Going with the flow: faster, more-flexible sequencing data analysis

DNA sequencing data analysis is the behind-the-scenes process that transforms this chaos into organized, usable information — turning those masses of digital puzzle pieces into insights. That includes capturing the raw signal; turning it into readable genetic code; analyzing, organizing and storing those files; and sharing the data so scientists can use it in their research.

With so many critical diseases like cancer still in need of effective treatments, that same urgency is true for researchers.

Speed is just one of several fundamental barriers that currently prevent researchers from achieving efficient and cost-effective sequencing workflow in genomic discovery.

“Genomics researchers are constantly needing to balance cost efficiency, flexibility and turnaround time," Scott explains. "In order to drive cost efficiency, researchers often maximize the number of samples they can get from a sequencing run, and that can often mean waiting until you have all of those samples and maximizing the amount of data generated.”

Once a sufficient number of samples are acquired, it can take one to two days just to generate the raw data. And, the entire run needs to be complete before downstream analysis can begin.

These flexible options show great promise in helping researchers with a broader range of data analysis and management needs. But what about that speed? 

Working with an early access partner, Roche demonstrated at European Human Genetics Conference (ESHG) in May 2025 that the technology is able to go from library preparation to identifying genetic variants for a single whole genome sample in under five hours¹ — a process that can often take one to two days.

"Within seconds of sequencing starting, we were doing downstream analysis," Scott explains.  

For researchers working on critical applications — such as breakthrough cancer research — this real-time data processing, combined with flexible batch sizes and data management features, enables the flow state that Scott envisions: A future where we spend less time focusing on how to generate and manage data without sacrificing data quality, allowing undivided focus to the results and insights it can deliver.

In a field where breakthroughs could transform millions of lives, achieving that flow state isn't just about novel approaches to sequencing and data analysis — it's a shift that can help unleash human potential to tackle the questions that matter most.

Removing barriers

Given the unique properties of SBX reads, the Roche team has developed a suite of permissive, free, SBX–optimised open source analysis tools (XOOS) to serve as the foundation upon which the SBX ecosystem will be built.

In addition to accessibility and transparency, Chen Zhao, Vice President of Computational Biology, Molecular Lab, and his team are continuously innovating to deliver high–performance, accurate and scalable tools: “Our goal is to empower the NGS community and lower the barrier of adoption for SBX technology.”

In parallel, Zhao’s team has been working closely with the Google DeepVariant team to adapt DeepVariant for SBX, achieving strong performance on Whole Genome germline variant analysis.