No two days look the same in a core genomics lab, but every morning begins with the same calculation: which samples need to run, which can wait, and how much capacity is available. Natasha, the lab’s operations lead, is barely through the morning sample queue when she sees a problem. A new set of urgent samples has come in, and there is little capacity left in the schedule.
Already weighing her options, she walks through to the instrument room and watches the high-throughput sequencer quietly humming away. It’s midway through a 48-hour cycle, processing yesterday’s urgent batch, while the other side of the sequencer sits frustratingly empty. Her team has spent months refining protocols, training staff, and tightening handoffs, but they’re still struggling to hit their targets. They need more flexible solutions to make any further progress improving turnaround times.
Core facilities can't afford to wait
Natasha’s experience is not unique. Core facilities routinely face trade-offs between utilization efficiency and operational flexibility. Scheduling and batching inefficiencies are structural constraints inherent to dual-sided sequencers; if labs have libraries prepared for a single flow cell, they must decide between initiating the run and sacrificing throughput or delaying the run to maximize capacity. As Shrikant Mane, PhD, executive director of the Yale Center for Genome Analysis, explains, “We generally alternate use between instruments to keep one instrument open in the event of a rapid whole genome case, even if that means delaying the start of a standard production run by a day or two.”
The impact of these compromises on efficiency and turnaround time increases with application diversity and evolving project demands. Modern genomics core facilities require greater flexibility to maintain responsiveness and adapt to changing customer expectations.
Current technological solutions are reducing these batching constraints, enabling labs to align operations more closely with project demand through continuous, adaptable workflows.
Solving the batching problem
“We were constantly running mental flow charts to decide whether to load a flow cell now or wait for the next one to fill up,” reflects Zong Ye Wu, PhD, US lab director at Novogene. The staggered start function on their Illumina NovaSeq™ X Plus System changed that by enabling each flow cell to be initiated independently, allowing staff to load a second run while the first is still active. As Wu explains, “With staggered start, we don’t have to make that trade-off anymore—we can load as samples are ready.”
Figure 1. The NovaSeq X Plus System enables continuous staggered starts, turning sequencing capacity into a rolling, on-demand pipeline instead of discrete full-run blocks
CREDIT: Illumina
Labs can initiate new flow cells on a rolling basis for days or weeks at a time, creating recurring load windows for more predictable turnaround times and optimal throughput despite day-to-day variability in sample load (Figure 1).
This flexibility can also increase instrument uptime. Alvaro Hernandez, PhD, director of DNA Services at the University of Illinois at Urbana-Champaign, states, “Staggered runs let us manage the instrument time more efficiently, so we’re able to load at least two additional flow cells per week on the same machine.”
While staggered start improves run efficiency, maintaining efficiency across the entire sequencing workflow also depends on addressing downstream friction points, including indexing errors, which can impact data quality and require time-consuming resolution.
Overcoming indexing challenges in multi-workflow labs
The hidden cost of sample sheet errors
Sequencing run time is only part of the overall turnaround. Correctly demultiplexing reads after a run completes depends on accurate sample sheet data. Any errors, such as missing index sequences, incorrect index assignments, and inconsistent formatting, can introduce delays. More complex errors, such as swapped i7/i5 indices, reverse-complemented barcodes, or incorrect index orientation, can leave many reads classified as undetermined or misassigned. These failures may not be immediately apparent and are often only detected during quality control checks.
In high-throughput production environments processing externally submitted libraries, these issues are especially prevalent due to varying indexing conventions. Resolution typically requires manual investigation, repeated demultiplexing efforts, and user communication, adding cost through rework, delayed reporting, and, in some cases, unrecoverable sample attribution. Instrument software advances, such as Illumina’s NovaSeq X Series upgrades, can detect and correct index errors during the run itself, reducing post-run intervention and improving data integrity.
