Newick Format Research Articles

NewickTreeModifier: A simple web tool to prune and modify Newick trees.

Large-scale selection analyses of protein-coding sequences and phylogenetic tree reconstructions require suitable trees in Newick format. We developed the NewickTreeModifier (NTM), a simple web-based tool to trim and modify Newick trees for such analyses. The users can choose provided master trees or upload a tree to prune it to selected species available in FASTA, NEXUS, or PHYLIP sequence format with an internal converter, a simple species list, or directly determined from a checklist interface of the master trees. Plant, insect, and vertebrate master trees comprise the maximum number of species in an up-to-date phylogenetic order directly transferable to the pruned Newick outfile. NTM is available at https://retrogenomics.uni-muenster.de/tools/ntm.

PhyloCoalSimulations: A Simulator for Network Multispecies Coalescent Models, Including a New Extension for the Inheritance of Gene Flow.

We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example. We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at https://github.com/cecileane/PhyloCoalSimulations.jl.

GPTree Cluster: phylogenetic tree cluster generator in the context of supertree inference.

For many years, evolutionary and molecular biologists have been working with phylogenetic supertrees, which are oriented acyclic graph structures. In the standard approaches, supertrees are obtained by concatenating a set of phylogenetic trees defined on different but overlapping sets of taxa (i.e. species). More recent approaches propose alternative solutions for supertree inference. The testing of new metrics for comparing supertrees and adapting clustering algorithms to overlapping phylogenetic trees with different numbers of leaves requires large amounts of data. In this context, designing a new approach and developing a computer program to generate phylogenetic tree clusters with different numbers of overlapping leaves are key elements to advance research on phylogenetic supertrees and evolution. The main objective of the project is to propose a new approach to simulate clusters of phylogenetic trees defined on different, but mutually overlapping, sets of taxa, with biological events. The proposed generator can be used to generate a certain number of clusters of phylogenetic trees in Newick format with a variable number of leaves and with a defined level of overlap between trees in clusters. A Python script version 3.7, called GPTree Cluster, which implements the discussed approach, is freely available at: https://github.com/tahiri-lab/GPTree/tree/GPTreeCluster.

NetRAX: accurate and fast maximum likelihood phylogenetic network inference.

MotivationPhylogenetic networks can represent non-treelike evolutionary scenarios. Current, actively developed approaches for phylogenetic network inference jointly account for non-treelike evolution and incomplete lineage sorting (ILS). Unfortunately, this induces a very high computational complexity and current tools can only analyze small datasets.ResultsWe present NetRAX, a tool for maximum likelihood (ML) inference of phylogenetic networks in the absence of ILS. Our tool leverages state-of-the-art methods for efficiently computing the phylogenetic likelihood function on trees, and extends them to phylogenetic networks via the notion of ‘displayed trees’. NetRAX can infer ML phylogenetic networks from partitioned multiple sequence alignments and returns the inferred networks in Extended Newick format. On simulated data, our results show a very low relative difference in Bayesian Information Criterion (BIC) score and a near-zero unrooted softwired cluster distance to the true, simulated networks. With NetRAX, a network inference on a partitioned alignment with 8000 sites, 30 taxa and 3 reticulations completes within a few minutes on a standard laptop.Availability and implementationOur implementation is available under the GNU General Public License v3.0 at https://github.com/lutteropp/NetRAX.Supplementary information Supplementary data are available at Bioinformatics online.

Qiime Artifact eXtractor (qax): A Fast and Versatile Tool to Interact with Qiime2 Archives.

Qiime2 is one of the most popular software tools used for analysis of output from metabarcoding experiments (e.g., sequencing of 16S, 18S, or ITS amplicons). Qiime2 introduced a novel and innovative data exchange format: the ‘Qiime2 artifact’. Qiime2 artifacts are structured compressed archives containing a dataset and its associated metadata. Examples of datasets are FASTQ reads, representative sequences in FASTA format, a phylogenetic tree in Newick format, while examples of metadata are the command that generated the artifact, information on the execution environment, citations on the used software, and all the metadata of the artifacts used to produce it. While artifacts can improve the shareability and reproducibility of Qiime2 workflows, they are less easily integrated with general bioinformatics pipelines. Accessing metadata in the artifacts also requires full Qiime2 installation. Qiime Artifact eXtractor (qax) allows users to easily interface with Qiime2 artifacts from the command line, without needing the full Qiime2 environment installed (or activated).

