INSTRAL: Discordance-Aware Phylogenetic Placement Using Quartet Scores

Comparison of phylogenetic trees defined on different but mutually overlapping sets of taxa: A review

Phylogenetic trees represent the evolutionary relationships and ancestry of various species or groups of organisms. Comparing these trees by measuring the distance between them is essential for applications such as tree clustering and the Tree of Life project. Many distance metrics for phylogenetic trees focus on trees defined on the same set of taxa. However, some problems require calculating distances between trees with different but overlapping sets of taxa. This study reviews state-of-the-art distance measures for such trees, covering six major approaches, including the constraint-based Robinson-Foulds (RF) distance RF(-), the completion-based RF(+), the generalized RF (GRF), the dissimilarity measure, the vectorial tree distance, and the geodesic distance in the extended Billera-Holmes-Vogtmann tree space. Among these, three RF-based methods, RF(-), RF(+), and GRF, were examined in detail on generated clusters of phylogenetic trees defined on different but mutually overlapping sets of taxa. Additionally, we reviewed nine related techniques, including leaf imputation methods, the tree edit distance, and visual comparison. A comparison of the related distance measures, highlighting their principal advantages and shortcomings, is provided. This review offers valuable insights into their applicability and performance, guiding the appropriate use of these metrics based on tree type (rooted or unrooted) and information type (topological or branch lengths).

Generation of accurate, expandable phylogenomic trees with uDance

Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues.

Recent progress on methods for estimating and updating large phylogenies

With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the past few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g. incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Distance-Based Phylogenetic Placement with Statistical Support

Phylogenetic identification of unknown sequences by placing them on a tree is routinely attempted in modern ecological studies. Such placements are often obtained from incomplete and noisy data, making it essential to augment the results with some notion of uncertainty. While the standard likelihood-based methods designed for placement naturally provide such measures of uncertainty, the newer and more scalable distance-based methods lack this crucial feature. Here, we adopt several parametric and nonparametric sampling methods for measuring the support of phylogenetic placements that have been obtained with the use of distances. Comparing the alternative strategies, we conclude that nonparametric bootstrapping is more accurate than the alternatives. We go on to show how bootstrapping can be performed efficiently using a linear algebraic formulation that makes it up to 30 times faster and implement this optimized version as part of the distance-based placement software APPLES. By examining a wide range of applications, we show that the relative accuracy of maximum likelihood (ML) support values as compared to distance-based methods depends on the application and the dataset. ML is advantageous for fragmentary queries, while distance-based support values are more accurate for full-length and multi-gene datasets. With the quantification of uncertainty, our work fills a crucial gap that prevents the broader adoption of distance-based placement tools.

Metagenomic Analysis Using Phylogenetic Placement-A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10 years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

DEPP: Deep Learning Enables Extending Species Trees using Single Genes

Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without pre-specified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multi-locus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data.

Completing gene trees without species trees in sub-quadratic time

MOTIVATION

As genome-wide reconstruction of phylogenetic trees becomes more widespread, limitations of available data are being appreciated more than ever before. One issue is that phylogenomic datasets are riddled with missing data, and gene trees, in particular, almost always lack representatives from some species otherwise available in the dataset. Since many downstream applications of gene trees require or can benefit from access to complete gene trees, it will be beneficial to algorithmically complete gene trees. Also, gene trees are often unrooted, and rooting them is useful for downstream applications. While completing and rooting a gene tree with respect to a given species tree has been studied, those problems are not studied in depth when we lack such a reference species tree.

RESULTS

We study completion of gene trees without a need for a reference species tree. We formulate an optimization problem to complete the gene trees while minimizing their quartet distance to the given set of gene trees. We extend a seminal algorithm by Brodal et al. to solve this problem in quasi-linear time. In simulated studies and on a large empirical data, we show that completion of gene trees using other gene trees is relatively accurate and, unlike the case where a species tree is available, is unbiased.

AVAILABILITY AND IMPLEMENTATION

Our method, tripVote, is available at https://github.com/uym2/tripVote.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fast and accurate distance-based phylogenetic placement using divide and conquer

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy. Distance-based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance-based phylogenetic placement method, APPLES-2, that is more accurate and scalable than existing distance-based methods and even some of the leading maximum-likelihood methods. This scalability is owed to a divide-and-conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enables us to study the effectiveness of APPLES-2 for placing microbial genomes on a data set of 10,575 microbial species using subsets of 381 marker genes. APPLES-2 has very high accuracy in this setting, placing 97% of query genomes within three branches of the optimal position in the species tree using 50 marker genes. Our proof-of-concept results show that APPLES-2 can quickly place metagenomic scaffolds on ultra-large backbone trees with high accuracy as long as a scaffold includes tens of marker genes. These results pave the path for a more scalable and widespread use of distance-based placement in various areas of molecular ecology.

App-SpaM: phylogenetic placement of short reads without sequence alignment

Motivation

Phylogenetic placement is the task of placing a query sequence of unknown taxonomic origin into a given phylogenetic tree of a set of reference sequences. A major field of application of such methods is, for example, the taxonomic identification of reads in metabarcoding or metagenomic studies. Several approaches to phylogenetic placement have been proposed in recent years. The most accurate of them requires a multiple sequence alignment of the references as input. However, calculating multiple alignments is not only time-consuming but also limits the applicability of these approaches.

