New study shows how higher accuracy reduces costs and enhances rare variant detection
Advances in sequencing technologies are redefining what’s possible in the field of clinical research. As cancer researchers seek to push levels of detection to increase success from challenging samples and enable minimal residual disease and early resistance emergence monitoring, the precision and reliability of sequencing data become critical.
Until recently, next generation sequencing platforms delivered results with Q30 accuracy (99.9%), with redundance from increased coverage depth and error correct methods enabling many applications. Fundamental improvements to NGS have led to the introduction of Q40 sequencing (99.99% accuracy) representing a significant leap in quality, but questions have been raised about the impact that increased raw accuracy has on variant detection.
A recent preprint study from Fudan University provides one of the most comprehensive explorations of Q40 sequencing for both DNA and RNA sequencing applications to date. Using the Element AVITI™ system to generate Q40 data, they demonstrated that, when compared to standard Q30 results, costs of sequencing could be reduced by enhanced detection of clinically relevant rare variants.
For translational and clinical oncology research, where sensitivity, precision, and cost-effectiveness are paramount, these results signal a major advance in how sequencing data can be used to detect low-frequency somatic mutations and track disease evolution.
Benchmarking across reference standards
To evaluate the impact of increased raw read accuracy on downstream analytical precision, the study used a diverse set of validated control samples. Germline variant calling precision was assessed using whole exome sequencing from the NIST RM 8398 standard, and Mendelian consistency was evaluated with the Quartet control set. The effects on RNA detection and quantitative expression analysis were measured using bulk RNA-seq data generated from well-characterized RNA controls, including MAQC samples and ERCC synthetic spike-in pools. Accuracy of somatic variant detection was analyzed using the HCC1295/BL mixed reference sample. For comparison with established Q30 performance benchmarks, the researchers sequenced the same samples on an AVITI and Illumina NovaSeq 6000. All datasets were downsampled as needed to achieve comparable coverage levels for a fair performance comparison.
AVITI delivers higher accuracy at lower depth
In their germline variant analysis, the authors observed a lower duplication rate in the AVITI datasets, a characteristic consistent with previously reported performance of Avidite Base Chemistry™. However, the duplication rate alone did not account for the improved results. The key findings emerged when comparing InDel and SNV accuracy across downsampled coverage levels from 10× to 120×: AVITI Q40 data consistently achieved accuracy comparable to Illumina Q30 data at only 66.6% of the relative coverage. The authors highlighted the practical implications of this increased efficiency, noting that reduced sequencing depth requirements translate into estimated per-sample cost savings of 30–50%, depending on the application.
Germline SNV and InDel calling performance (Q30 vs Q40)
While high accuracy in germline variant detection is essential for genetic disease research and population-scale studies, the authors noted that the most transformative impact of Q40 sequencing accuracy may be in oncology, particularly for detecting rare somatic variants. To evaluate this, they compared the performance of AVITI Q40 sequencing against Illumina Q30 sequencing using control samples designed to model low-frequency mutations. Consistent with their germline findings, the authors reported that AVITI required substantially lower sequencing coverage to achieve equivalent sensitivity and precision in rare variant detection. They also observed notable improvements in copy number variant (CNV) detection at reduced coverage levels.
Somatic CNV calling performance (Q30 vs Q40)
The future of tumor profiling and early cancer detection
The relevance of these findings is amplified by ongoing advances in liquid biopsy, which enables minimally invasive detection of tumor DNA. As oncology applications increasingly target variant allele frequencies at or below 0.1%, sequencing sensitivity requirements continue to grow. Current methods rely heavily on unique molecular identifiers (UMIs) to suppress sequencing noise and improve detection accuracy; however, achieving the molecular and alignment coverage required to reliably identify variants at frequencies near 1:1000 demands very high sequencing depths, driving up per-sample costs and limiting scalability. Higher base-level accuracy offers a compelling alternative path. With more accurate raw reads, highly confident consensus sequences can be generated with far fewer reads per UMI, reducing total sequencing requirements and overall costs. The authors suggest that improvements in sequencing accuracy may therefore be a key enabler for transitioning liquid biopsy applications from research settings into routine clinical testing.
Continued innovation in this area may further accelerate clinical research adoption. Element Biosciences recently extended its accuracy leadership with UltraQ™ chemistry, enabling Q50+ sequencing performance. Ongoing gains in sensitivity, scalability, and cost-efficiency will be critical to unlocking widespread use of ultra-high-accuracy sequencing in oncology and other precision medicine applications.