Tree decomposition of Reeb graphs, parametrized complexity, and applications to phylogenetics

Inspired by the interval decomposition of persistence modules and the extended Newick format of phylogenetic networks, we show that, inside the larger category of partially ordered Reeb graphs, every Reeb graph with n leaves and first Betti number s, can be identified with a coproduct of at most $$2^s$$ partially ordered trees with $$(n + s)$$ leaves. Reeb graphs are therefore classified up to isomorphism by their tree-decomposition. An implication of this result, is that the isomorphism problem for Reeb graphs is fixed parameter tractable when the parameter is the first Betti number. We propose partially ordered Reeb graphs as a model for time consistent phylogenetic networks and propose a certain Hausdorff distance as a metric on these structures.

Crop Origins and Phylo Food: A database and a phylogenetic tree to stimulate comparative analyses on the origins of food crops

AbstractMotivationKnowledge on the origins of food crops in the wild is abundant but fragmentary and unconsolidated, which hinders macroecological approaches to long‐standing questions on the phylogeography and history of crop origins.Main types of variable contained(a) Comprehensive checklist of crop species cultivated for food; (b) data on diverse continuous and categorical descriptors of antiquity of cultivation (earliest written records of cultivation and of domestication), organ harvested for primary use, and growth form; (c) identities, geographic distribution and current climate at the origin of crops’ wild progenitors; (d) a time‐calibrated molecular phylogeny (Phylo Food v 1.0) of food crops.Spatial location and grainGlobal. Spatial accuracy of geographic records for wild progenitors varies among Global Biodiversity Information Facility (GBIF) source databases. WorldClim data were retrieved at 2.5 min (approximately 4.5 km at the equator) spatial resolution.Time period and grainTemporal accuracy of estimation of cultivation antiquity varies among sources, but unit of measurements is years ago. The scale of the phylogenetic tree is millions of years ago.Major taxa and level of measurementFlowering plants. Specifically, 866 species of food crops and 901 species of wild progenitors. Most information was retrieved at the species level. However, geographic occurrence of wild progenitors was obtained from GBIF records of observations of individuals of a given species in the field.Software formatCrop Origins v 1.0 and Phylo Food v 1.0 are provided as zipped files in the Supporting Information of this paper. The zipped files contain a. csv file with v 1.0 of the Crop Origins database, a. tre file with Newick format as Phylo Food v 1.0, metadata and literature sources files for both resources, and R code used to build Phylo Food v 1.0. The resources are also deposited, and will be regularly updated, at https://rubenmilla.github.io/Crop_Origins_Phylo.

VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs.

In the past decades a number of software programs have been developed to infer phylogenetic relationships between populations. However, most of these programs typically use alignments of sequences from genes to build phylogeny. Recently, many standalone or web applications have been developed to handle large-scale whole genome data, but they are either computationally intensive, dependent on third party software or required significant time and resource of a web server. In the post-genomic era, researchers are able to obtain bioinformatically processed high-quality publication-ready whole genome data for many individuals in a population from next generation sequencing companies due to the reduction in the cost of sequencing and analysis. Such genotype data is typically presented in the Variant Call Format (VCF) and there is no simple software available that directly uses this data format to construct the phylogeny of populations in a short time. To address this limitation, we have developed a user-friendly software, VCF2PopTree that uses genome-wide SNPs to construct and display phylogenetic trees in seconds to minutes. For example, it reads a VCF file containing 4 million SNPs and draws a tree in less than 30 seconds. VCF2PopTree accepts genotype data from a local machine, constructs a tree using UPGMA and Neighbour-Joining algorithms and displays it on a web-browser. It also produces pairwise-diversity matrix in MEGA and PHYLIP file formats as well as trees in the Newick format which could be directly used by other popular phylogenetic software programs. The software including the source code, a test VCF file and a documentation are available at: https://github.com/sansubs/vcf2pop.

