TY - JOUR
T1 - SVvalidation
T2 - A long-read-based validation method for genomic structural variation
AU - Zheng, Yan
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2024 Zheng, Shang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/1
Y1 - 2024/1
N2 - Although various methods have been developed to detect structural variations (SVs) in genomic sequences, few are used to validate these results. Several commonly used SV callers produce many false positive SVs, and existing validation methods are not accurate enough. Therefore, a highly efficient and accurate validation method is essential. In response, we propose SVvalidation-a new method that uses long-read sequencing data for validating SVs with higher accuracy and efficiency. Compared to existing methods, SVvalidation performs better in validating SVs in repeat regions and can determine the homozygosity or heterozygosity of an SV. Additionally, SVvalidation offers the highest recall, precision, and F1-score (improving by 7-16%) across all datasets. Moreover, SVvalidation is suitable for different types of SVs. The program is available at https://github.com/ nwpuzhengyan/SVvalidation.
AB - Although various methods have been developed to detect structural variations (SVs) in genomic sequences, few are used to validate these results. Several commonly used SV callers produce many false positive SVs, and existing validation methods are not accurate enough. Therefore, a highly efficient and accurate validation method is essential. In response, we propose SVvalidation-a new method that uses long-read sequencing data for validating SVs with higher accuracy and efficiency. Compared to existing methods, SVvalidation performs better in validating SVs in repeat regions and can determine the homozygosity or heterozygosity of an SV. Additionally, SVvalidation offers the highest recall, precision, and F1-score (improving by 7-16%) across all datasets. Moreover, SVvalidation is suitable for different types of SVs. The program is available at https://github.com/ nwpuzhengyan/SVvalidation.
UR - http://www.scopus.com/inward/record.url?scp=85181631369&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0291741
DO - 10.1371/journal.pone.0291741
M3 - 文章
C2 - 38181020
AN - SCOPUS:85181631369
SN - 1932-6203
VL - 19
JO - PLoS ONE
JF - PLoS ONE
IS - 1 January
M1 - e0291741
ER -