Using genotype abundance to improve phylogenetic inference
Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times. This motivates strategies to incorporate genotype abundance information in phylogenetic inference. In this paper, we synthesize a stochastic process model with standard sequence-based phylogenetic optimality, and show that tree estimation is substantially improved by doing so. Our method is validated with extensive simulations and an experimental single-cell lineage tracing study of germinal center B cell receptor affinity maturation.
Code (1)
Similar Papers 제목 키워드 기반
Statistical theory of phenotype abundance distributions: a test through exact enumeration of genotype spaces
The evolutionary dynamics of molecular populations are strongly dependent on the structure of genotype spaces. The map between genotype and phenotype determines how easily genotype spaces can be navigated and the accessi…
Non-bifurcating phylogenetic tree inference via the adaptive LASSO
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees c…
Mycorrhiza: Genotype Assignment usingPhylogenetic Networks
Motivation The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildli…
BIG-bench Machine LearningDimensionality ReductionPopulation AssignmentEcology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure
Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to…
Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants
Motivation: The abundance of gene flow in the Tree of Life challenges the notion that evolution can be represented with a fully bifurcating process, as this process cannot capture important biological realities like hybr…
Heuristic Search