OutbreakFinder: a visualization tool for rapid detection of bacterial strain clusters based on optimized multidimensional scaling.

With the evolution of next generation sequencing (NGS) technologies, whole-genome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering. OutbreakFinder can be downloaded for free at https://github.com/skypes/Newton-method-MDS.

PGT: Visualizing temporal and spatial biogeographic patterns

AbstractAimA geophylogeny, generated by mapping a phylogeny onto geographic regions, graphically summarizes large‐scale genetic variation over space and time, and is consequently crucial for conceptual understanding and visualization of global biogeographic patterns. The rapidly expanding DNA barcoding data with geographic coordinates associated with each specimen have dramatically increased the number of global phylogeographic studies that would benefit from software generating geophylogenies. A number of software programs have been developed, some with advanced features, but they either require additional software or are lacking in quality, especially in geographic resolution.InnovationPGT (Phylogeographic Tree), freely available at http://dambe.bio.uottawa.ca/PGT/PGT.aspx, combines the highest map quality and user‐friendliness. It accesses Microsoft Bing Maps and Google Maps seamlessly and generates geophylogenies on high‐resolution regular or terrain maps. Only a few mouse clicks are needed from PGT installation to the generation of high‐resolution geophylogenies, making PGT perfect for both teaching and research in global ecology and biogeography. The input tree can be in NEXUS or Newick format, and the geographic data with latitude and longitude values can be in tab‐delimited or comma‐delimited format such as those exported from spreadsheet programs. A Quick‐Start guide is included in the built‐in help system.Main conclusionsPGT is simpler, more elegant, and of much higher quality than alternatives for plotting phylogenetic trees over geographic regions for visualizing distribution of biodiversity over space and time.

Inferring Phylogenetic Networks Using PhyloNet

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

TreePics: visualizing trees with pictures

While many programs are available to edit phylogenetic trees, associating pictures with branch tips in an efficient and automatic way is not an available option. Here, we present TreePics, a standalone software that uses a web browser to visualize phylogenetic trees in Newick format and that associates pictures (typically, pictures of the voucher specimens) to the tip of each branch. Pictures are visualized as thumbnails and can be enlarged by a mouse rollover. Further, several pictures can be selected and displayed in a separate window for visual comparison. TreePics works either online or in a full standalone version, where it can display trees with several thousands of pictures (depending on the memory available). We argue that TreePics can be particularly useful in a preliminary stage of research, such as to quickly detect conflicts between a DNA-based phylogenetic tree and morphological variation, that may be due to contamination that needs to be removed prior to final analyses, or the presence of species complexes.

A Critical Review on the Use of Support Values in Tree Viewers and Bioinformatics Toolkits.

Phylogenetic trees are routinely visualized to present and interpret the evolutionary relationships of species. Most empirical evolutionary data studies contain a visualization of the inferred tree with branch support values. Ambiguous semantics in tree file formats can lead to erroneous tree visualizations and therefore to incorrect interpretations of phylogenetic analyses. Here, we discuss problems that arise when displaying branch values on trees after rerooting. Branch values are typically stored as node labels in the widely-used Newick tree format. However, such values are attributes of branches. Storing them as node labels can therefore yield errors when rerooting trees. This depends on the mostly implicit semantics that tools deploy to interpret node labels. We reviewed ten tree viewers and ten bioinformatics toolkits that can display and reroot trees. We found that 14 out of 20 of these tools do not permit users to select the semantics of node labels. Thus, unaware users might obtain incorrect results when rooting trees. We illustrate such incorrect mappings for several test cases and real examples taken from the literature. This review has already led to improvements in eight tools. We suggest tools should provide options that explicitly force users to define the semantics of node labels.

MixtureTree annotator: a program for automatic colorization and visual annotation of MixtureTree.

