Abstract

BackgroundDetection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005–0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.ResultHere, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.ConclusionsOur comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

Highlights

  • Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years

  • Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs

  • Low-coverage highthroughput single cell sequencing has emerged in recent years and has been applied to study e.g. low-level mosaicism introduced by differing CNVs in cell subpopulations in cultured Human embryonic stem cell (hESC) samples [13]

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Summary

Introduction

Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. Copy number variation (CNV) is defined as deletion or amplification of relatively large DNA segment (from 50 basepairs to several megabases) [1]. They contribute to genetic diversity and have relevance both evolutionarily and clinically. Low-coverage sequencing is a valuable alternative for the cost efficient high-throughput monitoring of karyotypes of primary cell lines, such as human pluripotent cell lines, and is a necessity in order to karyotype formalin-fixed paraffin embedded (FFPE) samples [11, 12]. In addition to the versatility of applications of low-coverage sequencing, the advantages of this approach include lower costs and less computational resources and storage capacity compared to high-coverage sequencing

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