Evaluating Structural Variation Detection Tools for Long-Read Sequencing Datasets in Saccharomyces cerevisiae - Luan, Mei-Wei; Zhang, Xiao-Ming; Zhu, Zi-Bin; Chen, Ying; Xie, Shang-Qian (Frontiers in Genetics, 2020)
Structural variation (SV) represents a major form of genetic variations that contribute to polymorphic variations, human diseases and phenotypes in many organisms. Long-read sequencing has been successfully used to identify novel and complex SVs. However, comparison of SV detection tools for long-read sequencing datasets have not been reported. Therefore, we developed an analysis workflow combined two alignment tools (NGMLR and minimap2) and five callers (Sniffles, Picky, smartie-sv, PBHoney and NanoSV) to evaluate the SV detection in 6 datasets of S. cerevisiae. The accuracy of SV regions was validated by re-aligning raw reads in diverse alignment tools, SV callers, experimental conditions and sequencing platforms. The results showed that SV detection between NGMLR and minimap2 was not significant when using a same caller. The PBHoney was with the highest average accuracy (89.04%) and Picky has the lowest average accuracy (35.85%). The accuracy of NanoSV, Sniffles and smartie-sv were 68.67%, 60.47% and 57.67%, respectively. In addition, smartie-sv and NanoSV detected the most and least number of SVs, and SVs detection from PacBio sequencing platform was significantly more than that from ONT (p-value = 0.000173).