Many researchers find that the sequencing problems they run into are not really problems with the sequencing itself. More often, they trace back to earlier decisions about how an experiment was designed, which method was chosen, or what assumptions carried over from a previous workflow that no longer apply.
For labs evaluating long-read sequencing for the first time, or trying to get more out of a workflow they already have, the decisions that matter most tend to cluster around the same questions: Is this the right technology for what I am studying? Which sequencing approach fits my specific question? What does sample prep actually require? And how do I build something that scales? These decisions often determine whether a sequencing project goes smoothly or requires expensive troubleshooting, so it’s important to get them right the first time.
What those decisions make possible has also expanded this week, with SPRQ-Nx chemistry for the Revio system now shipping worldwide, bringing the cost of a HiFi human whole genome to approximately $300 or below at scale. As long-read sequencing becomes accessible to a wider range of labs and projects, getting these decisions right early has more impact than ever.
To provide support at these inflection points, we’ve put together a practical resource bundle covering four areas where getting things right early pays off most: understanding the technology, choosing the right approach for your research, planning the experiment at the sample prep level, and scaling workflows through automation.
Is long-read sequencing the right fit for my research?
Before anything else, it helps to have a grounded understanding of what HiFi sequencing actually does and what distinguishes it from other approaches. This first resource walks through the fundamentals: how accuracy and read length interact, what native methylation detection means in practice, and which applications are a natural fit for the technology versus which require additional consideration.
For researchers coming from short-read workflows, this is also where some of the most common misconceptions tend to surface. The data is richer per read, the variant detection is more comprehensive, and the analytical assumptions are different. Understanding that foundation makes every downstream decision easier and reduces the risk of designing an experiment around constraints that no longer apply.
Which sequencing method fits my research question?
Not every research question calls for the same approach. Whole genome sequencing, targeted panels, RNA sequencing, epigenetic profiling, and metagenomic assembly each involve different tradeoffs in coverage, throughput, and how you interpret the data.
The application decision tree is designed to help you navigate those tradeoffs based on your actual research goals rather than defaulting to familiarity. For core facilities supporting diverse user bases, it is also a useful reference when helping investigators determine which method fits a new project, without having to walk through the full landscape each time.
Sample prep decisions that determine data quality
Sample preparation is where the quality of your data is largely determined, well before sequencing begins. Library quality, DNA input mass, shearing consistency, and QC checkpoints all influence the data you get out, and finding out something was off at the wrong stage is expensive in both time and consumables.
The step-by-step checklist covers the equipment and materials needed at each stage of the HiFi workflow, from extraction and shearing through library prep and final QC. Having this mapped out ahead of time is what separates runs that proceed cleanly from ones that require troubleshooting.
How to scale HiFi sequencing for higher throughput
For core facilities and labs running larger sample volumes, workflow efficiency becomes its own design problem. Reproducibility across samples, hands-on time, and integration with existing infrastructure all factor into whether a platform is practically sustainable at scale.
The automation buyer’s guide covers how to evaluate automation options based on throughput needs, sample types, CapEx, and where in the workflow bottlenecks are most likely to occur. It is practical enough to support a real purchasing or planning conversation, not just a general overview of what automation can do.
How SPRQ-Nx raises the return on getting it right
The decisions outlined above matter regardless of which sequencing chemistry you are running, but the introduction of SPRQ-Nx on the Revio system makes the stakes feel more immediate. By enabling multi-use SMRT Cells, SPRQ-Nx reduces HiFi whole genome sequencing to approximately $345 per human genome and $300 or below at scale, a 30% cost reduction that puts long-read sequencing within reach for population-scale studies, large disease cohorts, and core facilities whose users are working within typical grant budgets.
And cost is only part of what has changed. SPRQ-Nx also brings expanded methylation detection, including a new 5hmC caller relevant to cancer and tissue research, updated yield improvements validated across more than 1,400 runs in beta testing, and AI-driven variant calling advances developed in collaboration with Google Research. Together, these make each run more information-rich without adding workflow complexity.
Getting more value out of each run still depends on the decisions that precede it. Choosing the wrong sequencing approach for the biology or underestimating the value of sample prep adds cost back in ways that are harder to account for but very visible in the results.
The resources in this bundle are designed to help avoid exactly that. Whether you are evaluating long-read sequencing for the first time or expanding what your existing workflow can do, having reliable guidance at each decision point is what makes the difference between data you can use and data you have to second-guess.