Summary: Circular consensus sequencing reads are promising for the comprehensive detection of structural variants (SVs). However, alignment-based SV calling pipelines are computationally intensive due to the generation of complete read-alignments and its post-processing. Herein, we propose a SKeleton-based analysis toolkit for Structural Variation detection (SKSV). Benchmarks on real and simulated datasets demonstrate that SKSV has an order of magnitude of faster speed than state-of-the-art SV calling approaches; moreover, it achieves higher F1 scores for various types of SVs.
Availability And Implementation: SKSV is available from https://github.com/ydLiu-HIT/SKSV.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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| http://dx.doi.org/10.1093/bioinformatics/btab341 | DOI Listing |
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