Biology subjects

Wicke, K.

Publications and source records attributed to Wicke, K..

4 recordsLinked to original sources

bioRxiv2026

Consensus methods for trees of blobs and level-1 phylogenetic networks

Phylogenetic networks are increasingly used to represent evolutionary histories involving hybridization, introgression, and other reticulate processes that cannot be captured by a single tree. Recent advances have produced numerous methods for inferring networks from genomic data. These methods may produce multiple plausible networks due to optimality ties, bootstrap analyses, Bayesian posterior samples, or differences in tools, datasets, and parameters. While consensus methods are well established for trees and some approaches exist for rooted networks, no consensus framework currently exists for semi-directed (unrooted) networks. We address this gap by introducing a new consensus framework for phylogenetic networks, rooted or semi-directed. We first develop a method for constructing a consensus tree of blobs, taking as input networks of any complexity. Building on this approach, we construct a consensus for level-1 networks (i.e., networks with non-overlapping cycles). This method, called 'L1N consensus', takes a collection of level-1 networks as input and returns a level-1 network. The different components of this consensus network (e.g., blobs, hybrid clades, and edges) have support values that correspond to the frequency of the given component among the input networks. These methods are implemented in PhyloSummaries, building on the foundation of PhyloNetworks. We evaluate the performance of L1N consensus using large-scale simulated data. Our results demonstrate that L1N consensus outperforms existing methods in the rooted setting in terms of informativity and accuracy. Similar results are obtained in the semi-directed setting. Applying L1N consensus to Leopardus and HIV datasets confirms these findings and demonstrates the usefulness of the method.

bioinformatics↗

bioRxiv2024

NANUQ+: A divide-and-conquer approach to network estimation

Inference of a species network from genomic data remains a difficult problem, with recent progress mostly limited to the level-1 case. However, inference of the Tree of Blobs of a network, showing only the networks cut edges, can be performed for any network by TINNiK, suggesting a divide-and-conquer approach to network inference where the trees multifurcations are individually resolved to give more detailed structure. Here we develop a method, NANUQ+, to quickly perform such a level-1 resolution. Viewed as part of the NANUQ pipeline for fast level-1 inference, this gives tools for both understanding when the level-1 assumption is likely to be met and for exploring all highly-supported resolutions to cycles.

evolutionary biology↗

bioRxiv2023

Effects of phylogenetic variation on prioritization of species for conservation

Many questions in evolutionary biology require the specification of a phylogeny for downstream phylogenetic analyses. However, with the increasingly widespread availability of genomic data, phylogenetic studies are often confronted with conflicting signal in the form of genomic heterogeneity and incongruence between gene trees and the species tree. This raises the question of determining what data and phylogeny should be used in downstream analyses, and to what extent the choice of phylogeny (e.g., gene trees versus species trees) impacts the analyses and their outcomes. In this paper, we study this question in the realm of phylogenetic diversity indices, which provide ways to prioritize species for conservation based on their relative evolutionary isolation on a phylogeny, and are thus one example of downstream phylogenetic analyses. We use the Fair Proportion (FP) index, also known as the evolutionary distinctiveness score, and explore the variability in species rankings based on gene trees as compared to the species tree for several empirical data sets. Our results indicate that prioritization rankings among species vary greatly depending on the underlying phylogeny, suggesting that the choice of phylogeny is a major influence in assessing phylogenetic diversity in a conservation setting. While we use phylogenetic diversity conservation as an example, we suspect that other types of downstream phylogenetic analyses such as ancestral state reconstruction are similarly affected by genomic heterogeneity and incongruence. Our aim is thus to raise awareness of this issue and inspire new research on which evolutionary information (species trees, gene trees, or a combination of both) should form the basis for analyses in these settings.

evolutionary biology↗

bioRxiv2020

Formal Links between Feature Diversity and Phylogenetic Diversity

AO_SCPLOWBSTRACTC_SCPLOWThe extent to which phylogenetic diversity (PD) captures feature diversity (FD) is a topical and controversial question in biodiversity conservation. In this short paper, we formalise this question and establish a precise mathematical condition for FD (based on discrete characters) to coincide with PD. In this way, we make explicit the two main reasons why the two diversity measures might disagree for given data; namely, the presence of certain patterns of feature evolution and loss, and using temporal branch lengths for PD in settings that may not be appropriate (e.g. due to rapid evolution of certain features over short periods of time). Our paper also explores the relationship between the Fair Proportion index of PD and a simple index of FD (both of which correspond to Shapley values in cooperative game theory). In a second mathematical result, we show that the two indices can take identical values for any phylogenetic tree, provided the branch lengths in the tree are chosen appropriately.

evolutionary biology↗