biostudies-literatureJoe SKRIBB Research Initiative ProgramMinistry of Science and ICT, South Korea318https://www.ebi.ac.uk/biostudies/studies/S-EPMC10976732biostudies-literatureUnknown25(1)<h4>Background</h4>Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.<h4>Results</h4>Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing rBMC genomicsComparison of structural variant callers for massive whole-genome sequence data.PMC10976732NRF-2020M3E5D708517212, 2020M3A9I6A0103605713NTIS-1711170620Kim SKim SYPark JHJoe SPark JLYeo MKYang JOKim JLee DfalseComparison of structural variant callers for massive whole-genome sequence data.<h4>Background</h4>Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.<h4>Results</h4>Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing r2024-01-01T00:00:00Z2024 Mar2026-05-29T11:21:46.931Z2025-04-05T23:17:29.345ZS-EPMC109767323854909210.1186/s12864-024-10239-9