De novo mutations (DNMs) can have a profound impact on our understanding of evolution and genetic disease, and as a result identification of these is extremely important in the fields of evolutionary biology and medical genomics. However, complicating the isolation of DNMs is a combination of factors including but not limited to sequencing errors and sequencing depth. Further confounding comparison of mutation isolation across different studies are the differing workflows that researchers employ, which include both the tools used to perform different steps of a variant isolation pipeline, but also the filtration steps taken to isolate DNMs from the vastly higher proportion of false positives (FPs). In order to analyze the impact of varying pipelines on DNM isolation, I first analyze six short-read simulators to assist in producing a realistic simulation framework that has “ground-truth” knowledge of sequencing errors and mapping efficacy. Then, using the best short-read simulator, I simulate parent-offspring trios at varying sequencing depths, and quantify common filtering thresholds efficacies at isolating DNMs. Finally, utilizing this knowledge, I then estimate the genome-wide mutation rate of the African oil palm (Elaeis guineensis) using a large single-generation pedigree, characterizing the mutational spectra and the presence of post-zygotic mutations, setting up the basis for further population genetic analysis.
