Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

Abstract

Background With the rapid development of long-read sequencing technologies, it is possible to reveal the full spectrum of genetic structural variation (SV). However, the expensive cost, finite read length and high sequencing error for long-read data greatly limit the widespread adoption of SV calling. Therefore, it is urgent to establish guidance concerning sequencing coverage, read length, and error rate to maintain high SV yields and to achieve the lowest cost simultaneously. Results In this study, we generated a full range of simulated error-prone long-read datasets containing various sequencing settings and comprehensively evaluated the performance of SV calling with state-of-the-art long-read SV detection methods. The benchmark results demonstrate that almost all SV callers perform better when the long-read data reach 20× coverage, 20 kbp average read length, and approximately 10–7.5% or below 1% error rates. Furthermore, high sequencing coverage is the most influential factor in promoting SV calling, while it also directly determines the expensive costs. Conclusions Based on the comprehensive evaluation results, we provide important guidelines for selecting long-read sequencing settings for efficient SV calling. We believe these recommended settings of long-read sequencing will have extraordinary guiding significance in cutting-edge genomic studies and clinical practices.

Benchmarking Oxford Nanopore Read Alignment-Based Structural Variant Detection Tools in Crop Plant Genomes

“…The findings are in line with a recent study reporting that cuteSV performed better than other tested SV tools such as Sniffles1, SVIM, and pbsv for precision and recall at both SV calling and genotyping in human datasets (Bolognini and Magi, 2021). Increasing coverage improved recall and F1-scores for all tested SVs calling combinations, confirming that the probability of detecting quality SVs increases with more sequencing coverage (Jiang et al, 2021). However, even at low coverages (5×) using cuteSV, Sniffles2, and dysgu for SV detection from reads aligned by minimap2 achieved >0.8 F1-scores on simulated datasets, suggesting that Oxford Nanopore technology might be suitable for large-scale low coverage re-sequencing projects.…”

“…Many of the SV detection tools are benchmarked primarily on human/animal datasets, (Bolognini and Magi, 2021; Coster et al, 2019; Dierckxsens et al, 2021; Jiang et al, 2020; Jiang et al, 2021; Zhou et al, 2019), however the complexity and different SV profiles of crop plant genomes might bring unique challenges. Therefore, to guide the design of large-scale long-read re-sequencing studies, this study performed comprehensive benchmarking of popular SV calling tools with a focus on tool performance at lower sequencing coverage.…”

Progress in Methods for Copy Number Variation Profiling

“…In turn, the Oxford nanopore technology is based on the evaluation of change in an electrical current induced by a single-stranded DNA molecule passing through a nanopore. In either case, to detect variations, signatures inside the reads completely covering variations and signatures indicating the presence of a variation based on discrepancies between the reads (orientation, size, or location) are analyzed [114,115] Moreover, calling efficiency depends more on coverage than on the read length or error rate [116].…”

Regenotyping structural variants through an accurate force-calling method

“…It can consider all samples simultaneously and generates high-quality population-scale variant callsets, which can give an overall reveal of the variant distribution of population. Since the much more difficulty of obtaining reliable genotypes for a given SV across a population group, the critical step is to perform a re-genotyping for each sample at all SV positions, which benefits refining the original genotypes or filling them up due to insufficient coverage [15, 16] .…”