NDLI: Comparison of multiple algorithms to reliably detect structural variants in pears

Content Provider	Springer Nature : BioMed Central
Author	Liu, Yueyuan Zhang, Mingyue Sun, Jieying Chang, Wenjing Sun, Manyi Zhang, Shaoling Wu, Jun
Abstract	Background Structural variations (SVs) have been reported to play an important role in genetic diversity and trait regulation. Many computer algorithms detecting SVs have recently been developed, but the use of multiple algorithms to detect high-confidence SVs has not been studied. The most suitable sequencing depth for detecting SVs in pear is also not known. Results In this study, a pipeline to detect SVs using next-generation and long-read sequencing data was constructed. The performances of seven types of SV detection software using next-generation sequencing (NGS) data and two types of software using long-read sequencing data (SVIM and Sniffles), which are based on different algorithms, were compared. Of the nine software packages evaluated, SVIM identified the most SVs, and Sniffles detected SVs with the highest accuracy (> 90%). When the results from multiple SV detection tools were combined, the SVs identified by both MetaSV and IMR/DENOM, which use NGS data, were more accurate than those identified by both SVIM and Sniffles, with mean accuracies of 98.7 and 96.5%, respectively. The software packages using long-read sequencing data required fewer CPU cores and less memory and ran faster than those using NGS data. In addition, according to the performances of assembly-based algorithms using NGS data, we found that a sequencing depth of 50× is appropriate for detecting SVs in the pear genome. Conclusion This study provides strong evidence that more than one SV detection software package, each based on a different algorithm, should be used to detect SVs with higher confidence, and that long-read sequencing data are better than NGS data for SV detection. The SV detection pipeline that we have established will facilitate the study of diversity in other crops.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-020-6455-x.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-020-6455-x
Journal	BMC Genomics
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2020-01-20
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics SV detection NGS Long-read sequencing Sequencing depth Accuracy of SVs SV calling pipeline
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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