STEVEN HUNG-HSI WURodrigo, A.G.A.G.Rodrigo2021-08-182021-08-1820151471-2105https://scholars.lib.ntu.edu.tw/handle/123456789/578156Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny.enApproximate Bayesian computation | Evolutionary genetics | Markov chain Monte Carlo | Next generation sequencing | Short read sequencesjournal article10.1186/s12859-015-0810-y265368602-s2.0-84946400296http://dx.doi.org/10.1186/s12859-015-0810-y98069986