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.
CITATION STYLE
Jiang, T., Liu, S., Cao, S., & Wang, Y. (2022). Structural Variant Detection from Long-Read Sequencing Data with cuteSV. In Methods in Molecular Biology (Vol. 2493, pp. 137–151). Humana Press Inc. https://doi.org/10.1007/978-1-0716-2293-3_9
Mendeley helps you to discover research relevant for your work.