Abstract
BACKGROUND: Structural variations (SVs) have recently become a topic of great interest in the area of genetic diversity and trait regulation. As genomic sequencing technologies have rapidly advanced, longer reads have been used to identify SVs at high resolution and with increased accuracy. It is important to choose a suitable sequencing platform and appropriate sequencing depth for SV detection in the pear genome. RESULTS: In this study, two types of long reads from sequencing platforms, continuous long reads from Pacific Biosciences (PB-CLR) and long reads from Oxford Nanopore Technologies (ONT), were used to comprehensively analyze and compare SVs in the pear genome. The mapping rate of long reads was higher when the program Minimap2 rather than the other three mapping tools (NGMLR, LRA and Winnowmap2) was used. Three SV detection programs (Sniffles_v2, CuteSV, and Nanovar) were compared, and Nanovar had the highest sensitivity in detecting SVs at low sequencing depth (10-15×). A sequencing depth of 15× was suitable for SV detection in the pear genome using Nanovar. SVs detected by Sniffles_v2 and CuteSV with ONT reads had the high overlap with presence/absence variations (PAVs) in the pear cultivars 'Bartlett' and 'Dangshansuli', both of them with 38% of insertions and 55% of deletions overlapping with PAVs at sequencing depth of 30×. For the ONT sequencing data, over 37,526 SVs spanning ~ 28 Mb were identified by all three software packages for the 'Bartlett' and 'Dangshansuli' genomes. Those SVs were annotated and combined with transcriptome profiles derived from 'Bartlett' and 'Dangshansuli' fruit flesh at 60 days after cross-pollination. Several genes related to levels of sugars, acid, stone cells, and aromatic compounds were identified among the SVs. Transcription factors were then predicted among those genes, and results included bHLH, ERF, and MYB genes. CONCLUSION: SV detection is of great significance in exploring phenotypic differences between pear varieties. Our study provides a framework for assessment of different SV software packages and sequencing platforms that can be applied in other plant genome studies. Based on these analyses, ONT sequencing data was determined to be more suitable than PB-CLR for SV detection in the pear genome. This analysis model will facilitate screening of genes related to agronomic traits in other crops.