Background GenoLab M is a recently developed next-generation sequencing (NGS) platform from GeneMind Biosciences. To establish the performance of GenoLab M, we present the first report to benchmark and compare the WGS and WES sequencing data of the GenoLab M sequencer to NovaSeq 6000 and NextSeq 550 platform in various types of analysis. For WGS, thirty-fold sequencing from Illumina NovaSeq platform and processed by GATK pipeline is currently considered as the golden standard. Thus this dataset is generated as a benchmark reference in this study. Results GenoLab M showed an average of 94.62% of Q20 percentage for base quality, while the NovaSeq was slightly higher at 96.97%. However, GenoLab M outperformed NovaSeq or NextSeq at a duplication rate, suggesting more usable data after deduplication. For WGS short variant calling, GenoLab M showed significant accuracy improvement over the same depth dataset from NovaSeq, and reached similar accuracy to NovaSeq 33X dataset with 22x depth. For 100X WES, the F-score and Precision in GenoLab M were higher than NovaSeq or NextSeq, especially for InDel calling. Conclusions GenoLab M is a promising NGS platform for high-performance WGS and WES applications. For WGS, 22X depth in the GenoLab M sequencing platform offers a cost-effective alternative to the current mainstream 33X depth on Illumina.

Background: GenoLab M is a recently developed next-generation sequencing (NGS) platform from GeneMind Biosciences. To establish the performance of GenoLab M, we present the first report to benchmark and compare the WGS and WES sequencing data of the GenoLab M sequencer to NovaSeq 6000 and NextSeq 550 platform in various types of analysis. 30-fold sequencing from Illumina NovaSeq platform and processed by GATK pipeline is currently considered as the golden standard of WGS. Thus this dataset is generated as a benchmark reference in this study.Results: GenoLab M showed an average of 94.62% of Q20 percentage for base quality, while the NovaSeq was slightly higher at 96.97%. However, GenoLab M outperformed NovaSeq or NextSeq at a duplication rate, suggesting more usable data after deduplication. For WGS short variant calling, GenoLab M showed significant accuracy improvement over the same depth dataset from NovaSeq, and reached similar accuracy to NovaSeq 33X dataset with 22x depth. For 100X WES, the F-score and Precision in GenoLab M were higher than NovaSeq or NextSeq, especially for InDel calling.Conclusions: GenoLab M is a promising NGS platform for high-performance WGS and WES applications. For WGS, 22X depth in the GenoLab M sequencing platform offers a cost-effective alternative to the current mainstream 33X depth on Illumina.

Background 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 TSof 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. Results 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%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (~21 hr) 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 of tumor variant diagnosis. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an integration of multiple tools to improve variant calling sensitivity and accuracy for the cancer genome. Conclusions Illumina and GeneMind technologies can be used independently or together by public health laboratories performing tumor TS. FASTASeq 300 platform performs better regarding variant detection sensitivity under SNVer and VarScan 2 algorithms along with the shortest turnaround time. Our study provides a standardized target sequencing resource to benchmark new bioinformatics protocols and sequencing platforms.

Technological innovation and increased affordability have contributed to the widespread adoption of target genome sequencing (TS) technologies in scientific research and clinical diagnostic applications. Cancer research consortia showed increasing acceptance of TS techniques to define the mutation landscape of multiple cancer types. These studies have primarily utilized Illumina’s sequencing instruments. In this study, we performed target genome sequencing of Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using a new platform, GenoLab M, and compared it with NovaSeq 6000 and NextSeq 550 platforms. The data demonstrated that all three platforms showed high-quality scores, deep coverage, and high concordance for variant detection (94.8%). Besides, the GenoLab M platform displays high accuracy in tumor diagnosis of FFPE sample. Briefly, the GenoLab M platform shows comparable performances with NovaSeq 6000 and NextSeq 550, indicating its potential applicability in cancer TS at a lower cost.