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Abstract
Recent advances in paleogenomic technologies have enabled an increasingly detailed
understanding of the evolutionary relationships of now-extinct mammalian taxa. However,
a number of enigmatic Quaternary species have never been characterized with molecular
data, often because available fossils are rare or are found in environments that are
not optimal for DNA preservation. Here, we analyze paleogenomic data extracted from
bones attributed to the late Pleistocene western camel, Camelops cf. hesternus, a
species that was distributed across central and western North America until its extinction
approximately 13,000 years ago. Despite a modal sequence length of only around 35
base pairs, we reconstructed high-coverage complete mitochondrial genomes and low-coverage
partial nuclear genomes for each specimen. We find that Camelops is sister to African
and Asian bactrian and dromedary camels, to the exclusion of South American camelids
(llamas, guanacos, alpacas, and vicuñas). These results contradict previous morphology-based
phylogenetic models for Camelops, which suggest instead a closer relationship between
Camelops and the South American camelids. The molecular data imply a Late Miocene
divergence of the Camelops clade from lineages that separately gave rise to the extant
camels of Eurasia. Our results demonstrate the increasing capacity of modern paleogenomic
methods to resolve evolutionary relationships among distantly related lineages.
Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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