The MixtureTree Annotator, written in JAVA, allows the user to automatically color any phylogenetic tree in Newick format generated from any phylogeny reconstruction program and output the Nexus file. By providing the ability to automatically color the tree by sequence name, the MixtureTree Annotator provides a unique advantage over any other programs which perform a similar function. In addition, the MixtureTree Annotator is the only package that can efficiently annotate the output produced by MixtureTree with mutation information and coalescent time information. In order to visualize the resulting output file, a modified version of FigTree is used. Certain popular methods, which lack good built-in visualization tools, for example, MEGA, Mesquite, PHY-FI, TreeView, treeGraph and Geneious, may give results with human errors due to either manually adding colors to each node or with other limitations, for example only using color based on a number, such as branch length, or by taxonomy. In addition to allowing the user to automatically color any given Newick tree by sequence name, the MixtureTree Annotator is the only method that allows the user to automatically annotate the resulting tree created by the MixtureTree program. The MixtureTree Annotator is fast and easy-to-use, while still allowing the user full control over the coloring and annotating process.

Combining spatial and chemical information for clustering pharmacophores.

BackgroundA pharmacophore model consists of a group of chemical features arranged in three-dimensional space that can be used to represent the biological activities of the described molecules. Clustering of molecular interactions of ligands on the basis of their pharmacophore similarity provides an approach for investigating how diverse ligands can bind to a specific receptor site or different receptor sites with similar or dissimilar binding affinities. However, efficient clustering of pharmacophore models in three-dimensional space is currently a challenge.ResultsWe have developed a pharmacophore-assisted Iterative Closest Point (ICP) method that is able to group pharmacophores in a manner relevant to their biochemical properties, such as binding specificity etc. The implementation of the method takes pharmacophore files as input and produces distance matrices. The method integrates both alignment-dependent and alignment-independent concepts.ConclusionsWe apply our three-dimensional pharmacophore clustering method to two sets of experimental data, including 31 globulin-binding steroids and 4 groups of selected antibody-antigen complexes. Results are translated from distance matrices to Newick format and visualised using dendrograms. For the steroid dataset, the resulting classification of ligands shows good correspondence with existing classifications. For the antigen-antibody datasets, the classification of antigens reflects both antigen type and binding antibody. Overall the method runs quickly and accurately for classifying the data based on their binding affinities or antigens.

Multiple consensus trees: a method to separate divergent genes

BackgroundIt is generally admitted that the species tree cannot be inferred from the genetic sequences of a single gene because the evolution of different genes, and thus the gene tree topologies, may vary substantially. Gene trees can differ, for example, because of horizontal transfer events or because some of them correspond to paralogous instead of orthologous sequences. A variety of methods has been proposed to tackle the problem of the reconciliation of gene trees in order to reconstruct a species tree. When the taxa in all the trees are identical, the problem can be stated as a consensus tree problem.ResultsIn this paper we define a new method for deciding whether a unique consensus tree or multiple consensus trees can best represent a set of given phylogenetic trees. If the given trees are all congruent, they should be compatible into a single consensus tree. Otherwise, several consensus trees corresponding to divergent genetic patterns can be identified. We introduce a method optimizing the generalized score, over a set of tree partitions in order to decide whether the given set of gene trees is homogeneous or not.ConclusionsThe proposed method has been validated with simulated data (random trees organized in three topological groups) as well as with real data (bootstrap trees, homogeneous set of trees, and a set of non homogeneous gene trees of 30 E. Coli strains; it is worth noting that some of the latter genes underwent horizontal gene transfers). A computer program, MCT - Multiple Consensus Trees, written in C was made freely available for the research community (it can be downloaded from http://bioinformatics.lif.univ-mrs.fr/consensus/index.html). It handles trees in a standard Newick format, builds three hierarchies corresponding to RF and QS similarities between trees and the greedy ascending algorithm. The generalized score values of all tree partitions are computed.

