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SNP datasets used in the paper “Floristic classifications and bioregionalizations are not predictors of intra-specific evolutionary patterns”

The University of Queensland
Dr Patrick Fahey (Aggregated by) Dr Patrick Fahey (Aggregated by) Honorary Professor Maurizio Rossetto (Aggregated by) Mr Richard Dimon (Aggregated by) Mr Richard Dimon (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/5c76de2&rft.title=SNP datasets used in the paper “Floristic classifications and bioregionalizations are not predictors of intra-specific evolutionary patterns”&rft.identifier=RDM ID: 7dc1d180-42d7-11ee-86d2-97cd69273a70&rft.publisher=The University of Queensland&rft.description=The SNP dataset for each species investigated in this study is present. These datasets are saved as R data objects in list formats with meta-data for samples and post filtering DArTseq SNPs. Sample filtering included removing samples which we suspected to be mis-identified taxa, hybrids and those with >50% missing data, after which any samples from populations with less than 5 suitable samples remaining were also removed. SNP filtering included removing loci with reproducibility values below 0.96, missingness of >20%, followed by subsampling to one SNP per locus to remove any linkage effects. Datasets can be read into R, where they are formatted as list objects.&rft.creator=Dr Patrick Fahey&rft.creator=Dr Patrick Fahey&rft.creator=Honorary Professor Maurizio Rossetto&rft.creator=Mr Richard Dimon&rft.creator=Mr Richard Dimon&rft.date=2023&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Computational ecology and phylogenetics&rft_subject=Bioinformatics and computational biology&rft_subject=BIOLOGICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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[email protected]
Queensland Alliance for Agriculture and Food Innovation

Full description

The SNP dataset for each species investigated in this study is present. These datasets are saved as R data objects in list formats with meta-data for samples and post filtering DArTseq SNPs. Sample filtering included removing samples which we suspected to be mis-identified taxa, hybrids and those with >50% missing data, after which any samples from populations with less than 5 suitable samples remaining were also removed. SNP filtering included removing loci with reproducibility values below 0.96, missingness of >20%, followed by subsampling to one SNP per locus to remove any linkage effects. Datasets can be read into R, where they are formatted as list objects.

Issued: 28 08 2023

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Subjects
Biological Sciences | Bioinformatics and Computational Biology | Computational Ecology and Phylogenetics | eng |

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Identifiers
ACN 633 798 857