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The number of reference genomes of snakes lags behind several other vertebrate groups (e.g. birds and mammals). However, in the last two years, a concerted effort by researchers from around the world has produced new genomes of snakes representing members from several new families. Here, we present a high-quality, annotated genome of the central ratsnake (Pantherophis alleghaniensis), a member of the most diverse snake lineage, Colubroidea. Pantherophis alleghaniensis is found in the central part of the Nearctic, east of the Mississippi River. This genome was sequenced using 10X Chromium synthetic long reads and polished using Illumina short reads. The final genome assembly had an N50 of 21.82 Mb and an L50 of 22 scaffolds with a maximum scaffold length of 82.078 Mb. The genome is composed of 49.24% repeat elements dominated by long interspersed elements. We annotated this genome using transcriptome assemblies from 14 tissue types and recovered 28,368 predicted proteins. Finally, we estimated admixture proportions between two species of ratsnakes and discovered that this specimen is an admixed individual containing genomes from the western (Pantherophis obsoletus) and central ratsnakes (P. alleghaniensis). We discuss the importance of considering interspecific admixture in downstream approaches for inferring demography and phylogeny.

 

Abstract Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. Although several studies of phylogenetic MCMC convergence exist, these have focused on simulated data sets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many analyses appeared to perform well and failures in convergence were most likely to be detected using the average standard deviation of split frequencies, a diagnostic that compares topologies among independent chains. Different diagnostics yielded different information about failed convergence, demonstrating that multiple diagnostics must be employed to reliably detect problems. The number of taxa and average branch lengths in analyses have clear impacts on MCMC performance, with more taxa and shorter branches leading to more difficult convergence. We show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites is not broadly problematic for MCMC convergence but is also unnecessary. Changes to heating and the usage of model-averaged substitution models can both offer improved convergence in some cases, but neither are a panacea.