GenNon-h: Generating multiple sequence alignments on nonhomogeneous phylogenetic trees

BackgroundA number of software packages are available to generate DNA multiple sequence alignments (MSAs) evolved under continuous-time Markov processes on phylogenetic trees. On the other hand, methods of simulating the DNA MSA directly from the transition matrices do not exist. Moreover, existing software restricts to the time-reversible models and it is not optimized to generate nonhomogeneous data (i.e. placing distinct substitution rates at different lineages).ResultsWe present the first package designed to generate MSAs evolving under discrete-time Markov processes on phylogenetic trees, directly from probability substitution matrices. Based on the input model and a phylogenetic tree in the Newick format (with branch lengths measured as the expected number of substitutions per site), the algorithm produces DNA alignments of desired length. GenNon-h is publicly available for download.ConclusionThe software presented here is an efficient tool to generate DNA MSAs on a given phylogenetic tree. GenNon-h provides the user with the nonstationary or nonhomogeneous phylogenetic data that is well suited for testing complex biological hypotheses, exploring the limits of the reconstruction algorithms and their robustness to such models.

TreeSnatcher plus: capturing phylogenetic trees from images

BackgroundFigures of phylogenetic trees are widely used to illustrate the result of evolutionary analyses. However, one cannot easily extract a machine-readable representation from such images. Therefore, new software emerges that helps to preserve phylogenies digitally for future research.ResultsTreeSnatcher Plus is a GUI-driven JAVA application that semi-automatically generates a Newick format for multifurcating, arbitrarily shaped, phylogenetic trees contained in pixel images. It offers a range of image pre-processing methods and detects the topology of a depicted tree with adequate user assistance. The user supervises the recognition process, makes corrections to the image and to the topology and repeats steps if necessary. At the end TreeSnatcher Plus produces a Newick tree code optionally including branch lengths for rectangular and freeform trees.ConclusionsAlthough illustrations of phylogenies exist in a vast number of styles, TreeSnatcher Plus imposes no limitations on the images it can process with adequate user assistance. Given that a fully automated digitization of all figures of phylogenetic trees is desirable but currently unrealistic, TreeSnatcher Plus is the only program that reliably facilitates at least a semi-automatic conversion from such figures into a machine-readable format.

ETE: a python Environment for Tree Exploration

BackgroundMany bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.ResultsHere we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.ConclusionsETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

TULIP Software and Web Server: Automatic Classification of Protein Sequences Based on Pairwise Comparisons and Z-Value Statistics

A configuration space of homologous protein sequences (or CSHP) has been recently constructed based on pairwise comparisons, with probabilities deduced from Z-value statistics (Monte Carlo methods applied to pairwise comparisons) and following evolutionary assumptions. A Z-value cut-off is applied so as proteins are placed in the CSHP only when the similarity of pairs of sequences is significant following the Theorem of the Upper Limit of a score Probability (TULIP theorem). Based on the positions of similar protein sequences in the CSHP, a classification can be deduced, which can be visualized as trees, called TULIP trees. In previous case studies, TULIP trees where shown to be consistent with phylogenetic trees. To date, no tool has been made available to allow the computation of TULIP trees following this model. The availability of methods to cluster proteins based on pairwise comparisons and following evolutionary assumptions should be useful for evaluation and for the future improvements they might inspire. We developed a web server allowing the local or online computation of TULIP trees based on the CSHP probabilities. The input is a set of homologous protein sequences in multi-FASTA format. Pairwise comparisons are conducted using the Smith-Waterman method, with 100-1,000 sequence shuffling to estimate pairwise Z-values. Obtained Z-value matrix is used to infer a tree which is then written to a file. Output consists therefore of a Z-value matrix, a distance matrix, a TULIP treefile in NEWICK format, and a TULIP tree visualisation. The TULIP server provides an easy-to-use interface to the TULIP software, and allows a classification of protein sequences based on pairwise alignments and following evolutionary assumptions. TULIP trees are consistent with phylogenies in numerous cases, but they can be inconsistent for multi-domain proteins in which some domains have been conserved in all branches. Thus TULIP trees cannot be considered as conventional phylogenetic trees, following the MIAPA (Minimum Information About a Phylogenetic Analysis) recommendations. A major strength of the TULIP classification is its statistical validity when analysing samples including compositionally unbiased and biased sequences (i.e. with biased amino acid distributions), like sequences from Plasmodium falciparum. The TULIP web server is a service of the Malaria Portal of the University of Pretoria, South Africa, and is available at http://malport.bi.up.ac.za/TULIP/.