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The increased availability of genomic and metagenomic data poses challenges at multiple analysis levels, including visualization of very large-scale microbial and microbial community data paired with rich metadata. We developed GraPhlAn (Graphical Phylogenetic Analysis), a computational tool that produces high-quality, compact visualizations of microbial genomes and metagenomes. This includes phylogenies spanning up to thousands of taxa, annotated with metadata ranging from microbial community abundances to microbial physiology or host and environmental phenotypes. GraPhlAn has been developed as an open-source command-driven tool in order to be easily integrated into complex, publication-quality bioinformatics pipelines. It can be executed either locally or through an online Galaxy web application. We present several examples including taxonomic and phylogenetic visualization of microbial communities, metabolic functions, and biomarker discovery that illustrate GraPhlAn's potential for modern microbial and community genomics.

Background With the accumulation of phylogenomic data and the growing demand for bioinformatics analyses, it has become increasingly important and complex to construct evolutionary relationships for different research purposes. Therefore, the ability to support multiple scenarios has become an essential need for phylogenetic visualization. Results In this study, we present PhyloScape, a web-based application for interactive visualization of phylogenetic trees that can be used stand-alone or as a toolkit deployed on the users’ website. The platform supports customizable multiple visualization features and is equipped with a flexible metadata annotation system, providing researchers with publishable, interactive views of trees. PhyloScape extensions include views of amino acid identity, geometry, and protein structure, which are applicable to various areas such as microbial taxonomy, pathogen phylogeny, and plant conservation. Trees published on the website can be efficiently shared and integrated into the users’ own system via a unique address. Conclusions As a scalable platform, PhyloScape provides a variety of online plug-ins that users can easily combine for specific scenarios. PhyloScape is freely available at http://darwintree.cn/PhyloScape .

The two great modern naturalists, Linnaeus and Darwin, expressed their intuition about how best to visualize patterns of affinities, that is, morphological similarities and divergences between taxa. Linnaeus suggested that “all plants show affinities on all sides, like a territory on a geographical map,” while Darwin thought that it was virtually impossible to understand the affinities between living and extinct species without a genealogical tree. Genealogical trees follow the diachronic, evolving logic of a timeline, whereas maps depict a synchronous pattern of extant taxa. Although the two seem unrelated, various naturalists made attempts to combine them. Surprisingly, these resulted in three-dimensional images that, in order to be observed, had to be projected on paper. The naturalists Max Fürbringer and Richard Bowdler Sharpe were aware of this fact, but even Darwin himself twice combined the basic intuitions underlying the two modes of representation to produce three-dimensional images. This article is a brief history of the efforts to merge genealogical trees and map-like cross sections of affinities into one three-dimensional image.

This paper is about visualizing tree structured data. In particular, the emphasis is on visualizing the similarities and differences between pairs of trees. The impetus for the work comes from the field of bioinformatics, where biologists construct complex phylogenetic trees to represent the evolution of species or genes. The two main issues that arise when comparing these data is to know how to efficiently and effectively compare phylogenetic trees, and how to visually present the results of the comparison. The primary approach is to present a new framework for tree structure visualization techniques that will display pairs of trees “face to face” with leaf nodes aligned. The results show that a combination of automatic and manual rearrangement is often effective in rapidly generating an arrangement that facilitates tree comparison, even for quite large trees.

PhyloNetworks is a Julia package for the inference, manipulation, visualization, and use of phylogenetic networks in an interactive environment. Inference of phylogenetic networks is done with maximum pseudolikelihood from gene trees or multi-locus sequences (SNaQ), with possible bootstrap analysis. PhyloNetworks is the first software providing tools to summarize a set of networks (from a bootstrap or posterior sample) with measures of tree edge support, hybrid edge support, and hybrid node support. Networks can be used for phylogenetic comparative analysis of continuous traits, to estimate ancestral states or do a phylogenetic regression. The software is available in open source and with documentation at https://github.com/crsl4/PhyloNetworks.jl

