Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

Abstract

Background 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. 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. Result Here, 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. Conclusions 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. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

“…Here, a bin is a genomic region of fixed size and a segment is a genomic region of variable size which may be obtained by merging consecutive bins with the same copy number. In CNA detection, the general steps include binning, bias removal, segmentation, and copy number assignment [19, 42]. In binning, the genome is divided into bins of certain size, usually fixed, and reads aligned to each bin are counted.…”

AcornHRD: an HRD algorithm which was highly associated with anthracycline-based neoadjuvant chemotherapy in Chinese breast cancer

“…Fundamental to evaluating the HRD status is the robust determination of copy number data, which can be obtained using either SNP arrays, whole exome sequencing (WES) or WGS. Compared SNP array-, WES-to WGS-derived CNV have shown that WGS provides much more homogenous distribution of quality parameters (genotype quality, coverage depth) [45][46][47]. Studies indicated an excellent agreement (93.75%) between the original and downsampled WGS-derived HR classi cation status [44].…”

SCONCE: A method for profiling Copy Number Alterations in Cancer Evolution using Single Cell Whole Genome Sequencing

“…We applied SCONCE to a published dataset, consisting of 34 diploid cells (as determined by cell sorting), and 4 tumor subpopulations (24,24,4, and 8 cells, respectively) from one triple negative breast cancer patient (33), a cancer type with prevalent CNAs (34).…”

“…Many large scale cancer studies are done with bulk samples, and many methods and evaluation techniques (3, 4) have been developed to identify copy number alterations in bulk sequencing, especially for low coverage data (5) and tumor heterogeneity deconvolution (6). However, bulk sequencing averages mutations across many cells and loses the granularity and detail single cell sequencing (SCS) can provide.…”