Inference of mutability landscapes of tumors from single cell sequencing data

Viachaslau Tsyvina, Alex Zelikovsky, Sagi Snir, Pavel Skums

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish
Article numbere1008454
JournalPLoS Computational Biology
Issue number11
StatePublished - 30 Nov 2020

Bibliographical note

Publisher Copyright:
© 2020 Tsyvina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


Dive into the research topics of 'Inference of mutability landscapes of tumors from single cell sequencing data'. Together they form a unique fingerprint.

Cite this