| Literature DB >> 33963826 |
Yadong Liu1, Tao Jiang1, Junhao Su1,2, Bo Liu1, Tianyi Zang1, Yadong Wang1.
Abstract
SUMMARY: Circular consensus sequencing (CCS) 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: SKSV is available from https://github.com/ydLiu-HIT/SKSV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Entities:
Year: 2021 PMID: 33963826 DOI: 10.1093/bioinformatics/btab341
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937