Theoretical estimates on the expected number of mutations needed to reconstruct clonal lineage trees

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ID: 327875
2026
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Abstract
Abstract Motivation Phylogenetics faces a growing challenge from increasingly large and complicated data sets enabled by ever-improving sequencing technologies. The issue is particularly acute for somatic evolution studies, such as cancer cell lineages, where single-cell data sets may include tens of thousands of mutations in hundreds of thousands of genetically distinct cells. Simultaneously, the biological complexity of somatic evolution has led to complex phylogeny methods that struggle to scale to even modest data sizes. Results We explore the theoretical and empirical basis for one strategy for managing these large data sets: subsampling mutations for the computationally challenging phylogeny problem followed by faster placement of mutations on a putatively known guide tree. We specifically focus on determining the number of mutations sufficient to recover the true phylogeny at some level of resolution with high probability. We theoretically analyze variants of several common models that underlie popular tools for building clonal lineage trees. We further test these bounds through simulations of these models, extensions of them, and real biological datasets. The results suggest that modest numbers of mutations suffice to reconstruct clonal tree topologies for typical numbers of clones, supporting subsampling as a general strategy for managing the challenges of ever-growing data. Availability and Implementation All analysis code and scripts used for data simulation are implemented in Python 3 and available at https://github.com/CMUSchwartzLab/mutation-subsampling.git Supplementary information Additional supplementary methods and results are provided with the online version of the manuscript.
Reference Key
openalex_W7211875256 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nishat Anjum Bristy, Russell Schwartz
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag264
URL
https://doi.org/10.1093/bioadv/vbag264
Keywords Keywords not found

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