Supporting data for "A recurrence based approach for validating structural variation using long-read sequencing technology"
Dataset type: Genomic, Software
Data released on July 06, 2017
Although numerous algorithms have been developed to identify structural variation (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. This is significant, as the accurate identification of structural variation is still an outstanding but important problem in genomics. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as visual inspection of each region.
Here we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long read sequencing data. We assessed the performance of VaPoR on SVs in both simulated and real genomes and report a high-fidelity rate for overall accuracy across different levels of sequence depths. We show that VaPoR can interrogate a much larger range of SVs while still matching existing methods in terms of false positive validations and providing additional features considering breakpoint precision and predicted genotype. We further show that VaPoR can run quickly and efficiency without requiring a large processing or assembly pipeline.
VaPoR provides a long read based validation approach for genomic SVs that requires relatively low read depth and computing resources and thus will provide utility with targeted or low-pass sequencing coverage for accurate SV assessment. The VaPoR Software is available at: https://github.com/mills-lab/vapor.
Read the peer-reviewed publication(s):
Zhao, X., Weber, A. M., & Mills, R. E. (2017). A recurrence-based approach for validating structural variation using long-read sequencing technology. GigaScience, 6(8). doi:10.1093/gigascience/gix061