Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species

TL;DR

This paper reviews how tree-based models unify understanding of genetic ancestry across cells, populations, and species, highlighting recent methodological advances and shared challenges in inference.

Contribution

It provides a comprehensive comparison of tree-based methods across biological scales, identifying common principles and unique challenges to foster cross-disciplinary innovations.

Findings

Shared statistical and algorithmic challenges across fields

Recent advances in inferring ancestral recombination graphs

Opportunities for methodological cross-fertilization

Abstract

The ongoing explosion of genome sequence data is transforming how we reconstruct and understand the histories of biological systems. Across biological scales, from individual cells to populations and species, trees-based models provide a common framework for representing ancestry. Once limited to species phylogenetics, "tree thinking" now extends deeply to population genomics and cell biology, revealing the genealogical structure of genetic and phenotypic variation within and across organisms. Recently, there have been great methodological and computational advances on tree-based methods, including methods for inferring ancestral recombination graphs in populations, phylogenetic frameworks for comparative genomics, and lineage-tracing techniques in developmental and cancer biology. Despite differences in data types and biological contexts, these approaches share core statistical and algorithmic challenges: efficiently inferring branching histories from genomic information, integrating temporal and spatial signals, and connecting genealogical structures to evolutionary and functional processes. Recognizing these shared foundations opens opportunities for cross-fertilization between fields that are traditionally studied in isolation. By examining how tree-based methods are applied across cellular, population, and species scales, we identify the conceptual parallels that unite them and the distinct challenges that each domain presents. These comparisons offer new perspectives that can inform algorithmic innovations and lead to more powerful inference strategies across the full spectrum of biological systems.