ArticlePLoS computational biology2020
Inference of mutability landscapes of tumors from single cell sequencing data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
- Application and prospects of single cell sequencing in tumors.Biomarker research · 2021Review
- Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.
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