TY - JOUR T1 - NextSV: a computational pipeline for structural variation analysis from low-coverage long-read sequencing JF - bioRxiv DO - 10.1101/092544 SP - 092544 AU - Li Fang AU - Jiang Hu AU - Depeng Wang AU - Kai Wang Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/07/17/092544.abstract N2 - Structural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, users are often faced with issues such as what coverage is needed and how to optimally use the aligners and SV callers. Here, we developed NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive / stringent) for different analysis purpose. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 value) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5%~94.1% for deletions and 87.9%~93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset. Our results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data. ER -