Inferring Species Trees from Incongruent Multi-Copy Gene Trees Using the Robinson-Foulds Distance

We present a new method for inferring species trees from multi-copy gene trees. Our method is based on a generalization of the Robinson-Foulds (RF) distance to multi-labeled trees (mul-trees), i.e., gene trees in which multiple leaves can have the same label. Unlike most previous phylogenetic methods using gene trees, this method does not assume that gene tree incongruence is caused by a single, specific biological process, such as gene duplication and loss, deep coalescence, or lateral gene transfer. We prove that it is NP-hard to compute the RF distance between two mul-trees, but it is easy to calculate the generalized RF distance between a mul-tree and a singly-labeled tree. Motivated by this observation, we formulate the RF supertree problem for mul-trees (MulRF), which takes a collection of mul-trees and constructs a species tree that minimizes the total RF distance from the input mul-trees. We present a fast heuristic algorithm for the MulRF supertree problem. Simulation experiments demonstrate that the MulRF method produces more accurate species trees than gene tree parsimony methods when incongruence is caused by gene tree error, duplications and losses, and/or lateral gene transfer. Furthermore, the MulRF heuristic runs quickly on data sets containing hundreds of trees with up to a hundred taxa.Comment: 16 pages, 11 figure

Exploring Complex Disease Gene Relationships Using Simultaneous Analysis

The characterization of complex diseases remains a great challenge for biomedical researchers due to the myriad interactions of genetic and environmental factors. Adaptation of phylogenomic techniques to increasingly available genomic data provides an evolutionary perspective that may elucidate important unknown features of complex diseases. Here an automated method is presented that leverages publicly available genomic data and phylogenomic techniques. The approach is tested with nine genes implicated in the development of Alzheimer Disease, a complex neurodegenerative syndrome. The developed technique, which is an update to a previously described Perl script called “ASAP,” was implemented through a suite of Ruby scripts entitled “ASAP2,” first compiles a list of sequence-similarity based orthologues using PSI-BLAST and a recursive NCBI BLAST+ search strategy, then constructs maximum parsimony phylogenetic trees for each set of nucleotide and protein sequences, and calculates phylogenetic metrics (partitioned Bremer support values, combined branch scores, and Robinson-Foulds distance) to provide an empirical assessment of evolutionary conservation within a given genetic network. This study demonstrates the potential for using automated simultaneous phylogenetic analysis to uncover previously unknown relationships among disease-associated genes that may not have been apparent using traditional, single-gene methods. Furthermore, the results provide the first integrated evolutionary history of an Alzheimer Disease gene network and identify potentially important co-evolutionary clustering around components of oxidative stress pathways

GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene Family Tree Inference under Gene Duplication, Transfer, and Loss

Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstrap trees) and rely on approximations and heuristics that limit the degree of tree space exploration. Here, we present GeneRax, the first maximum likelihood species-tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared with competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson–Foulds distance. On empirical data sets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1,099 Cyanobacteria families in 8 min on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax (last accessed June 17, 2020)

TRACTION: Fast Non-Parametric Improvement of Estimated Gene Trees

Gene tree correction aims to improve the accuracy of a gene tree by using computational techniques along with a reference tree (and in some cases available sequence data). It is an active area of research when dealing with gene tree heterogeneity due to duplication and loss (GDL). Here, we study the problem of gene tree correction where gene tree heterogeneity is instead due to incomplete lineage sorting (ILS, a common problem in eukaryotic phylogenetics) and horizontal gene transfer (HGT, a common problem in bacterial phylogenetics). We introduce TRACTION, a simple polynomial time method that provably finds an optimal solution to the RF-Optimal Tree Refinement and Completion Problem, which seeks a refinement and completion of an input tree t with respect to a given binary tree T so as to minimize the Robinson-Foulds (RF) distance. We present the results of an extensive simulation study evaluating TRACTION within gene tree correction pipelines on 68,000 estimated gene trees, using estimated species trees as reference trees. We explore accuracy under conditions with varying levels of gene tree heterogeneity due to ILS and HGT. We show that TRACTION matches or improves the accuracy of well-established methods from the GDL literature under conditions with HGT and ILS, and ties for best under the ILS-only conditions. Furthermore, TRACTION ties for fastest on these datasets. TRACTION is available at https://github.com/pranjalv123/TRACTION-RF and the study datasets are available at https://doi.org/10.13012/B2IDB-1747658_V1

Assessing the Performance of Single-Copy Genes for Recovering Robust Phylogenies

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Inference of parsimonious species phylogenies from multi-locus data

The main focus of this dissertation is the inference of species phylogenies, i.e. evolutionary histories of species. Species phylogenies allow us to gain insights into the mechanisms of evolution and to hypothesize past evolutionary events. They also find applications in medicine, for example, the understanding of antibiotic resistance in bacteria. The reconstruction of species phylogenies is, therefore, of both biological and practical importance. In the traditional method for inferring species trees from genetic data, we sequence a single locus in species genomes, reconstruct a gene tree, and report it as the species tree. Biologists have long acknowledged that a gene tree can be different from a species tree, thus implying that this traditional method might infer the wrong species tree. Moreover, reticulate events such as horizontal gene transfer and hybridization make the evolution of species no longer tree-like. The availability of multi-locus data provides us with excellent opportunities to resolve those long standing problems. In this dissertation, we present parsimony-based algorithms for reconciling species/gene tree incongruence that is assumed to be due solely to lineage sorting. We also describe a unified framework for detecting hybridization despite lineage sorting. To address the first problem of species/gene tree incongruence caused by lineage sorting, we present three algorithms. In Chapter 3, we present an algorithm based on an integer-linear programming (ILP) formula to infer the species tree's topology and divergence times from multiple gene trees. In Chapter 4, we describe two methods that infer the species tree by minimizing deep coalescences (MDC), a criterion introduced by Maddison in 1997. The first method is also based on an ILP formula, but it eliminates the enumeration phase of candidate species trees of the algorithm in Chapter 3. The second algorithm further eliminates the dependence on external ILP solvers by employing dynamic programming. We ran those methods on both biological and simulated data, and experimental results demonstrate their high accuracy and speed in species tree inference, which makes them suitable for analyzing multi-locus data. The second problem this dissertation deals with is reticulation (e.g., horizontal gene transfer, hybridization) detection despite lineage sorting. The phylogeny-based approach compares the evolutionary histories of different genomic regions and test them for incongruence that would indicate hybridization. However, since species tree and gene tree incongruence can also be due to lineage sorting, phylogeny-based hybridization methods might overestimate the amount of hybridization. We present in this dissertation a framework that can handle both hybridization and lineage sorting simultaneously. In this framework, we extend the MDC criterion to phylogenetic networks, and use it to propose a heuristic to detect hybridization despite lineage sorting. Empirical results on a simulated and a yeast data set show its promising performance, as well as several directions for future research