Indirect Identification of Horizontal Gene Transfer

TL;DR

This paper characterizes LDT graphs used to infer horizontal gene transfer, providing polynomial-time recognition algorithms and demonstrating how to extract HGT information from these graphs through simulations and graph editing.

Contribution

It introduces a graph-theoretic framework for identifying HGT events, including recognition algorithms and methods to infer HGT from LDT graphs.

Findings

LDT graphs are a subclass of properly vertex-colored cographs.

Recognition algorithms for LDT and rs-Fitch graphs operate in polynomial time.

A greedy graph editing scheme can effectively detect HGT events from LDT graphs.

Abstract

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs.