抄録
Tandem repeat copy number variations (TR-CNVs), structural variations (SVs), and short indels have been responsible for many diseases and traits, but no tools exist to distinguish and detect these variants. In this study, we developed a computational tool, TRsv, to distinguish and detect TR-CNVs, SVs, and short indels using long reads. In evaluation with simulated and real datasets, TRsv outperformed existing tools for detection of TR-CNVs and indels and performed equally well for detection of SVs. We demonstrated genome-wide detection of TR-CNVs, including variants associated with gene expression, disease, and quantitative traits, using 160 long-read whole genome sequencing data and TRsv.
| 本文言語 | 英語 |
|---|---|
| 論文番号 | 246 |
| ジャーナル | Genome Biology |
| 巻 | 26 |
| 号 | 1 |
| DOI | |
| 出版ステータス | 出版済み - 12-2025 |
| 外部発表 | はい |
All Science Journal Classification (ASJC) codes
- 生態、進化、行動および分類学
- 遺伝学
- 細胞生物学
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