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CC BY-NC-ND V4.0, Open Access
Journal article
Benchmarking strategies for CNV calling from whole genome bisulfite data in humans
Computational and Structural Biotechnology Journal, Vol.27, pp.912-919
2025
Abstract
It’s important to dissect the relationship between copy number variations (CNVs) and DNA methylation, because both greatly change the dosages of genes and are responsible for diverse human cancers. Although whole genome bisulfite sequencing (WGBS) informs CNVs and DNA methylation, no study has provided a systematic benchmark for detecting CNVs from WGBS data. Herein, based on simulated and real WGBS datasets of 84.62 billion reads, we undertook 714 CNV detections to comprehensively benchmark the performance of 35 strategies, 5 alignment algorithms (bismarkbt2, bsbolt, bsmap, bwameth, and walt) wrapping with 7 CNV detection applications (BreakDancer, cn.mops, CNVkit, CNVnator, DELLY, GASV and Pindel). The results highlighted a subset of strategies that accurately called CNVs depending on numbers, lengths, precision, recall, and F1 scores of CNV detections. We found that bwameth-DELLY and bwameth-BreakDancer were the best strategies for calling deletions, and walt-CNVnator and bismarkbt2-CNVnator were the best strategies for calling duplications. These works provided investigators with useful information to accurately explore CNVs from WGBS data in humans.
Details
- Title
- Benchmarking strategies for CNV calling from whole genome bisulfite data in humans
- Publication Details
- Computational and Structural Biotechnology Journal, Vol.27, pp.912-919
- Publisher
- Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology
- Number of pages
- 8
- Grant note
- National Key Research and Development Program of China: 2022YFF1000900 Guangdong Basic and Applied Basic Research Foundation: 2024B1515020112, 2024A1515012999 Science and Technology Program of Guangzhou: 2024B03J1305 earmarked fund for China Agriculture Research System: CARS-35 National Center of Technology Innovation: NCTIP-XDB14
This research was funded by National Key Research and Development Program of China (2022YFF1000900) , Guangdong Basic and Applied Basic Research Foundation (2024B1515020112 and 2024A1515012999) , Science and Technology Program of Guangzhou (2024B03J1305) , the earmarked fund for China Agriculture Research System (CARS-35) , and National Center of Technology Innovation for Pigs (NCTIP-XDB14) .
- Identifiers
- 991005751327607891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- Centre for Healthy Ageing
- Resource Type
- Journal article
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