CD Genomics performs high-throughput Illumina short-read sequencing on NovaSeq X and NovaSeq X Plus for research projects that need reliable PE150 data at scale (RUO). In plain terms, NovaSeq X is the single-flow-cell workhorse for high output in a straightforward run setup, while NovaSeq X Plus adds a dual-flow-cell configuration—most valuable when you want the highest single-run capacity with a 25B high-density flow cell.
Quick takeaways
Built for data-intensive work: large WGS cohorts, deep RNA-seq, exomes at scale, and more
Flow cell options to match project size: 1.5B / 10B / 25B
Common configuration: PE150 (2×150 bp) (other read lengths can be planned as needed)
Optional secondary analysis (e.g., DRAGEN, upon request) can help shorten the path from run completion to analysis-ready outputs
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Table of Contents
01 Who This Platform Is For (and What It Solves)
If you're here, you probably don't need a reminder of what Illumina sequencing is—you need an answer to a more practical question: will this platform make my project easier to finish?
NovaSeq X series platforms are a good fit when you're dealing with one (or more) of these realities:
Your study is cohort-scale.
Once sample counts grow, the pain often shifts from "data generation" to "batching, consistency, and scheduling." You want fewer run boundaries, fewer "this batch looks different" headaches, and a plan that doesn't collapse when you add another 200 samples.
Your assay is read-hungry.
Deep RNA-seq designs, high-coverage genomes, bisulfite sequencing, and mixed-library production runs all put pressure on total reads per week—not just a single lane here or there.
You care about predictable QC at high output.
At high throughput, the right question isn't "is Q30 good?" but "is QC consistent across batches, and do downstream metrics behave the way we expect for this library type?"
If you're still mapping your project to an Illumina strategy, start from a broad overview of high-throughput sequencing and applications here: Next Generation Sequencing.
02 NovaSeq X vs NovaSeq X Plus: Which One Fits Your Scale?
Most teams don't choose between X and X Plus because of read length. They choose based on how they want to batch the study.
NovaSeq X (single flow cell)
NovaSeq X is the simpler setup: one flow cell per run. It's a strong choice when you want high output but your project planning doesn't require dual-flow-cell capacity. If you're running consistent, repeatable batches—and you want run planning to stay clean—NovaSeq X often hits the sweet spot.
NovaSeq X Plus (dual flow cells)
NovaSeq X Plus supports two flow cells per run. That matters when you're pushing into production-style throughput: large cohorts, repeated weekly batches, or situations where "one more run" becomes the difference between hitting a milestone or slipping it. With the 25B flow cell, X Plus is the configuration most people mean when they talk about "max output per run."
A quick way to decide:
If your study plan keeps turning into "we'll split it across many runs," X Plus is usually worth a serious look. If your plan already fits comfortably into a steady run cadence, NovaSeq X can be the simpler, cleaner option.
03 Choosing a Flow Cell (1.5B vs 10B vs 25B)
Flow cell choice is where platform capability turns into a real run plan. You're not picking "better vs worse"—you're picking scale and flexibility.
A practical selector
Flow cell
Best for
Why it's chosen in real projects
1.5B
Pilot runs, smaller batches, method development
Flexible planning when you don't need maximum output
10B
Mid-to-large studies
Balanced option: strong throughput with adaptable batching
25B
Cohort-scale production and deep sequencing
Highest density/output; ideal when total data is the constraint
When 25B is the obvious choice
You'll feel the benefit of 25B when your study is limited by total reads or by how many runs you can reasonably schedule. It's especially common for:
Large WGS cohorts (e.g., 30× planning)
Deep transcriptome designs where read depth per sample is non-negotiable
Mixed-library production runs where you want fewer run boundaries across sample groups
If you already have libraries prepared and want sequencing only, the most relevant entry point is Pre-made Library Sequencing.
04 Core Specs (PE150 as the Default)
Most high-throughput study designs standardize around PE150 (2×150 bp) because it's broadly compatible with common library types and downstream pipelines.
Here's the spec framing that's actually useful when planning:
Read mode: PE150 is the most common setup for WGS, RNA-seq, WES, and many production runs
Output: scales with flow cell type (1.5B/10B/25B) and whether you run single vs dual flow cells
Run time: depends on configuration and read length; for planning, think in a wide window rather than a single fixed number
Run planning: NovaSeq X Plus (dual flow cells) is designed to make very large outputs more "one-run predictable" when the project is big enough
If you're comparing platforms, it helps to separate "spec sheet maximums" from "what you'll plan around." Spec maximums set the ceiling; your library complexity, pooling strategy, and target depth decide what you actually schedule.
05 What NovaSeq X Plus 25B Enables (in Plain Terms)
The 25B flow cell exists for one reason: more usable output per run, with a setup that's meant to stay stable across high-volume production work.
In practice, the upgrade you notice most often is not a single metric—it's how the platform changes your planning:
You can often finish a cohort in fewer runs, which reduces run-to-run variability and administrative overhead.
Pooling and batching become easier to standardize because you're working with a larger per-run budget of reads.
For mixed library types, you can reserve enough headroom to avoid "this run is tight; we had to compromise" decisions.
You'll still evaluate QC the same way you would on any Illumina platform—Q30 is useful, but it's not the whole story. The point is that 25B gives you more room to design a run that doesn't feel fragile.
