Online Bayesian phylogenetic inference: Theoretical foundations via sequential Monte Carlo

© 2017 The Author(s). Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding newsequences to already substantial databases.With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below.We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence

A Surrogate Function for One-Dimensional Phylogenetic Likelihoods

© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: [email protected]. Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-Alone open-source C library for calling from phylogenetics algorithms; it has proven essential for good performance of our online phylogenetic algorithm sts

PhyloSift: Phylogenetic analysis of genomes and metagenomes

Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection. In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata. These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454). © 2014 Darling et al

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies.

Bayesian Markov chain Monte Carlo explores tree space slowly, in part because it frequently returns to the same tree topology. An alternative strategy would be to explore tree space systematically, and never return to the same topology. In this article, we present an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, which we show to be a good approximation of the high posterior set of tree topologies on the data sets analyzed. Here, "likelihood" of a topology refers to the tree likelihood for the corresponding tree with optimized branch lengths. We call this method "phylogenetic topographer" (PT). The PT strategy is very simple: starting in a number of local topology maxima (obtained by hill-climbing from random starting points), explore out using local topology rearrangements, only continuing through topologies that are better than some likelihood threshold below the best observed topology. We show that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies. By using a nonblocking hash table keyed on unique representations of tree topologies, we avoid visiting topologies more than once across all concurrent threads exploring tree space. We demonstrate that PT can be used directly to approximate a Bayesian consensus tree topology. When combined with an accurate means of evaluating per-topology marginal likelihoods, PT gives an alternative procedure for obtaining Bayesian posterior distributions on phylogenetic tree topologies

19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology.

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators

Pneumococcal sepsis presenting as acute compartment syndrome of the lower limbs: a case report

<p>Abstract</p> <p>Introduction</p> <p>Acute compartment syndrome is a surgical emergency requiring immediate fasciotomy. Spontaneous onset of acute compartment syndrome of the lower limbs is rare. We present a very rare case of pneumococcal sepsis leading to spontaneous acute compartment syndrome.</p> <p>Case presentation</p> <p>A 40-year-old Caucasian man presented as an emergency with spontaneous onset of pain in both legs and signs of compartment syndrome. This was confirmed on fasciotomy. Blood culture grew <it>Streptococcus pneumoniae</it>.</p> <p>Conclusion</p> <p>Sepsis should be strongly suspected in bilateral acute compartment syndrome of spontaneous onset.</p

The results of arthroscopic anterior stabilisation of the shoulder using the bioknotless anchor system

<p>Abstract</p> <p>Background</p> <p>Shoulder instability is a common condition, particularly affecting a young, active population. Open capsulolabral repair is effective in the majority of cases, however arthroscopic techniques, particularly using suture anchors, are being used with increasing success.</p> <p>Methods</p> <p>15 patients with shoulder instability were operated on by a single surgeon (VK) using BioKnotless anchors (DePuy Mitek, Raynham, MA). The average length of follow-up was 21 months (17 to 31) with none lost to follow-up. Constant scores in both arms, patient satisfaction, activity levels and recurrence of instability was recorded.</p> <p>Results</p> <p>80% of patients were satisfied with their surgery. 1 patient suffered a further dislocation and another had recurrent symptomatic instability. The average constant score returned to 84% of that measured in the opposite (unaffected) shoulder. There were no specific post-operative complications encountered.</p> <p>Conclusion</p> <p>In terms of recurrence of symptoms, our results show success rates comparable to other methods of shoulder stabilisation. This technique is safe and surgeons familiar with shoulder arthroscopy will not encounter a steep learning curve. Shoulder function at approximately 2 years post repair was good or excellent in the majority of patients and it was observed that patient satisfaction was correlated more with return to usual activities than recurrence of symptoms.</p