How autodetect indices works, and what it recovers
Autodetect indices helps users quickly identify and address common errors introduced in sample sheets. By generating index information after index two is complete, autodetect identifies potential indexing errors before the run completes, exposing issues that would otherwise emerge during post-run demultiplexing. Users can choose to generate a sample sheet directly from the sequencing data, or alternatively, add missing or corrected samples to an existing sample sheet (Figure 2).
Figure 2. Users can choose from three autodetect modes based on their indexing strategy, enabling capabilities such as accurate sample detection, extended demultiplexing, and reverse complement error correction.
CREDIT: Illumina
Autodetection of indices ensures samples are demultiplexed correctly the first time, reducing manual sample-sheet corrections and preventing failed demultiplexing attempts (Figure 3).
Figure 3. Autodetect indices can resolve undetermined reads. The 15M “undetermined” reads are demultiplexed into additional barcode-identified FASTQ files.
CREDIT: Illumina
The demand for this feature emerged through discussions between Illumina and its customers. As Benton Davies, associate director of product management, NovaSeq X Series at Illumina, shares, “What we’ve learned from speaking with customers is one of the most common reasons for sequencing run delay or rework isn’t really the instrument, it’s the sample sheet and index errors when you have all of these different users and library types.” This insight helped shape the autodetect indices feature powered by DRAGEN™. “This feature automatically detects indexes actually present in the run and fixes demultiplexing upfront, meaning customers spend less time troubleshooting and calling around, trying to resolve sample sheet errors.”
While these capabilities are standalone technological advancements in the v1.4 software update to the NovaSeq X Series, they sit within a broader improvement roadmap, ensuring a single purchase benefits from years of future innovation.
An innovation roadmap
Labs invest significant time, budget, and resources into their sequencing platforms. To provide customers with greater confidence in that investment, alongside the new NovaSeq X Series v1.4 software update, Illumina has outlined an 18-month innovation roadmap that reinforces its long-term commitment to the NovaSeq X Series and the broader ecosystem it comprises (Figure 4). As Davies summarizes, “The NovaSeq X Series v1.4 software shifts sequencing from labs optimizing around the instrument to the instrument adapting to how labs want to actually run their workflows.”
Figure 4. The 18-month NovaSeq X Series innovation roadmap ensures continuous return on investment.
CREDIT: Illumina
Several upcoming and recently introduced capabilities expand what labs can accomplish on the platform. The 1.5B 600-cycle kit supports longer-read applications, like metagenomics by delivering higher usable yield, improved base quality, deeper coverage, and more complete de novo assemblies.
Early user observations also point to continued performance gains. Hernandez reports, “The reason they call it the 1.5B is because each lane is supposed to give around 750 million reads, but what we consistently see is closer to 1 billion reads per lane. So that’s about 30 percent higher output than what Illumina advertises.” He adds, “We see the same trend with the 10B and the 25B flow cells too—we consistently get at least 10–15 percent higher output.”
TruPath™ Genome, currently exclusive to the NovaSeq X Series, brings long-range genomic insights to short-read sequencing through an ultra-simple workflow, enabling access to challenging genomic regions, structural variants, and phased variant calls across the human genome without the complexity of long-read platforms.
Planned updates continue that trajectory. Throughout 2027 updates are designed to support high-sensitivity applications and large, population-level genomics studies. Together, these advances position the NovaSeq X Series as an evolving platform for labs managing demanding whole-genome and multiomics projects.
Conclusion
The NovaSeq X Series is more than a sequencer—it’s a gateway to a continuously evolving ecosystem. Alongside the recent v1.4 software update, Illumina’s roadmap reflects a commitment to incorporating customer feedback and real-world use cases, reducing operational friction while preparing labs for emerging applications. For core labs, academic medical centers, and commercial services, the NovaSeq X Series is designed for diverse workflows. As advances in genomics continue to expand what laboratories can ask and achieve, adaptable technologies will help turn tomorrow’s possibilities into routine practice.
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