While phylogenetic trees and associated data have been getting easier to generate, it has been difficult to reuse, combine, and synthesize the information they provided, because published trees are often only available as image files and associated data are often stored in incompatible formats. To increase the reproducibility and reusability of phylogenetic data, the ggtree object was designed for storing phylogenetic tree and associated data, as well as visualization directives. The ggtree object itself is a graphic object and can be rendered as a static image. More importantly, the input tree and associated data that are used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree, and data in one single object) and thus enhancing data reuse and analytical reproducibility, as well as facilitating integrative and comparative studies. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree. The ggtree object is designed to store phylogenetic tree and associated data, and the object itself is a graphic object that can be rendered as an image file. This work will increase the reproducibility and reusability of phylogenetic data, as well as facilitate integrative and comparative studies. Highlights The phylogenetic tree and diverse accompanying data can be stored in a ggtree graph object, which improves the reproducibility and reusability of phylogenetic data. The phylogenetic tree and associated data can be extracted from the ggtree object, which can be reanalyzed and help various scientific disciplines synthesize their comparative studies and phylogenetic information. The ggtree graph object can be rendered as a static image, and the visualization directives that were previously saved in the object can be reused to display a different tree object in a manner akin to Microsoft Word Format Painter.

Ggtree is a comprehensive R package for visualizing and annotating phylogenetic trees with associated data. It can also map and visualize associated external data on phylogenies with two general methods. Method 1 allows external data to be mapped on the tree structure and used as visual characteristic in tree and data visualization. Method 2 plots the data with the tree side by side using different geometric functions after reordering the data based on the tree structure. These two methods integrate data with phylogeny for further exploration and comparison in the evolutionary biology context. Ggtree is available from http://www.bioconductor.org/packages/ggtree.

As the SARS-CoV-2 virus spreads through human populations, the unprecedented accumulation of viral genome sequences is ushering in a new era of ‘genomic contact tracing’—that is, using viral genomes to trace local transmission dynamics. However, because the viral phylogeny is already so large—and will undoubtedly grow many fold—placing new sequences onto the tree has emerged as a barrier to real-time genomic contact tracing. Here, we resolve this challenge by building an efficient tree-based data structure encoding the inferred evolutionary history of the virus. We demonstrate that our approach greatly improves the speed of phylogenetic placement of new samples and data visualization, making it possible to complete the placements under the constraints of real-time contact tracing. Thus, our method addresses an important need for maintaining a fully updated reference phylogeny. We make these tools available to the research community through the University of California Santa Cruz SARS-CoV-2 Genome Browser to enable rapid cross-referencing of information in new virus sequences with an ever-expanding array of molecular and structural biology data. The methods described here will empower research and genomic contact tracing for SARS-CoV-2 specifically for laboratories worldwide. Ultrafast Sample placement on Existing tRees (UShER) is an efficient method that facilitates the addition of new SARS-CoV-2 genome sequences onto the existing phylogeny, aiding in real-time analysis of viral evolution during the COVID-19 pandemic.

Background Cophylogeny reconciliation is a powerful method for analyzing host-symbiont coevolution. The cophylogeny problem consists of mapping the phylogenetic tree of the symbionts into the one of the hosts, including events such as duplications, co-speciation, host-switches, and extinctions by comparing the discrepancies between the topologies of the associated symbiont evolutionary trees. Visualizing tree reconciliations is important for biologists as it aids in understanding and identifying specific patterns in the coevolution of hosts and symbionts. Additionally, when multiple optimal solutions exist, it allows for the quick comparison of different reconciliations between the same pair of trees. Results Here, we present VIRI (visual inspector of reconciliation instances), a new tree reconciliation visualizer. We adopt a hybrid metaphor combining space-filling (for host trees) and node-link (for symbiont trees) approaches, implementing the algorithms described in Calamoneri et al. (Theor Comput Sci 815:228–245. https://doi.org/10.1016/j.tcs.2019.12.024 , 2020). The visualizations produced by VIRI are designed to be clear and interpretable, thanks to an unambiguous, top-down layout of tree reconciliations and the preservation of the user’s mental map when comparing multiple reconciliations on the same pair of trees. In particular, the consistent use of a shared host tree layout across visualizations is a novel feature that facilitates direct comparison. Moreover, VIRI proposes a crossing-free visualization whenever possible. Finally, VIRI allows users to store datasets and download their visualizations, offering a convenient way to organize and share data. An example of visualization produced by VIRI is depicted in Fig. 1. Conclusions VIRI efficiently produces clear and easy-to-read visualizations of tree reconciliations. VIRI is free and available at https://viri.di.uniroma1.it/ .

Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.