Paper List

Journal: ArXiv Preprint
Published: Unknown
BioinformaticsEvolutionary Biology

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

Stanford University | University of Oxford | University of California, Berkeley | Peking University | Guangzhou Medical University

Yun Deng, Shing H. Zhan, Yulin Zhang, Chao Zhang, Bingjie Chen
Figure
Figure
Figure

The 30-Second View

IN SHORT: This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and statistical challenges in phylogenetics, population genetics, and cell lineage tracing.

Innovation (TL;DR)

  • Methodology Identifies deep conceptual parallels between phylogenetic placement algorithms and ARG threading methods, demonstrating how phylogenetic placement generalizes to ARG reconstruction.
  • Biology Shows that quartet-based network methods in phylogenetics and ABBA-BABA statistics in population genetics capture the same underlying signal of gene flow through asymmetric genealogical relationships.
  • Methodology Demonstrates how ARG-based migration inference methods (e.g., GAIA, spacetrees) extend classical phylogeographic approaches by leveraging the full sequence of locally correlated genealogies along the genome.

Key conclusions

  • Tree-based models provide a unified framework for ancestry inference across biological scales, with ARGs representing ~2.48 million SARS-CoV-2 genomes demonstrating pandemic-scale feasibility.
  • Methodological parallels exist across domains: phylogenetic placement algorithms share core logic with ARG threading, and quartet-based methods in phylogenetics mirror ABBA-BABA statistics in population genetics for detecting gene flow.
  • Current ARG inference algorithms remain constrained by simplifying assumptions (neutrality, panmixia, constant population size) and face challenges in uncertainty quantification, particularly for non-model species or limited sample sizes.
Background and Gap： Tree-based inference methods have developed in isolation across different biological scales (species, populations, cells), creating redundant algorithmic development and missed opportunities for cross-disciplinary innovation despite shared statistical and computational challenges.

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.