wgd-simple command line tools for the analysis of ancient whole genome duplications

MOTIVATION: Ancient whole genome duplications (WGDs) have been uncovered in almost all major lineages of life on Earth and the search for traces or remnants of such events has become standard practice in most genome analyses. This is especially true for plants, where ancient WGDs are abundant. Common approaches to find evidence for ancient WGDs include the construction of KS distributions and the analysis of intragenomic co-linearity. Despite the increased interest in WGDs and the acknowledgement of their evolutionary importance, user-friendly and comprehensive tools for their analysis are lacking. Here, we present an easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and co-linearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner. AVAILABILITY & IMPLEMENTATION: wgd is free and open source software implemented in Python and is available at https://github.com/arzwa/wgd. SUPPLEMENTARY INFORMATION: Supplementary methods are available at Bioinformatics online

Inference of ancient whole-genome duplications and the evolution of gene duplication and loss rates

Gene tree - species tree reconciliation methods have been employed for studying ancient whole genome duplication (WGD) events across the eukaryotic tree of life. Most approaches have relied on using maximum likelihood trees and the maximum parsimony reconciliation thereof to count duplication events on specific branches of interest in a reference species tree. Such approaches do not account for uncertainty in the gene tree and reconciliation, or do so only heuristically. The effects of these simplifications on the inference of ancient WGDs are unclear. In particular the effects of variation in gene duplication and loss rates across the species tree have not been considered. Here, we developed a full probabilistic approach for phylogenomic reconciliation based WGD inference, accounting for both gene tree and reconciliation uncertainty using a method based on the principle of amalgamated likelihood estimation. The model and methods are implemented in a maximum likelihood and Bayesian setting and account for variation of duplication and loss rate across the species tree, using methods inspired by phylogenetic divergence time estimation. We applied our newly developed framework to ancient WGDs in land plants and investigate the effects of duplication and loss rate variation on reconciliation and gene count based assessment of these earlier proposed WGDs

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Model-based detection of whole-genome duplications in a phylogeny

Ancient whole-genome duplications (WGDs) leave signatures in comparative genomic data sets that can be harnessed to detect these events of presumed evolutionary importance. Current statistical approaches for the detection of ancient WGDs in a phylogenetic context have two main drawbacks. The first is that unwarranted restrictive assumptions on the “background” gene duplication and loss rates make inferences unreliable in the face of model violations. The second is that most methods can only be used to examine a limited set of a priori selected WGD hypotheses and cannot be used to discover WGDs in a phylogeny. In this study, we develop an approach for WGD inference using gene count data that seeks to overcome both issues. We employ a phylogenetic birth–death model that includes WGD in a flexible hierarchical Bayesian approach and use reversible-jump Markov chain Monte Carlo to perform Bayesian inference of branch-specific duplication, loss, and WGD retention rates across the space of WGD configurations. We evaluate the proposed method using simulations, apply it to data sets from flowering plants, and discuss the statistical intricacies of model-based WGD inference.PhD fellowship of the Research Foundation Flanders (FWO) and the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program.http://mbe.oxfordjournals.orghj2021BiochemistryGeneticsMicrobiology and Plant Patholog

The hornwort genome and early land plant evolution

Hornworts, liverworts and mosses are three early diverging clades of land plants, and together comprise the bryophytes. Here, we report the draft genome sequence of the hornwort Anthoceros angustus. Phylogenomic inferences confirm the monophyly of bryophytes, with hornworts sister to liverworts and mosses. The simple morphology of hornworts correlates with low genetic redundancy in plant body plan, while the basic transcriptional regulation toolkit for plant development has already been established in this early land plant lineage. Although the Anthoceros genome is small and characterized by minimal redundancy, expansions are observed in gene families related to RNA editing, UV protection and desiccation tolerance. The genome of A. angustus bears the signatures of horizontally transferred genes from bacteria and fungi, in particular of genes operating in stress-response and metabolic pathways. Our study provides insight into the unique features of hornworts and their molecular adaptations to live on land

Inference of Ancient Polyploidy from Genomic Data

Whole-genome sequence data have revealed that numerous eukaryotic organisms derive from distant polyploid ancestors, even when these same organisms are genetically and karyotypically diploid. Such ancient whole-genome duplications (WGDs) have been important for long-term genome evolution and are often speculatively associated with important evolutionary events such as key innovations, adaptive radiations, or survival after mass extinctions. Clearly, reliable methods for unveiling ancient WGDs are key toward furthering understanding of the long-term evolutionary significance of polyploidy. In this chapter, we describe a set of basic established comparative genomics approaches for the inference of ancient WGDs from genomic data based on empirical age distributions and collinearity analyses, explain the principles on which they are based, and illustrate a basic workflow using the software “wgd,” geared toward a typical exploratory analysis of a newly obtained genome sequence

Model-based detection of whole-genome duplications in a phylogeny

Ancient whole-genome duplications (WGDs) leave signatures in comparative genomic data sets that can be harnessed to detect these events of presumed evolutionary importance. Current statistical approaches for the detection of ancient WGDs in a phylogenetic context have two main drawbacks. The first is that unwarranted restrictive assumptions on the ‘background’ gene duplication and loss rates make inferences unreliable in the face of model violations. The second is that most methods can only be used to examine a limited set of a priori selected WGD hypotheses; and cannot be used to discover WGDs in a phylogeny. In this study we develop an approach for WGD inference using gene count data that seeks to overcome both issues. We employ a phylogenetic birth-death model that includes WGD in a flexible hierarchical Bayesian approach, and use reversible-jump MCMC to perform Bayesian inference of branch-specific duplication, loss and WGD retention rates accross the space of WGD configurations. We evaluate the proposed method using simulations, apply it to data sets from flowering plants and discuss the statistical intricacies of model-based WGD inference

MorphDB: Prioritizing Genes for Specialized Metabolism Pathways and Gene Ontology Categories in Plants

Recent times have seen an enormous growth of “omics” data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named “MORPH bulk” (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest