Abstract
Structural Variation (SV) represents genomic rearrangements and is strongly associated with human health and disease. Recently, long-read sequencing technologies provide the opportunity to more comprehensive identification of SVs at an ever-high resolution. However, under the circumstance of high sequencing errors and the complexity of SVs, there remains lots of technical issues to be settled. Hence, we propose cuteSV, a sensitive, fast, and scalable alignment-based SV detection approach to complete comprehensive discovery of diverse SVs. The benchmarking results indicate cuteSV is suitable for large-scale genome project since its excellent SV yields and ultra-fast speed. Here, we explain the overall framework for providing a detailed outline for users to apply cuteSV correctly and comprehensively. More details are available at https://github.com/tjiangHIT/cuteSV.
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Jiang, T., Liu, S., Cao, S., Wang, Y. (2022). Structural Variant Detection from Long-Read Sequencing Data with cuteSV. In: Ng, C., Piscuoglio, S. (eds) Variant Calling. Methods in Molecular Biology, vol 2493. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2293-3_9
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DOI: https://doi.org/10.1007/978-1-0716-2293-3_9
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