Professor Dan Graur is writing a book on genome evolution and kindly shared with us the text on reticulate evolution. Those with computational and mathematical training may find this field intellectually exciting, because -
The methodology for reconstructing phylogenetic networks from empirical data is still in its infancy despite a veritable deluge of publications on the subject (e.g., Nakhleh et al. 2005, 2008; Huson and Bryant 2006; Jin et al. 2006, 2007; Than et al. 2008; Willson 2008; Meng and Kubatko 2009; Nakhleh 2009; Huson et al. 2009, 2011; Huson and Scornavacca 2011; Morrison 2011).
We will go through what bioinformatics methods are already available. Please note that this commentary is not from an expert, and therefore you are advised to do your due diligence.
Firstly, what is the problem?
In a 2009 review, Bapteste et al wrote -
Two sentences from reviewer John M. Logsdon, Jr. summarize the main message of the paper.
The prokaryotic tree of life is dead!
Long live the prokaryotic tree of life!
There you go.
Long answer - availability of genomic data from many prokaryotes show that prokaryotic evolution cannot be explained in terms of a tree, because the species had extensive amount of lateral gene transfer in the past.
Background: The concept of a tree of life is prevalent in the evolutionary literature. It stems from attempting to obtain a grand unified natural system that reflects a recurrent process of species and lineage splittings for all forms of life. Traditionally, the discipline of systematics operates in a similar hierarchy of bifurcating (sometimes multifurcating) categories. The assumption of a universal tree of life hinges upon the process of evolution being tree-like throughout all forms of life and all of biological time. In multicellular eukaryotes, the molecular mechanisms and species-level population genetics of variation do indeed mainly cause a tree-like structure over time. In prokaryotes, they do not. Prokaryotic evolution and the tree of life are two different things, and we need to treat them as such, rather than extrapolating from macroscopic life to prokaryotes. In the following we will consider this circumstance from philosophical, scientific, and epistemological perspectives, surmising that phylogeny opted for a single model as a holdover from the Modern Synthesis of evolution.
Results: It was far easier to envision and defend the concept of a universal tree of life before we had data from genomes. But the belief that prokaryotes are related by such a tree has now become stronger than the data to support it. The monistic concept of a single universal tree of life appears, in the face of genome data, increasingly obsolete. This traditional model to describe evolution is no longer the most scientifically productive position to hold, because of the plurality of evolutionary patterns and mechanisms involved. Forcing a single bifurcating scheme onto prokaryotic evolution disregards the non-tree-like nature of natural variation among prokaryotes and accounts for only a minority of observations from genomes.
Conclusion: Prokaryotic evolution and the tree of life are two different things. Hence we will briefly set out alternative models to the tree of life to study their evolution. Ultimately, the plurality of evolutionary patterns and mechanisms involved, such as the discontinuity of the process of evolution across the prokaryote-eukaryote divide, summons forth a pluralistic approach to studying evolution.
What can we do then? Enter the networks ! Below, we will link to a number of papers from various groups working on developing mathematical methods to construct phylogenetic networks. More detailed discussion will follow later.
Huson and Bryant - Application of Phylogenetic Networks in Evolutionary Studies
The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential ?rst step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are de?ned, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent con?dence sets of trees and introduce a conservative statistical test for whether the con?icting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.
Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa.
Huson (book) - Phylogenetic Networks: Concepts, Algorithms and Applications
Nakleh (book chapter) - Evolutionary Phylogenetic Networks: Models and Issues
Morrison - free electronic book
and lastly a very recent paper to help you get all other references -
We will add more on this topic later.