Arthur Zwaenepoel

Arthur Zwaenepoel — PhD Student
Joined the group in 2017

As a computational biologist in a broad sense, I currently direct my research efforts towards tackling questions in evolutionary biology by computational and statistical means. I am mainly working in the field of evolutionary genomics, where we try to understand the evolution of genomes by combining phylogenetics, molecular evolution and comparative genomics. Here I am focusing on the evolutionary importance of ancient whole genome duplications (in plants) and their inference from genome data.

I am strongly impressed by the power of stochastic models and (bayesian) statistics in science, and strive for fruitful applications thereof in my research. Additionally, I enjoy theoretical (eco-)evolutionary genetics, and I would like to study the interplay between evolutionary, ecological and demographic factors that might have led to an increased establishment of polyploid species during periods of environmental upheaval from a theoretical perspective. In general, I am intrigued, but also to some degree skeptical, about the possibility and fruitfulness of a 'theoretical biology'.

Perhaps the first lesson to be learned from biology is that there are lessons to be learned from biology. - Robert Rosen (2013), Essays on Life Itself

more/less

Birth: February 20, 1995, Ghent (Belgium)

Education

September 2012 - June 2015: Bachelor of Science in Biochemistry & Biotechnology, Ghent University
September 2013 - June 2015: Honours programme: Quetelet Colleges, Ghent University
September 2015 - June 2017: Master of Science in Bioinformatics (Systems Biology), Ghent University

Publications

  1. Zwaenepoel, A., Li, Z., Lohaus, R., & Van de Peer, Y. (2019). Finding evidence for whole genome duplications : a reappraisal. MOLECULAR PLANT, 12(2), 133–136.
  2. Zwaenepoel, A., & Van de Peer, Y. (2019). Inference of ancient whole genome duplications and the evolution of the gene duplication and loss rate. (S. Wright, Ed.)Molecular Biology and Evolution, 36(7), 1384–1404.
    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.
  3. Zwaenepoel, Arthur, & Van de Peer, Y. (2019). wgd : simple command line tools for the analysis of ancient whole genome duplications. BIOINFORMATICS.
    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.
  4. Zwaenepoel, Arthur, Diels, T., Amar, D., Van Parys, T., Shamir, R., Van de Peer, Y., & Tzfadia, O. (2018). MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants. FRONTIERS IN PLANT SCIENCE, 9.
    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.