann/admixturePipeline. BioMed Central 2020-07-29 /pmc/articles/PMC7391514/ /pubmed/32727359 http://dx.doi.org/10.1186/s12859-020-03701-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Mussmann, Steven M.
Douglas, Marlis R.
Chafin, Tyler K.
Douglas, Michael E.
AdmixPipe: population analyses in Admixture for non-model organisms
title AdmixPipe: population analyses in Admixture for non-model organisms
title_full AdmixPipe: population analyses in Admixture for non-model organisms
title_fullStr AdmixPipe: population analyses in Admixture for non-model organisms
title_full_unstemmed AdmixPipe: population analyses in Admixture for non-model organisms
title_short AdmixPipe: population analyses in Admixture for non-model organisms
title_sort admixpipe: population analyses in admixture for non-model organisms
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391514/
https://www.ncbi.nlm.nih.gov/pubmed/32727359
http://dx.doi.org/10.1186/s12859-020-03701-4
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