Long-read based detection of large copy number variants with potential functional significance using the ContextSV structural variant caller
Long read sequencing provides increased read lengths required for resolving larger structural variants (SVs) in the human genome. Current widely used long-read SV callers are limited in the size and complexity of detectable SVs due to a reliance on alignment evidence. As a result, SVs with potential disease relevance may be missed. To overcome these limitations, we develop ContextSV, which integrates evidence beyond alignments to improve performance for larger SVs. Alignment evidence is integrated with copy number predictions based on coverage and SNV allele frequencies, increasing performance particularly for large copy number variants. We also develop a novel SV scoring and filtering method based on important validation features including genomic context. We perform a benchmarking study on both real and simulated datasets to demonstrate the advantages of ContextSV for identifying a class of SVs often missed by current widely used callers. Furthermore, we highlight unique advantages by identifying and experimentally validating a set of large SVs in the KOLF2.1J reference stem cell line missed by other methods. In summary, our results show that ContextSV is a valuable complement to existing long-read SV detection approaches by improving performance for a class of large SVs with potential clinical significance.