CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples

Mol Biol Evol. 2020 May 1;37(5):1535-1542. doi: 10.1093/molbev/msaa025.


Our capacity to study individual cells has enabled a new level of resolution for understanding complex biological systems such as multicellular organisms or microbial communities. Not surprisingly, several methods have been developed in recent years with a formidable potential to investigate the somatic evolution of single cells in both healthy and pathological tissues. However, single-cell sequencing data can be quite noisy due to different technical biases, so inferences resulting from these new methods need to be carefully contrasted. Here, I introduce CellCoal, a software tool for the coalescent simulation of single-cell sequencing genotypes. CellCoal simulates the history of single-cell samples obtained from somatic cell populations with different demographic histories and produces single-nucleotide variants under a variety of mutation models, sequencing read counts, and genotype likelihoods, considering allelic imbalance, allelic dropout, amplification, and sequencing errors, typical of this type of data. CellCoal is a flexible tool that can be used to understand the implications of different somatic evolutionary processes at the single-cell level, and to benchmark dedicated bioinformatic tools for the analysis of single-cell sequencing data. CellCoal is available at https://github.com/dapogon/cellcoal.

Keywords: allele dropout; amplification error; single-cell genomics; somatic evolution.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Evolution, Molecular
  • Genetic Techniques*
  • Genotype*
  • Sequence Analysis, DNA
  • Single-Cell Analysis*
  • Software*