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Phylogenetics
Our group develops phylogenetics algorithms and theory to understand how organisms evolve.
Participants
PhD students
Summary
Insight into the evolutionary past of organisms has profound consequences for many areas affecting our daily life, including drug development, food production, agriculture, and biodiversity conservation to name but a few. Examples of applications where our algorithms and methodologies have shed light into in the past include the evolutionary past of yeast (Wu et al, 2008) which is of use in classifying yeasts which cause food spoilage, the origin of the evolutionary phenomenon of polyploidy (Brysting et al, 2007) which is very common in plants, including crops such as wheat, understanding the genetic diversity and dispersal of plants (Winkworth et al, 2005), reconstructing/understanding the evolution of viruses such as Hepatitis and SARS (Magiorkinis et al, 2004), and shedding light in the evolutionary relationships between over 500 different wheat varieties (Kettleborough et al, 2015) some of which were collected as part of the GEDIFLUX EU Framework V project.
Our group also works in phylogenetic combinatorics, a branch of discrete applied mathematics concerned with the mathematical structures related to phylogenetic trees and networks such as graphs, split systems, metrics and tight spans. The goal is to develop the theory that forms the basis of phylogenetic reconstruction methods. Our more recent work has mainly centred on phylogenetic networks, where the main question is how we can best model reticulate evolution. It includes a novel encoding of a certain type of phylogenetic network called an X-cactus described in [4] which has already led to a novel consensus network approach for summarizing sets of phylogenetic networks. More recently, it has focused on the problem of how to root an unrooted phylogenetic network and also on improving our understanding of the combinatorial structure of so called semidirected networks such as the one depicted in Figure 1. Motivated by the fact that organisms that inhabit different environmental niches can still exchange genetic material, we have also introduced a novel type of phylogenetic network that we call a forest-based network in [3].
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Fig 1: A phylogenetic network that appeared in [1] for a set of 25 swordtail fish and platyfish.
Various techniques for building networks that visualize complex evolutionary histories have been proposed over the years. In addition to extending and applying such techniques together with evolutionary biologists, our contributions have included the development of methods for reconstructing phylogenetic networks directly from molecular data such as the NeighborNet algorithm (Bryant and Moulton, 2004) which is available as part of the software package Splitstree4, and also the QNet software tool (Gruenewald et al, 2007), the TriiLoNet software tool (Oldman et al, 2016) and the OSF-Builder software tool (Scholz et al, 2019). More recently, we have introduced the Squirrel software tool described in [1] and proposed a new distance based model in [2] for studying convergence evolution, a phenomenon that occurs when that independent species can evolve similar feature due to the fact that they have to adopt to similar environmental niches.
The quality and quantity of data generated by Next Generation Sequencing technology has posed exciting new challenges for phylogenetics. To address some of them, several members of our group participated in and were involved in organizing a 4 month programme in phylogenetics that took place at the Isaac Newton Institute, Cambridge in 2007. More recently, some members of our group took part in the 3 months long Theory and Applications of Quantitative Phylogenomics (https://icerm.brown.edu/program/semester_program/sp-f24) program hosted by The Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University, Rhode Island, USA in 2024.
Funding
Some aspects of this long term projects have been supported with international Exchanges grants from The Royal Society, and with the London Mathematical Society’s Research in Pairs grants and Computer Science Small Grants.
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Partners
In addition to the PhD-students mentioned above, it has also included other PhD-students, postdoctoral fellows and other members of faculty. Over the years, members of the group have collaborated with researchers from all over the world including:
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Publications
[1] Squirrel: Reconstructing semi-directed phylogenetic level-1 networks from four-leaved networks of sequence alignments, N. Holtgrefe, KT Huber, L van Iersel, M Jones, S Martin, V Moulton, doi: https://doi.org/10.1101/2024.11.01.621567.
[2] A distance-based model for convergent evolution, B. Holland, K. T. Huber, V Moulton, Journal of Mathematical Biology, 2024, 88:17.
[3] Forest-based networks, KT Huber, V. Moulton, G. E. Scholz. Bulletin of Mathematical Biology 2022, 84 Article number 119.
[4] Encoding and ordering X-cactuses, A. Francis, KT Huber, V. Moulton, T. Wu. Advances in Applied Mathematics, 2023, 142:102414.