IcyTree: rapid browser-based visualization for phylogenetic trees and networks

Vaughan, Timothy G.

doi:10.1093/bioinformatics/btx155

Cited by 110 publications

(90 citation statements)

References 11 publications

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“…A tutorial on how to set-up an analysis using the coalescent with reassortment is available at https://taming-the-beast.org/tutorials/Reassortment-Tutorial/ (Barido-Sottani et al, 2017). Networks are logged in the extended Newick format (Cardona, Rosselló, and Valiente, 2008) and can be visualized using for example icytree.org (Vaughan, 2017). Additionally, we provide python scripts to plot networks based on https://github.com/evogytis/baltic.…”

Section: Methodsmentioning

confidence: 99%

“…This is similar to the well-known and challenging problem of inferring ancestral recombination graphs (ARGs), with the difference that segments in our model have fixed boundaries, but no defined ordering. While many approaches to inferring ARGs exist, some are restricted to tree-based networks (Didelot et al, 2010; Vaughan, Welch, et al, 2017), meaning that the networks consist of a base tree where recombination edges always attach to edges on the base tree. Other approaches (e.g (M.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

Müller

Stolz

Dudas³

et al. 2019

Preprint

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Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. We here introduce a novel coalescent based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. Together, these provide the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

Müller

Stolz

Dudas³

et al. 2019

Preprint

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“…R> pdf(file = "SampleHagellochTree.pdf", height = 20, width = 7) R> plot(out, index = 100) R> dev.off() A second option is to use the epi2newick() or write.epinet() function to write the transmission trees to the text-based Newick format (Felsenstein 2004), which is the standard tree format used in phylogenetics, that can then be plotted using any of a number of packages such as ape (Paradis, Claude, and Strimmer 2004) Vaughan 2017). The epi2newickmcmc() function returns a single inferred transmission tree as a Newick formatted string.…”

Section: Age Diffmentioning

confidence: 99%

epinet: An R Package to Analyze Epidemics Spread across Contact Networks

Groendyke¹,

Welch²

2018

J. Stat. Soft.

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We present the R package epinet, which provides tools for analyzing the spread of epidemics through populations. We assume that the relationships among individuals in a population are modeled by a contact network described by an exponential-family random graph model and that the disease being studied spreads across the edges of this network from infectious to susceptible individuals. We use a susceptible-exposed-infectiousremoved compartmental model to describe the progress of the disease within each host. We describe the functionality of the package, which consists of routines that perform simulation, plotting, and inference. The main inference routine utilizes a Bayesian approach and a Markov chain Monte Carlo algorithm. We demonstrate the use of the package through two examples, one involving simulated data and one using data from an actual measles outbreak.

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“…While these packages are powerful, they require substantial computing expertise, which can be an impediment for some scientists. Current web based tree viewers are convenient in that they do not require the installation of additional software and provide customization and management features (e.g., Evolview (He et al, 2016), IcyTree (Vaughan, 2017), iTOL (Letunic and Bork, 2016), PhyD3 (Kreft et al, 2017), Phylemon (Sánchez et al, 2011), PhyloBot (Hanson-Smith and Johnson, 2016), Phylo.io (Robinson et al, 2016)), but often have complex user interfaces or complicated file formats to enable complex annotations. Iroki strikes a balance between flexibility and usability by combining visualization of trees in a clean, user-friendly web interface with powerful automatic customization based on simple, tab-separated text files.…”

Section: Introductionmentioning

confidence: 99%

Iroki: automatic customization and visualization of phylogenetic trees

Harrison

McAllister

et al. 2017

Preprint

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Phylogenetic trees are an important analytical tool for evaluating community diversity and evolutionary history. In the case of microorganisms, the decreasing cost of sequencing has enabled researchers to generate ever-larger sequence datasets, which in turn have begun to fill gaps in the evolutionary history of microbial groups. However, phylogenetic analyses of these types of datasets create complex trees that can be challenging to interpret. Scientific inferences made by visual inspection of phylogenetic trees can be simplified and enhanced by customizing various parts of the tree. Yet, manual customization is time-consuming and error prone, and programs designed to assist in batch tree customization often require programming experience or complicated file formats for annotation. Iroki, a user-friendly web interface for tree visualization, addresses these issues by providing automatic customization of large trees based on metadata contained in tab-separated text files. Iroki’s utility for exploring biological and ecological trends in sequencing data was demonstrated through a variety of microbial ecology applications in which trees with hundreds to thousands of leaf nodes were customized according to extensive collections of metadata. The Iroki web application and documentation are available at https://www.iroki.net or through the VIROME portal (http://virome.dbi.udel.edu). Iroki’s source code is released under the MIT license and is available at https://github.com/mooreryan/iroki.

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IcyTree: rapid browser-based visualization for phylogenetic trees and networks

Cited by 110 publications

References 11 publications

Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

epinet: An R Package to Analyze Epidemics Spread across Contact Networks

Iroki: automatic customization and visualization of phylogenetic trees

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