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.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.

Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop "guided" proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.

A Surrogate Function for One-Dimensional Phylogenetic Likelihoods.

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.

Bacterial Composition of the Human Upper Gastrointestinal Tract Microbiome Is Dynamic and Associated with Genomic Instability in a Barrett's Esophagus Cohort.

BACKGROUND: The incidence of esophageal adenocarcinoma (EAC) has increased nearly five-fold over the last four decades in the United States. Barrett's esophagus, the replacement of the normal squamous epithelial lining with a mucus-secreting columnar epithelium, is the only known precursor to EAC. Like other parts of the gastrointestinal (GI) tract, the esophagus hosts a variety of bacteria and comparisons among published studies suggest bacterial communities in the stomach and esophagus differ. Chronic infection with Helicobacter pylori in the stomach has been inversely associated with development of EAC, but the mechanisms underlying this association remain unclear. METHODOLOGY: The bacterial composition in the upper GI tract was characterized in a subset of participants (n=12) of the Seattle Barrett's Esophagus Research cohort using broad-range 16S PCR and pyrosequencing of biopsy and brush samples collected from squamous esophagus, Barrett's esophagus, stomach corpus and stomach antrum. Three of the individuals were sampled at two separate time points. Prevalence of H. pylori infection and subsequent development of aneuploidy (n=339) and EAC (n=433) was examined in a larger subset of this cohort. RESULTS/SIGNIFICANCE: Within individuals, bacterial communities of the stomach and esophagus showed overlapping community membership. Despite closer proximity, the stomach antrum and corpus communities were less similar than the antrum and esophageal samples. Re-sampling of study participants revealed similar upper GI community membership in two of three cases. In this Barrett's esophagus cohort, Streptococcus and Prevotella species dominate the upper GI and the ratio of these two species is associated with waist-to-hip ratio and hiatal hernia length, two known EAC risk factors in Barrett's esophagus. H. pylori-positive individuals had a significantly decreased incidence of aneuploidy and a non-significant trend toward lower incidence of EAC.