Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals
Journal
{BMC} Bioinformatics
Journal Volume
16
Journal Issue
1
Date Issued
2015
Author(s)
Rodrigo, A.G.
Abstract
Over 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.
Subjects
Approximate Bayesian computation | Evolutionary genetics | Markov chain Monte Carlo | Next generation sequencing | Short read sequences
Publisher
Springer Science and Business Media
Type
journal article
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Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals
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