Results

Herein, we propose Alignment-free phylogenetic placement algorithm based on Spaced-word Matches (App-SpaM), an efficient algorithm for the phylogenetic placement of short sequencing reads on a tree of a set of reference sequences. App-SpaM produces results of high quality that are on a par with the best available approaches to phylogenetic placement, while our software is two orders of magnitude faster than these existing methods. Our approach neither requires a multiple alignment of the reference sequences nor alignments of the queries to the references. This enables App-SpaM to perform phylogenetic placement on a broad variety of datasets.

Availability and implementation

The source code of App-SpaM is freely available on Github at https://github.com/matthiasblanke/App-SpaM together with detailed instructions for installation and settings. App-SpaM is furthermore available as a Conda-package on the Bioconda channel.

Contact

matthias.blanke@biologie.uni-goettingen.de.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Beyond Drosophila: resolving the rapid radiation of schizophoran flies with phylotranscriptomics

BACKGROUND

The most species-rich radiation of animal life in the 66 million years following the Cretaceous extinction event is that of schizophoran flies: a third of fly diversity including Drosophila fruit fly model organisms, house flies, forensic blow flies, agricultural pest flies, and many other well and poorly known true flies. Rapid diversification has hindered previous attempts to elucidate the phylogenetic relationships among major schizophoran clades. A robust phylogenetic hypothesis for the major lineages containing these 55,000 described species would be critical to understand the processes that contributed to the diversity of these flies. We use protein encoding sequence data from transcriptomes, including 3145 genes from 70 species, representing all superfamilies, to improve the resolution of this previously intractable phylogenetic challenge.

RESULTS

Our results support a paraphyletic acalyptrate grade including a monophyletic Calyptratae and the monophyly of half of the acalyptrate superfamilies. The primary branching framework of Schizophora is well supported for the first time, revealing the primarily parasitic Pipunculidae and Sciomyzoidea stat. rev. as successive sister groups to the remaining Schizophora. Ephydroidea, Drosophila's superfamily, is the sister group of Calyptratae. Sphaeroceroidea has modest support as the sister to all non-sciomyzoid Schizophora. We define two novel lineages corroborated by morphological traits, the 'Modified Oviscapt Clade' containing Tephritoidea, Nerioidea, and other families, and the 'Cleft Pedicel Clade' containing Calyptratae, Ephydroidea, and other families. Support values remain low among a challenging subset of lineages, including Diopsidae. The placement of these families remained uncertain in both concatenated maximum likelihood and multispecies coalescent approaches. Rogue taxon removal was effective in increasing support values compared with strategies that maximise gene coverage or minimise missing data.

CONCLUSIONS

Dividing most acalyptrate fly groups into four major lineages is supported consistently across analyses. Understanding the fundamental branching patterns of schizophoran flies provides a foundation for future comparative research on the genetics, ecology, and biocontrol.

Dynamic Meta-Storms enables comprehensive taxonomic and phylogenetic comparison of shotgun metagenomes at the species level

MOTIVATION

An accurate and reliable distance (or dissimilarity) among shotgun metagenomes is fundamental to deducing the beta-diversity of microbiomes. To compute the distance at the species level, current methods either ignore the evolutionary relationship among species or fail to account for unclassified organisms that cannot be mapped to definite tip nodes in the phylogenic tree, thus can produce erroneous beta-diversity pattern.

RESULTS

To solve these problems, we propose the Dynamic Meta-Storms (DMS) algorithm to enable the comprehensive comparison of metagenomes on the species level with both taxonomy and phylogeny profiles. It compares the identified species of metagenomes with phylogeny, and then dynamically places the unclassified species to the virtual nodes of the phylogeny tree via their higher-level taxonomy information. Its high speed and low memory consumption enable pairwise comparison of 100 000 metagenomes (synthesized from 3688 bacteria) within 6.4 h on a single computing node.

AVAILABILITY AND IMPLEMENTATION

An optimized implementation of DMS is available on GitHub (https://github.com/qibebt-bioinfo/dynamic-meta-storms) under a GNU GPL license. It takes the species-level profiles of metagenomes as input, and generates their pairwise distance matrix. The bacterial species-level phylogeny tree and taxonomy information of MetaPhlAn2 have been integrated into this implementation, while customized tree and taxonomy are also supported.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Forcing external constraints on tree inference using ASTRAL

Background

To account for genome-wide discordance among gene trees, several widely-used methods seek to find a species tree with the minimum distance to input gene trees. To efficiently explore the large space of species trees, some of these methods, including ASTRAL, use dynamic programming (DP). The DP paradigm can restrict the search space, and thus, ASTRAL and similar methods use heuristic methods to define a restricted search space. However, arbitrary constraints provided by the user on the output tree cannot be trivially incorporated into such restrictions. The ability to infer trees that honor user-defined constraints is needed for many phylogenetic analyses, but no solution currently exists for constraining the output of ASTRAL.

Results

We introduce methods that enable the ASTRAL dynamic programming to infer constrained trees in an effective and scalable manner. To do so, we adopt a recently developed tree completion algorithm and extend it to allow multifurcating input and output trees. In simulation studies, we show that the approach for honoring constraints is both effective and fast. On real data, we show that constrained searches can help interrogate branches not recovered in the optimal ASTRAL tree to reveal support for alternative hypotheses.

Conclusions

The new algorithm is added ASTRAL to all user-provided constraints on the species tree.