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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: ubib.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# updogAlpha: Using Parental Data for Offspring Genotyping
[![DOI](https://zenodo.org/badge/73749148.svg)](https://zenodo.org/badge/latestdoi/73749148)
This is the original code for the updog procedure. To use the new updog package, please see [here](https://github.com/dcgerard/updog).
This package will fit an empirical Bayesian procedure to genotype autopolyploid individuals from reduced-representation next-generation sequencing (NGS) data, such as genotyping by sequencing (GBS) [@elshire2011robust] or restriction site-associated DNA sequencing (RAD-Seq) [@baird2008rapid]. For such NGS data there exist other methods for genotyping --- see for example [ebg](https://github.com/pblischak/polyploid-genotyping) [@blischak2017snp] and [TET](http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.039008/-/DC1) [@maruki2017genotype]. `updog` adds to this field by:
- Incorporating hierarchical structure into the genotyping procedure by either assuming the population consists of siblings (either an F1 or S1 population), or the population is in Hardy-Weinberg equilibrium. Though see @serang2012efficient and @li2011statistical for other discussion on this.
- Estimating sequencing error for each SNP.
- Accounting for read-mapping bias.
- Incorporating overdispersion, a key indicator for the quality of a SNP.
- Automatically accounting for outlying observations.
- Beautifully plotting the NGS data (see `plot.updog`).
- Running a simple goodness of fit test (see `summary.updog`).
- Simulating under a fitted model (see `rupdog`).
We've included a few SNP's from the data of @shirasawa2017high to show off the features of `updog`. See `snpdata`.
A vignette is available [here](https://dcgerard.github.io/updog/articles/smells_like_updog.html).
Please report any bugs/issues [here](https://github.com/dcgerard/updog/issues).
# Installation
To install, run the following code in R:
``` {r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("dcgerard/updog")
```
# Citation
If you find the methods in this package useful, please cite
> Gerard, D., Ferrão L.F.V., Garcia, A.A.F., & Stephens, M. (2018). Harnessing Empirical Bayes and Mendelian Segregation for Genotyping Autopolyploids from Messy Sequencing Data. *bioRxiv*. doi: [10.1101/281550](https://doi.org/10.1101/281550).
Or, using BibTex:
``` tex
@article {gerard2018harnessing,
author = {Gerard, David and Ferr{\~a}o, Luis Felipe Ventorim and Garcia, Antonio Augusto Franco and Stephens, Matthew},
title = {Harnessing Empirical Bayes and Mendelian Segregation for Genotyping Autopolyploids from Messy Sequencing Data},
year = {2018},
doi = {10.1101/281550},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2018/03/16/281550},
eprint = {https://www.biorxiv.org/content/early/2018/03/16/281550.full.pdf},
journal = {bioRxiv}
}
```
# References