Methods for copy number aberration detection from single-cell DNA-sequencing data

Xian F Mallory,Nicholas Navin,Mohammadamin Edrisi,Luay Nakhleh

doi:10.1186/s13059-020-02119-8

Xian F Mallory, Nicholas Navin + Show 2 more

Open Access

https://doi.org/10.1186/s13059-020-02119-8

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Abstract

Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.

Highlights

Intra-tumor heterogeneity (ITH) has been a major confounding factor in cancer prognosis, treatment, and prevention [1–4]
Computational tools must be developed for detecting mutations in the individual cells and inferring the cell-lineage tree that describes the evolutionary history of the cells from their most recent common ancestral cell, along with the mutations that occurred
Inferring a phylogenetic tree based on Copy number aberrations (CNAs) detected from scDNAseq data to capture the cell-lineage tree which is crucial for unraveling ITH yet has not been extensively studied or pursued [27]

Summary

Background

Intra-tumor heterogeneity (ITH) has been a major confounding factor in cancer prognosis, treatment, and prevention [1–4]. Degenerate oligonucleotide-primed PCR (DOP-PCR) [17–20] and Transposon Barcoded library (TnBC) [21] are two single-cell DNA amplification protocols generating data suitable for CNA detection and are limited in terms of their coverage depth and uniformity. Inferring a phylogenetic tree based on CNAs detected from scDNAseq data to capture the cell-lineage tree which is crucial for unraveling ITH yet has not been extensively studied or pursued [27]. We review eight methods for detecting CNV/CNAs from the perspective of the application to scDNAseq data of human cancer genomes. We discuss these methods in terms of seven general steps that are often applied to scDNAseq data for CNA detection, and highlight the strengths and limitations of each method. While we refer the readers to [45–47] for more detailed information, we would like to

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