Gene Transfer-Based Phylogenetics: Analytical Expressions and Additivity via Birth–Death Theory

Author:

Katriel Guy1,Mahanaymi Udi2,Brezner Shelly2,Kezel Noor3,Koutschan Christoph4,Zeilberger Doron5,Steel Mike6,Snir Sagi2

Affiliation:

1. Department of Mathematics, Braude College of Engineering , Karmiel , Israel

2. Department of Evolutionary and Environmental Biology, University of Haifa , Haifa , Israel

3. Department of Mathematics, University of Haifa , Haifa , Israel

4. RICAM, Austrian Academy of Sciences , Linz , Austria

5. Department of Mathematics, Rutgers University , New Brunwick, NJ , USA

6. School of Mathematics and Statistics, University of Canterbury , Christchurch , New Zealand

Abstract

Abstract The genomic era has opened up vast opportunities in molecular systematics, one of which is deciphering the evolutionary history in fine detail. Under this mass of data, analyzing the point mutations of standard markers is often too crude and slow for fine-scale phylogenetics. Nevertheless, genome dynamics (GD) events provide alternative, often richer information. The synteny index (SI) between a pair of genomes combines gene order and gene content information, allowing the comparison of genomes of unequal gene content, together with order considerations of their common genes. Recently, genome dynamics has been modeled as a continuous-time Markov process, and gene distance in the genome as a birth–death–immigration process. Nevertheless, due to complexities arising in this setting, no precise and provably consistent estimators could be derived, resulting in heuristic solutions. Here, we extend this modeling approach by using techniques from birth–death theory to derive explicit expressions of the system’s probabilistic dynamics in the form of rational functions of the model parameters. This, in turn, allows us to infer analytically accurate distances between organisms based on their SI. Subsequently, we establish additivity of this estimated evolutionary distance (a desirable property yielding phylogenetic consistency). Applying the new measure in simulation studies shows that it provides accurate results in realistic settings and even under model extensions such as gene gain/loss or over a tree structure. In the real-data realm, we applied the new formulation to unique data structure that we constructed—the ordered orthology DB—based on a new version of the EggNOG database, to construct a tree with more than 4.5K taxa. To the best of our knowledge, this is the largest gene-order-based tree constructed and it overcomes shortcomings found in previous approaches. Constructing a GD-based tree allows to confirm and contrast findings based on other phylogenetic approaches, as we show.

Funder

Israel Science Foundation

United States-Israel Binational Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Genetics,Ecology, Evolution, Behavior and Systematics

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