Using all gene families vastly expands data available for phylogenomic inference

Shared single copy genes are generally reliable for inferring phylogenetic relationships among polyploid taxa

Polyploidy, or whole-genome duplication, is expected to confound the inference of species trees with phylogenetic methods for two reasons. First, the presence of retained duplicated genes requires the reconciliation of the inferred gene trees to a proposed species tree. Second, even if the analyses are restricted to shared single copy genes, the occurrence of reciprocal gene loss, where the surviving genes in different species are paralogs from the polyploidy rather than orthologs, will mean that such genes will not have evolved under the corresponding species tree and may not produce gene trees that allow inference of that species tree. Here we analyze three different ancient polyploidy events, using synteny-based inferences of orthology and paralogy to infer gene trees from nearly 17,000 sets of homologous genes. We find that the simple use of single copy genes from polyploid organisms provides reasonably robust phylogenetic signals, despite the presence of reciprocal gene losses. Such gene trees are also most often in accord with the inferred species relationships inferred from maximum likelihood models of gene loss after polyploidy: a completely distinct phylogenetic signal present in these genomes. As seen in other studies, however, we find that methods for inferring phylogenetic confidence yield high support values even in cases where the underlying data suggest meaningful conflict in the phylogenetic signals.

Single-worm long-read sequencing reveals genome diversity in free-living nematodes

Obtaining sufficient genetic material from a limited biological source is currently the primary operational bottleneck in studies investigating biodiversity and genome evolution. In this study, we employed multiple displacement amplification (MDA) and Smartseq2 to amplify nanograms of genomic DNA and mRNA, respectively, from individual Caenorhabditis elegans. Although reduced genome coverage was observed in repetitive regions, we produced assemblies covering 98% of the reference genome using long-read sequences generated with Oxford Nanopore Technologies (ONT). Annotation with the sequenced transcriptome coupled with the available assembly revealed that gene predictions were more accurate, complete and contained far fewer false positives than de novo transcriptome assembly approaches. We sampled and sequenced the genomes and transcriptomes of 13 nematodes from early-branching species in Chromadoria, Dorylaimia and Enoplia. The basal Chromadoria and Enoplia species had larger genome sizes, ranging from 136.6 to 738.8 Mb, compared with those in the other clades. Nine mitogenomes were fully assembled, and displayed a complete lack of synteny to other species. Phylogenomic analyses based on the new annotations revealed strong support for Enoplia as sister to the rest of Nematoda. Our result demonstrates the robustness of MDA in combination with ONT, paving the way for the study of genome diversity in the phylum Nematoda and beyond.

Major Revisions in Pancrustacean Phylogeny and Evidence of Sensitivity to Taxon Sampling

The clade Pancrustacea, comprising crustaceans and hexapods, is the most diverse group of animals on earth, containing over 80% of animal species and half of animal biomass. It has been the subject of several recent phylogenomic analyses, yet relationships within Pancrustacea show a notable lack of stability. Here, the phylogeny is estimated with expanded taxon sampling, particularly of malacostracans. We show small changes in taxon sampling have large impacts on phylogenetic estimation. By analyzing identical orthologs between two slightly different taxon sets, we show that the differences in the resulting topologies are due primarily to the effects of taxon sampling on the phylogenetic reconstruction method. We compare trees resulting from our phylogenomic analyses with those from the literature to explore the large tree space of pancrustacean phylogenetic hypotheses and find that statistical topology tests reject the previously published trees in favor of the maximum likelihood trees produced here. Our results reject several clades including Caridoida, Eucarida, Multicrustacea, Vericrustacea, and Syncarida. Notably, we find Copepoda nested within Allotriocarida with high support and recover a novel relationship between decapods, euphausiids, and syncarids that we refer to as the Syneucarida. With denser taxon sampling, we find Stomatopoda sister to this latter clade, which we collectively name Stomatocarida, dividing Malacostraca into three clades: Leptostraca, Peracarida, and Stomatocarida. A new Bayesian divergence time estimation is conducted using 13 vetted fossils. We review our results in the context of other pancrustacean phylogenetic hypotheses and highlight 15 key taxa to sample in future studies.

