Dataset Information

Protocol combining tree-based Maximum Parsimony and web-like Phylogenetic Networks analyses to investigate reticulate human evolution.

ABSTRACT: Our protocol combines Maximum Parsimony and Phylogenetic Networks approaches to understand the phylogenetic relationships and evolutionary processes of hominin species that might have shared inheritance from multiple ancestors. By addressing the questions of pattern and process in human phylogeny, the protocol can be used to clarify the taxonomic definition(s) of diverse hominin groups and ascertain whether or not the mode of evolution of genus Homo is reticulate. Using high quality and informative phenotypic data sets is necessary to yield meaningful results. For complete details on the use and execution of this protocol, please refer to Caparros and Prat (2021).

SUBMITTER: Caparros M

PROVIDER: S-EPMC8931433 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Protocol combining tree-based Maximum Parsimony and web-like Phylogenetic Networks analyses to investigate reticulate human evolution.

Caparros Miguel M Prat Sandrine S

STAR protocols 20220316 1

Our protocol combines Maximum Parsimony and Phylogenetic Networks approaches to understand the phylogenetic relationships and evolutionary processes of hominin species that might have shared inheritance from multiple ancestors. By addressing the questions of pattern and process in human phylogeny, the protocol can be used to clarify the taxonomic definition(s) of diverse hominin groups and ascertain whether or not the mode of evolution of genus <i>Homo</i> is reticulate. Using high quality and i ...[more]

PMID: 35308130

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Project description:Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as "convergence") and in estimating the correct proportions of the different types of them (known as "mixing"). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. [Bayesian phylogenetic inference; MCMC; parsimony; tree proposal.].

Dataset Information

Protocol combining tree-based Maximum Parsimony and web-like Phylogenetic Networks analyses to investigate reticulate human evolution.

Publications

Protocol combining tree-based Maximum Parsimony and web-like Phylogenetic Networks analyses to investigate reticulate human evolution.

Similar Datasets

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