Of traits and trees: probabilistic distances under continuous trait models for dissecting the interplay among phylogeny, model, and data

Stochastic models of character trait evolution have become a cornerstone of evolutionary biology in an array of contexts. While probabilistic models have been used extensively for statistical inference, they have largely been ignored for the purpose of measuring distances between phylogeny-aware models. Recent contributions to the problem of phylogenetic distance computation have highlighted the importance of explicitly considering evolutionary model parameters and their impacts on molecular sequence data when quantifying dissimilarity between trees. By comparing two phylogenies in terms of their induced probability distributions that are functions of many model parameters, these distances can be more informative than traditional approaches that rely strictly on differences in topology or branch lengths alone. Currently, however, these approaches are designed for comparing models of nucleotide substitution and gene tree distributions, and thus, are unable to address other classes of traits and associated models that may be of interest to evolutionary biologists. Here we expand the principles of probabilistic phylogenetic distances to compute tree distances under models of continuous trait evolution along a phylogeny. By explicitly considering both the degree of relatedness among species and the evolutionary processes that collectively give rise to character traits, these distances provide a foundation for comparing models and their predictions, and for quantifying the impacts of assuming one phylogenetic background over another while studying the evolution of a particular trait. We demonstrate the properties of these approaches using theory, simulations, and several empirical datasets that highlight potential uses of probabilistic distances in many scenarios. We also introduce an open-source R package named PRDATR for easy application by the scientific community for computing phylogenetic distances under models of character trait evolution.

What Role Should Government Play in the Personal Carbon Trading Market: Motivator or Punisher?

With increasing downstream carbon emissions, the implementation of a personal carbon trading scheme is urgently required. In order to facilitate the progress, government departments are supposed to adopt a motivating or punitive policy to make guidance for downstream carbon emissions reduction. This study determined and verified the evolutionarily stable strategies (ESSs) of government departments and individuals whose carbon emissions exceeded the initial carbon allowance (CEEICA individuals) by using the evolutionary game and numerical simulation methods, respectively. The findings show that the ESS of government departments is always a punitive policy during the variation of strategies of CEEICA individuals. The ESS of CEEICA individuals is an active plan when the added cost (the difference between emissions reduction cost and trading earning) is less than the carbon tax; otherwise, it is a passive plan. Furthermore, the rate of convergence can be significantly influenced by the probabilistic distances between initial strategies and the ESSs. On the basis of these findings, this study suggested implementing a “punishment first, motivation-supplemented” policy, and developing a stable operational mechanism for a personal carbon trading market.

Recovering genomic clusters of secondary metabolites from lakes: a Metagenomics 2.0 approach

BackgroundMetagenomic approaches became increasingly popular in the past decades due to decreasing costs of DNA sequencing and bioinformatics development. So far, however, the recovery of long genes coding for secondary metabolism still represents a big challenge. Often, the quality of metagenome assemblies is poor, especially in environments with a high microbial diversity where sequence coverage is low and complexity of natural communities high. Recently, new and improved algorithms for binning environmental reads and contigs have been developed to overcome such limitations. Some of these algorithms use a similarity detection approach to classify the obtained reads into taxonomical units and to assemble draft genomes. This approach, however, is quite limited since it can classify exclusively sequences similar to those available (and well classified) in the databases.In this work, we used draft genomes from Lake Stechlin, north-eastern Germany, recovered by MetaBat, an efficient binning tool that integrates empirical probabilistic distances of genome abundance, and tetranucleotide frequency for accurate metagenome binning. These genomes were screened for secondary metabolism genes, such as polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS), using the Anti-SMASH and NAPDOS workflows.ResultsWith this approach we were able to identify 243 secondary metabolite clusters from 121 genomes recovered from the lake samples. A total of 18 NRPS, 19 PKS and 3 hybrid PKS/NRPS clusters were found. In addition, it was possible to predict the partial structure of several secondary metabolite clusters allowing for taxonomical classifications and phylogenetic inferences.ConclusionsOur approach revealed a great potential to recover and study secondary metabolites genes from any aquatic ecosystem.

Morphological probabilistic hierarchies for texture segmentation

A general methodology is introduced for texture segmentation in binary, scalar, or multispectral images. Textural information is obtained from morphological operations on images. Starting from a fine partition of the image in regions, hierarchical segmentations are designed in a probabilistic framework by means of probabilistic distances conveying the textural or morphological information, and of random markers accounting for the morphological content of the regions and of their spatial arrangement. The probabilistic hierarchies are built from binary or multiple fusion of regions.

A robust statistical framework for reconstructing genomes from metagenomic data

We present software that reconstructs genomes from shotgun metagenomic sequences using a reference-independent approach. This method permits the identification of OTUs in large complex communities where many species are unknown. Binning reduces the complexity of a metagenomic dataset enabling many downstream analyses previously unavailable. In this study we developed MetaBAT, a robust statistical framework that integrates probabilistic distances of genome abundance with sequence composition for automatic binning. Applying MetaBAT to a human gut microbiome dataset identified 173 highly specific genomes bins including many representing previously unidentified species.