Improving quartet graph construction for scalable and accurate species tree estimation from gene trees

methods are widely used to estimate species trees from genome-scale data. However, they can fail to produce accurate species trees when the input gene trees are highly discordant because of estimation error and biological processes, such as incomplete lineage sorting. Here, we introduce TREE-QMC, a new summary method that offers accuracy and scalability under these challenging scenarios. TREE-QMC builds upon weighted Quartet Max Cut, which takes weighted quartets as input and then constructs a species tree in a divide-and-conquer fashion, at each step forming a graph and seeking its max cut. The wQMC method has been successfully leveraged in the context of species tree estimation by weighting quartets by their frequencies in the gene trees; we improve upon this approach in two ways. First, we address accuracy by normalizing the quartet weights to account for "artificial taxa" introduced during the divide phase so subproblem solutions can be combined during the conquer phase. Second, we address scalability by introducing an algorithm to construct the graph directly from the gene trees; this gives TREE-QMC a time complexity of [Formula: see text], where n is the number of species and k is the number of gene trees, assuming the subproblem decomposition is perfectly balanced. These contributions enable TREE-QMC to be highly competitive in terms of species tree accuracy and empirical runtime with the leading quartet-based methods, even outperforming them on some model conditions explored in our simulation study. We also present the application of these methods to an avian phylogenomics data set.

Identifying and addressing methodological incongruence in phylogenomics: A review

The availability of phylogenetic data has greatly expanded in recent years. As a result, a new era in phylogenetic analysis is dawning-one in which the methods we use to analyse and assess our data are the bottleneck to producing valuable phylogenetic hypotheses, rather than the need to acquire more data. This makes the ability to accurately appraise and evaluate new methods of phylogenetic analysis and phylogenetic artefact identification more important than ever. Incongruence in phylogenetic reconstructions based on different datasets may be due to two major sources: biological and methodological. Biological sources comprise processes like horizontal gene transfer, hybridization and incomplete lineage sorting, while methodological ones contain falsely assigned data or violations of the assumptions of the underlying model. While the former provides interesting insights into the evolutionary history of the investigated groups, the latter should be avoided or minimized as best as possible. However, errors introduced by methodology must first be excluded or minimized to be able to conclude that biological sources are the cause. Fortunately, a variety of useful tools exist to help detect such misassignments and model violations and to apply ameliorating measurements. Still, the number of methods and their theoretical underpinning can be overwhelming and opaque. Here, we present a practical and comprehensive review of recent developments in techniques to detect artefacts arising from model violations and poorly assigned data. The advantages and disadvantages of the different methods to detect such misleading signals in phylogenetic reconstructions are also discussed. As there is no one-size-fits-all solution, this review can serve as a guide in choosing the most appropriate detection methods depending on both the actual dataset and the computational power available to the researcher. Ultimately, this informed selection will have a positive impact on the broader field, allowing us to better understand the evolutionary history of the group of interest.

OrthoSNAP: A tree splitting and pruning algorithm for retrieving single-copy orthologs from gene family trees

Molecular evolution studies, such as phylogenomic studies and genome-wide surveys of selection, often rely on gene families of single-copy orthologs (SC-OGs). Large gene families with multiple homologs in 1 or more species-a phenomenon observed among several important families of genes such as transporters and transcription factors-are often ignored because identifying and retrieving SC-OGs nested within them is challenging. To address this issue and increase the number of markers used in molecular evolution studies, we developed OrthoSNAP, a software that uses a phylogenetic framework to simultaneously split gene families into SC-OGs and prune species-specific inparalogs. We term SC-OGs identified by OrthoSNAP as SNAP-OGs because they are identified using a splitting and pruning procedure analogous to snapping branches on a tree. From 415,129 orthologous groups of genes inferred across 7 eukaryotic phylogenomic datasets, we identified 9,821 SC-OGs; using OrthoSNAP on the remaining 405,308 orthologous groups of genes, we identified an additional 10,704 SNAP-OGs. Comparison of SNAP-OGs and SC-OGs revealed that their phylogenetic information content was similar, even in complex datasets that contain a whole-genome duplication, complex patterns of duplication and loss, transcriptome data where each gene typically has multiple transcripts, and contentious branches in the tree of life. OrthoSNAP is useful for increasing the number of markers used in molecular evolution data matrices, a critical step for robustly inferring and exploring the tree of life.