A new integrated approach to long-read sequencing of archived tissue samples has been introduced through a collaboration between PacBio and Covaris, with the workflow set to be presented at the American Association for Cancer Research Annual Meeting. The jointly developed solution combines Covaris’ extraction capabilities with PacBio’s sequencing technologies to enable a seamless transition from sample preparation to data generation. Designed specifically for formalin-fixed, paraffin-embedded tissue, the system addresses longstanding technical barriers associated with degraded DNA, positioning the HiFi sequencing workflow as a practical tool for oncology research.
Formalin-fixed, paraffin-embedded samples remain one of the most widely available resources in clinical and cancer research, yet their use in long-read sequencing has historically been constrained by DNA fragmentation and chemical damage. The newly introduced workflow seeks to overcome these issues by integrating Adaptive Focused Acoustics-based extraction with advanced library preparation methods that assemble shorter DNA fragments into longer molecules. This enables researchers to obtain high-fidelity sequencing data from challenging archival material, significantly expanding the usability of existing biological repositories.
Performance data generated from studies involving brain, kidney, and uterine tumor samples highlights the workflow’s capabilities. Each sample yielded more than 100 million HiFi reads, with average read lengths ranging between 750 and 1,500 base pairs. The approach enabled detection of over 11,000 structural variants alongside more than 5 million small variants per sample, with around 60% successfully phased into haplotypes. In contrast, conventional short-read sequencing methods typically identify fewer structural variants and struggle to directly phase mutations, often relying on computational inference. This distinction underscores the potential of the HiFi sequencing workflow to deliver more comprehensive genomic insights, particularly in complex regions of the genome.
Company executives emphasized the broader implications of the collaboration, noting its ability to unlock previously inaccessible data from archived samples. By enabling consistent performance across different tissue types and varying DNA quality, the workflow supports detailed genomic profiling, including structural variation and somatic mutation analysis. The development also opens new opportunities for leveraging historical datasets in AI-driven research, offering a pathway to uncover biological insights that were previously beyond reach.
