Presentation3_Systematic comparison of variant calling pipelines of target genome sequencing cross multiple next-generation sequencers.PDF

Targeted genomic sequencing (TS) greatly benefits precision oncology by rapidly detecting genetic variations with better accuracy and sensitivity owing to its high sequencing depth. Multiple sequencing platforms and variant calling tools are available for TS, making it excruciating for researchers to choose. Therefore, benchmarking study across different platforms and pipelines available for TS is imperative. In this study, we performed a TS of Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using four commercially available sequencers, and analyzed the output 50 datasets using five commonly-used bioinformatics pipelines. We systematically investigated the sequencing quality and variant detection sensitivity, expecting to provide optimal recommendations for future research. Four sequencing platforms returned highly concordant results in terms of base quality (Q20 > 94%), sequencing coverage (>97%) and depth (>2000×). Benchmarking revealed good concordance of variant calling across different platforms and pipelines, among which, FASTASeq 300 platform showed the highest sensitivity (100%) and precision (100%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (∼21 h) at the sequencing mode PE150. Through the intersection of 50 datasets generated in this study, we recommended a novel set of variant genes outside the truth set published by HD832, expecting to replenish HD832 for future research on tumor variant diagnosis. Besides, we applied these five tools to another panel (TargetSeq One) for Twist cfDNA Pan-cancer Reference Standard, comprehensive consideration of SNP and InDel sensitivity, SNVer and VarScan 2 performed best among them. Furthermore, SNVer and VarScan 2 also performed best for six cancer cell lines samples regarding SNP and InDel sensitivity. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an ...