Updog provides a suite of methods for genotyping polyploids from next-generation sequencing (NGS) data. It does this while accounting for many common features of NGS data: allele bias, overdispersion, and sequencing error. It is named updog for “Using Parental Data for Offspring Genotyping” because we originally developed the method for full-sib populations, but it works now for more general populations. The method is described in detail Gerard et. al. (2018) <doi:10.1534/genetics.118.301468>. Additional details concerning prior specification are described in Gerard and Ferrão (2020) <doi:10.1093/bioinformatics/btz852>.
The main functions are flexdog() and multidog(), which provide many options for the distribution of the genotypes in your sample. A novel genotype distribution is included in the class of proportional normal distributions (model = "norm"). This is the default prior distribution because it is the most robust to varying genotype distributions, but feel free to use more specialized priors if you have more information on the data.
Also provided are:
filter_snp(): filter out SNPs based on the output of multidog().format_multidog(): format the output of multidog() in terms of a multidimensional array.flexdog() and multidog() have plot methods. See the help files of plot.flexdog() and plot.multidog() for details.rgeno()) and read-counts (rflexdog()). These support all of the models available in flexdog().oracle_joint(), oracle_mis(), oracle_mis_vec(), and oracle_cor(). We mean “oracle” in the sense that we assume that the entire data generation process is known (i.e. the genotype distribution, sequencing error rate, allele bias, and overdispersion are all known). These are good approximations when there are a lot of individuals (but not necessarily large read-depth).The original updog package is now named updogAlpha and may be found here.
See also ebg, fitPoly, and polyRAD. Our best “competitor” is probably fitPoly, though polyRAD has some nice ideas for utilizing population structure and linkage disequilibrium.
See NEWS for the latest updates on the package.
I’ve included many vignettes in updog, which you can access online here.
If you find a bug or want an enhancement, please submit an issue here.
You can install updog from CRAN in the usual way:
You can install the current (unstable) version of updog from GitHub with:
Please cite
Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. doi: 10.1534/genetics.118.301468.
Or, using BibTex:
@article {gerard2018genotyping,
author = {Gerard, David and Ferr{\~a}o, Lu{\'i}s Felipe Ventorim and Garcia, Antonio Augusto Franco and Stephens, Matthew},
title = {Genotyping Polyploids from Messy Sequencing Data},
volume = {210},
number = {3},
pages = {789--807},
year = {2018},
doi = {10.1534/genetics.118.301468},
publisher = {Genetics},
issn = {0016-6731},
URL = {https://doi.org/10.1534/genetics.118.301468},
journal = {Genetics}
}If you are using the proportional normal prior class (model = "norm"), which is also the default prior, then please also cite:
Gerard D, Ferrão L (2020). “Priors for Genotyping Polyploids.” Bioinformatics, 36(6), 1795-1800. ISSN 1367-4803, doi: 10.1093/bioinformatics/btz852.
Or, using BibTex:
@article{gerard2020priors,
title = {Priors for Genotyping Polyploids},
year = {2020},
journal = {Bioinformatics},
publisher = {Oxford University Press},
volume = {36},
number = {6},
pages = {1795--1800},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz852},
author = {David Gerard and Lu{\'i}s Felipe Ventorim Ferr{\~a}o},
}Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.