@article{TEXTUAL,
      recid = {12832},
      author = {Gaynor, Michelle L. and Landis, Jacob B. and O'Connor,  Timothy K. and Laport, Robert G. and Doyle, Jeff J. and  Soltis, Douglas E. and Ponciano, José Miguel and Soltis,  Pamela S.},
      title = {nQuack: An R package for predicting ploidal level from  sequence data using site-based heterozygosity},
      journal = {Applications in Plant Sciences},
      address = {2024-07-14},
      number = {TEXTUAL},
      abstract = {<p>Premise: Traditional methods of ploidal-level  estimation are tedious; using DNA sequence data for  cytotype estimation is an ideal alternative. Multiple  statistical approaches to leverage sequence data for ploidy  inference based on site-based heterozygosity have been  developed. However, these approaches may require  high-coverage sequence data, use inappropriate probability  distributions, or have additional statistical shortcomings  that limit inference abilities. We introduce nQuack, an  open-source R package that addresses the main shortcomings  of current methods.</p> <p>Methods and Results: nQuack  performs model selection for improved ploidy predictions.  Here, we implement expectation maximization algorithms with  normal, beta, and beta-binomial distributions. Using  extensive computer simulations that account for variability  in sequencing depth, as well as real data sets, we  demonstrate the utility and limitations of nQuack.</p>  <p>Conclusions: Inferring ploidy based on site-based  heterozygosity alone is difficult. Even though nQuack is  more accurate than similar methods, we suggest caution when  relying on any site-based heterozygosity method to infer  ploidy.</p>},
      url = {http://knowledge.uchicago.edu/record/12832},
}