{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Joe S"],"funding":["KRIBB Research Initiative Program","Ministry of Science and ICT, South Korea"],"pagination":["318"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10976732"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<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 r"],"journal":["BMC genomics"],"pubmed_title":["Comparison of structural variant callers for massive whole-genome sequence data."],"pmcid":["PMC10976732"],"funding_grant_id":["NRF-2020M3E5D708517212, 2020M3A9I6A0103605713","NTIS-1711170620"],"pubmed_authors":["Kim S","Kim SY","Park JH","Joe S","Park JL","Yeo MK","Yang JO","Kim J","Lee D"],"additional_accession":[]},"is_claimable":false,"name":"Comparison of structural variant callers for massive whole-genome sequence data.","description":"<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 r","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-05-29T11:21:46.931Z","creation":"2025-04-05T23:17:29.345Z"},"accession":"S-EPMC10976732","cross_references":{"pubmed":["38549092"],"doi":["10.1186/s12864-024-10239-9"]}}