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1. QuCo: quartet-based co-estimation of species trees and gene trees

   https://doi.org/10.1093/bioinformatics/btac265  

   Rabiee, Maryam ; Mirarab, Siavash ( June 2022 , Bioinformatics)

   Phylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.

   We introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.

   QuCo is available on https://github.com/maryamrabiee/quco.

   Supplementary data are available at Bioinformatics online.

    

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2. Estimation of speciation times under the multispecies coalescent

   https://doi.org/10.1093/bioinformatics/btac679  

   Peng, Jing ; Swofford, David L. ; Kubatko, Laura ; Martelli, ed., Pier Luigi ( October 2022 , Bioinformatics)

   The multispecies coalescent model is now widely accepted as an effective model for incorporating variation in the evolutionary histories of individual genes into methods for phylogenetic inference from genome-scale data. However, because model-based analysis under the coalescent can be computationally expensive for large datasets, a variety of inferential frameworks and corresponding algorithms have been proposed for estimation of species-level phylogenies and associated parameters, including speciation times and effective population sizes.

   We consider the problem of estimating the timing of speciation events along a phylogeny in a coalescent framework. We propose a maximum a posteriori estimator based on composite likelihood (MAPCL) for inferring these speciation times under a model of DNA sequence evolution for which exact site-pattern probabilities can be computed under the assumption of a constant θ throughout the species tree. We demonstrate that the MAPCL estimates are statistically consistent and asymptotically normally distributed, and we show how this result can be used to estimate their asymptotic variance. We also provide a more computationally efficient estimator of the asymptotic variance based on the non-parametric bootstrap. We evaluate the performance of our method using simulation and by application to an empirical dataset for gibbons.

   The method has been implemented in the PAUP* program, freely available at https://paup.phylosolutions.com for Macintosh, Windows and Linux operating systems.

   Supplementary data are available at Bioinformatics online.

    

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3. Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

   https://doi.org/10.1093/bioinformatics/btz676  

   Wu, Yufeng ; Schwartz, Russell ( August 2019 , Bioinformatics)

   Abstract Motivation Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. Results In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. Availability and implementation The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Haplogenome assembly reveals structural variation in Eucalyptus interspecific hybrids

   https://doi.org/10.1093/gigascience/giad064  

   Lötter, Anneri ; Duong, Tuan A ; Candotti, Julia ; Mizrachi, Eshchar ; Wegrzyn, Jill L ; Myburg, Alexander A ( December 2022 , GigaScience)

   De novo phased (haplo)genome assembly using long-read DNA sequencing data has improved the detection and characterization of structural variants (SVs) in plant and animal genomes. Able to span across haplotypes, long reads allow phased, haplogenome assembly in highly outbred organisms such as forest trees. Eucalyptus tree species and interspecific hybrids are the most widely planted hardwood trees with F1 hybrids of Eucalyptus grandis and E. urophylla forming the bulk of fast-growing pulpwood plantations in subtropical regions. The extent of structural variation and its effect on interspecific hybridization is unknown in these trees. As a first step towards elucidating the extent of structural variation between the genomes of E. grandis and E. urophylla, we sequenced and assembled the haplogenomes contained in an F1 hybrid of the two species.

   Using Nanopore sequencing and a trio-binning approach, we assembled the separate haplogenomes (566.7 Mb and 544.5 Mb) to 98.0% BUSCO completion. High-density SNP genetic linkage maps of both parents allowed scaffolding of 88.0% of the haplogenome contigs into 11 pseudo-chromosomes (scaffold N50 of 43.8 Mb and 42.5 Mb for the E. grandis and E. urophylla haplogenomes, respectively). We identify 48,729 SVs between the two haplogenomes providing the first detailed insight into genome structural rearrangement in these species. The two haplogenomes have similar gene content, 35,572 and 33,915 functionally annotated genes, of which 34.7% are contained in genome rearrangements.

   Knowledge of SV and haplotype diversity in the two species will form the basis for understanding the genetic basis of hybrid superiority in these trees.

    

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5. DaTeR: error-correcting phylogenetic chronograms using relative time constraints

   https://doi.org/10.1093/bioinformatics/btad084  

   Mondal, Abhijit ; Rangel, L. Thiberio ; Payette, Jack G. ; Fournier, Gregory P. ; Bansal, Mukul S. ; Schwartz, ed., Russell ( February 2023 , Bioinformatics)

   A chronogram is a dated phylogenetic tree whose branch lengths have been scaled to represent time. Such chronograms are computed based on available date estimates (e.g. from dated fossils), which provide absolute time constraints for one or more nodes of an input undated phylogeny, coupled with an appropriate underlying model for evolutionary rates variation along the branches of the phylogeny. However, traditional methods for phylogenetic dating cannot take into account relative time constraints, such as those provided by inferred horizontal transfer events. In many cases, chronograms computed using only absolute time constraints are inconsistent with known relative time constraints.

   In this work, we introduce a new approach, Dating Trees using Relative constraints (DaTeR), for phylogenetic dating that can take into account both absolute and relative time constraints. The key idea is to use existing Bayesian approaches for phylogenetic dating to sample posterior chronograms satisfying desired absolute time constraints, minimally adjust or ‘error-correct’ these sampled chronograms to satisfy all given relative time constraints, and aggregate across all error-corrected chronograms. DaTeR uses a constrained optimization framework for the error-correction step, finding minimal deviations from previously assigned dates or branch lengths. We applied DaTeR to a biological dataset of 170 Cyanobacterial taxa and a reliable set of 24 transfer-based relative constraints, under six different molecular dating models. Our extensive analysis of this dataset demonstrates that DaTeR is both highly effective and scalable and that its application can significantly improve estimated chronograms.

   Freely available from https://compbio.engr.uconn.edu/software/dater/

   Supplementary data are available at Bioinformatics online.

    

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