AUTHOR=Luan Mei-Wei , Zhang Xiao-Ming , Zhu Zi-Bin , Chen Ying , Xie Shang-Qian 

TITLE=Evaluating Structural Variation Detection Tools for Long-Read Sequencing Datasets in Saccharomyces cerevisiae

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00159

DOI=10.3389/fgene.2020.00159

ISSN=1664-8021

ABSTRACT=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).