06 Recommended Study Setups (WGS 30× / 90×, Deep RNA-seq)
This is where platform pages should be honest: the "right" setup depends on your biology, your cohort size, and what you consider a successful endpoint. Below are patterns that translate well to NovaSeq X series planning.
WGS 30× (cohort-ready planning)
WGS 30× is usually less about "can we get the data?" and more about how cleanly we can scale it. If you have a cohort with many samples, you want a plan that keeps batch effects under control and minimizes run fragmentation.
NovaSeq X series is a strong fit when:
sample count is high,
you want PE150 with consistent QC across batches,
and you'd rather spend time interpreting results than constantly re-balancing run plans.
High-depth WGS shifts the bottleneck to total reads per sample. Planning becomes a trade-off among cohort size, depth, and the number of runs you're willing to manage. This is where higher per-run capacity (often with X Plus + 25B) can simplify scheduling—because you're less likely to end up with a plan that sprawls across many partial runs.
When teams choose high-depth designs, it's often because they're prioritizing sensitivity for specific research questions (RUO). The best approach is to start with your depth target, then map that to a run plan that avoids unnecessary run fragmentation.
Deep RNA-seq (read-hungry transcriptome designs)
Deep RNA-seq isn't one thing. Some studies need power across many samples; others need depth to support low-abundance signals, complex designs, or challenging sample types. NovaSeq X series can support both patterns, but your run plan should be explicit about what you're optimizing for: sample count or depth per sample.
Maximize consolidation to reduce run fragmentation and batch-to-batch variability
Keep major sample sets together while maintaining operational simplicity
Right-size runs by choosing flow cell type to match project scale
Decision point: when to consider switching
Too many partial runs, long production cycles, or heavy run fragmentation
You need higher per-run output without dual-flow-cell overhead
You prioritize familiarity/flexibility and don't require the newest throughput tier
Decision point: downstream readiness
Expect very large datasets—plan storage/compute and standardized pipelines
Large datasets—plan pipeline throughput and cohort-level reporting
Large but often more "standardized" data handling in existing pipelines
Decision point: what to confirm at kickoff
Pooling/batching plan, QC thresholds, and run boundaries for your assay
Pooling plan, usable reads expectation, and run cadence
Flow cell choice, depth targets, and scheduling for consistent coverage
Note: Use this table to choose a platform strategy; use the Performance Parameters/Core Specs sections for numeric details (RUO).
If you want the NovaSeq 6000 platform page for context, see NovaSeq 6000.
09 NovaSeq vs HiSeq X
NovaSeq is generally the better choice when you need higher throughput and more flexible run planning, while HiSeq X is mainly relevant for legacy WGS pipelines that are already established around that platform.
Throughput per run: NovaSeq typically supports higher output per run, which helps large cohorts finish in fewer runs; HiSeq X has a lower per-run ceiling by today's standards.
Run planning & batching: NovaSeq offers more flexibility for batching and scaling (useful when sample counts fluctuate); HiSeq X workflows are usually more rigid but stable once standardized.
Best-fit use case: NovaSeq fits modern, data-intensive projects (large WGS cohorts, deep sequencing, mixed high-volume runs); HiSeq X is primarily used when a lab keeps an existing, validated high-throughput WGS workflow.
Operational efficiency: NovaSeq platforms generally integrate newer workflow automation and analysis options that can simplify end-to-end processing; HiSeq X typically relies more on external, established pipelines.
Upgrade decision trigger: If your plan requires splitting cohorts across many runs or you're scaling up sample volume, NovaSeq is usually the more scalable path; if your current HiSeq X workflow already meets your needs, switching may not be necessary.
10 Data Quality: How to Read Q30, Coverage, and Duplicates
On high-throughput platforms, people love to argue about one number. The problem is that quality is multi-dimensional.
Q30 is helpful—but it's not enough
% bases ≥ Q30 is a solid run-level snapshot. It tells you whether the run quality is broadly healthy. It does not, by itself, tell you whether the data will behave the way you want in downstream analysis.
Coverage distribution and duplication often matter more than you expect
For WGS, you'll usually want to look at:
coverage uniformity (not just average depth)
duplication rate (especially in low-input or over-amplified libraries)
consistency across batches (if the study spans multiple runs)
For RNA-seq, quality often shows up as:
usable fragments after filtering,
mapping/assignment behavior,
and whether depth is balanced across sample groups (especially in differential designs)
What about "real-world" library performance across assays?
In practice, downstream behavior is usually driven more by library quality, pooling accuracy, and batch handling than by the platform label itself. When the same libraries are prepared consistently and run QC is healthy, downstream metrics often track closely across high-throughput short-read runs. Treat spec maximums as ceilings—plan around usable reads, coverage/duplication, and batch-to-batch consistency for your assay.
11 Project Support on NovaSeq X/X Plus
On NovaSeq X/X Plus projects, CD Genomics focuses on making the run plan predictable before the run starts. We help translate your study target (sample count plus depth/reads) into a clear choice of NovaSeq X vs X Plus and 1.5B/10B/25B flow cells, then validate library/index compatibility and pooling logic so batches stay consistent. Data are delivered as demultiplexed FASTQ files with a concise QC summary, and we can align the handoff to your downstream workflow—whether you want DRAGEN-enabled secondary analysis or prefer to run your own pipeline.
Sample Submission Guidelines
