CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples. (6th February 2020)

• Record Type: Journal Article
• Authors: Posada, David
• Editors: Townsend, Jeffrey
• Is Part Of: Molecular biology and evolution. Volume 37:Number 5(2020)
• Page Start: 1535
• Page End: 1542
• Publication Date: 2020-02-06
• Subjects: somatic evolution -- single-cell genomics -- allele dropout -- amplification error; Molecular biology -- Periodicals; Molecular evolution -- Periodicals; Evolution, Molecular -- Periodicals; Molecular Biology -- Periodicals; 572.8
• DOI: 10.1093/molbev/msaa025
• Languages: English
• ISSNs: 0737-4038

Abstract

Abstract: 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 .