STBase: one million species trees for comparative biology

Launching insectphylo.org; a new hub facilitating construction and use of synthesis molecular phylogenies of insects

The Holy Grail of an Insect Tree of Life can only be 'discovered' through extensive collaboration among taxon specialists, phylogeneticists and centralized frameworks such as Open Tree of Life, but insufficient effort from stakeholders has so far hampered this promising approach. The resultant unavailability of synthesis phylogenies is an unfortunate situation given the numerous practical usages of phylogenies in the near term and against the backdrop of the ongoing biodiversity crisis. To resolve this issue, we establish a new online hub that centralizes the collation of relevant phylogenetic data and provides the resultant synthesis molecular phylogenies. This is achieved through key developments in a proposed pipeline for the construction of a species-level insect phylogeny. The functionality of the framework is demonstrated through the construction of a highly supported, species-comprehensive phylogeny of Diptera, built from integrated omics data, COI DNA barcodes, and a compiled database of over 100 standardized, published Diptera phylogenies. Machine-readable forms of the phylogeny (and subsets thereof) are publicly available at insectphylo.org, a new public repository for species-comprehensive phylogenies for biological research.

treedata.table: a wrapper for data.table that enables fast manipulation of large phylogenetic trees matched to data

The number of terminals in phylogenetic trees has significantly increased over the last decade. This trend reflects recent advances in next-generation sequencing, accessibility of public data repositories, and the increased use of phylogenies in many fields. Despite R being central to the analysis of phylogenetic data, manipulation of phylogenetic comparative datasets remains slow, complex, and poorly reproducible. Here, we describe the first R package extending the functionality and syntax of data.table to explicitly deal with phylogenetic comparative datasets. treedata.table significantly increases speed and reproducibility during the data manipulation steps involved in the phylogenetic comparative workflow in R. The latest release of treedata.table is currently available through CRAN (https://cran.r-project.org/web/packages/treedata.table/). Additional documentation can be accessed through rOpenSci (https://ropensci.github.io/treedata.table/).

Linear-time algorithms for phylogenetic tree completion under Robinson-Foulds distance

BACKGROUND

We consider two fundamental computational problems that arise when comparing phylogenetic trees, rooted or unrooted, with non-identical leaf sets. The first problem arises when comparing two trees where the leaf set of one tree is a proper subset of the other. The second problem arises when the two trees to be compared have only partially overlapping leaf sets. The traditional approach to handling these problems is to first restrict the two trees to their common leaf set. An alternative approach that has shown promise is to first complete the trees by adding missing leaves, so that the resulting trees have identical leaf sets. This requires the computation of an optimal completion that minimizes the distance between the two resulting trees over all possible completions.

RESULTS

We provide optimal linear-time algorithms for both completion problems under the widely-used Robinson-Foulds (RF) distance measure. Our algorithm for the first problem improves the time complexity of the current fastest algorithm from quadratic (in the size of the two trees) to linear. No algorithms have yet been proposed for the more general second problem where both trees have missing leaves. We advance the study of this general problem by proposing a useful restricted version of the general problem and providing optimal linear-time algorithms for the restricted version. Our experimental results on biological data sets suggest that completion-based RF distances can be very different compared to traditional RF distances.

Construction of a Species-Level Tree of Life for the Insects and Utility in Taxonomic Profiling

Although comprehensive phylogenies have proven an invaluable tool in ecology and evolution, their construction is made increasingly challenging both by the scale and structure of publically available sequences. The distinct partition between gene-rich (genomic) and species-rich (DNA barcode) data is a feature of data that has been largely overlooked, yet presents a key obstacle to scaling supermatrix analysis. I present a phyloinformatics framework for draft construction of a species-level phylogeny of insects (Class Insecta). Matrix-building requires separately optimized pipelines for nuclear transcriptomic, mitochondrial genomic, and species-rich markers, whereas tree-building requires hierarchical inference in order to capture species-breadth while retaining deep-level resolution. The phylogeny of insects contains 49,358 species, 13,865 genera, 760 families. Deep-level splits largely reflected previous findings for sections of the tree that are data rich or unambiguous, such as inter-ordinal Endopterygota and Dictyoptera, the recently evolved and relatively homogeneous Lepidoptera, Hymenoptera, Brachycera (Diptera), and Cucujiformia (Coleoptera). However, analysis of bias, matrix construction and gene-tree variation suggests confidence in some relationships (such as in Polyneoptera) is less than has been indicated by the matrix bootstrap method. To assess the utility of the insect tree as a tool in query profiling several tree-based taxonomic assignment methods are compared. Using test data sets with existing taxonomic annotations, a tendency is observed for greater accuracy of species-level assignments where using a fixed comprehensive tree of life in contrast to methods generating smaller de novo reference trees. Described herein is a solution to the discrepancy in the way data are fit into supermatrices. The resulting tree facilitates wider studies of insect diversification and application of advanced descriptions of diversity in community studies, among other presumed applications. [Data integration; data mining; insects; phylogenomics; phyloinformatics; tree of life.].