Is Your NGS Workflow Holding You Back?
Part 1: Rethinking library prep as a scalable workflow strategy to improve efficiency, data reliability, and performance in high-throughput labs.
Across antibody discovery, cell and gene therapy, metagenomics, synthetic biology, agrigenomics, and beyond, NGS has become the workhorse driving day-to-day decisions. The questions are more ambitious, sample counts keep climbing, and cost per gigabase keeps falling — yet for many labs, the rate-limiting step is no longer the sequencer. It’s everything upstream of it.
The need: Efficient, scalable workflows that keep pace with the questions scientists want to ask.
The seqWell solution: Simple, high-throughput library prep matched by deep multiplexing.
Library Prep Is a Workflow Decision, Not Just a Kit Choice
Library prep is often treated as a means to an end — pick a kit, follow the protocol, move on. But the workflow you adopt determines a lot more than the price on the invoice. It shapes:
- Throughput and turnaround time
- Reproducibility and data quality
- Hands-on burden and cost per sample
- How quickly new team members or sites can be brought online
What feels like minor procedural inconvenience at 8 samples becomes a serious operational obstacle at 384 — fewer experiments per quarter, more variance to debug, and questions that don’t get asked because the workflow makes answering them too expensive.
Enter seqWell – Simplify. Scale. Advance.
Simplicity: It Starts With a Transposase
seqWell library prep is built on transposase chemistry. In a single reaction, the enzyme simultaneously fragments DNA and attaches sequencing adapters – a process frequently referred to as tagmentation. Tagmentation inherently simplifies library preparation by eliminating the multi-step processes of fragmentation, end-repair, and adapter ligation that other methods rely on.
The downstream consequences of that single step are larger than they look. Fewer transfers means fewer points where pipetting error, contamination, or sample misidentification can enter the workflow. Fewer reagent additions means a smaller consumable footprint and shorter total run times. And a one-pot reaction is far easier to deploy on a liquid handler than a serial protocol with multiple intermediate cleanups.
Let’s look at where that simplicity pays off
Throughput, Quality, Productivity, Accessibility and Affordability.
Throughput: Scale That Doesn’t Break
Faster workflows are useful at any scale. But “fast at 24 samples” and “fast at 1,536 samples” are two different engineering problems, and only one of them significantly changes what your lab can do.
A fast workflow at small scale is helpful—but a fast workflow that scales across hundreds or even thousands of samples is transformative.
seqWell’s design choices target the second problem. Massive multiplexing, assay-ready plate formats, early sample pooling, and auto-normalization compound to reduce hands-on time per sample as plate counts grow. The result is a workflow that scales close to linearly: adding more samples doesn’t mean adding proportional FTE or weekend shifts.
Quality: Reproducibility Is a Workflow Property
Sequencing data is only as trustworthy as the libraries they come from. Every additional liquid transfer, every operator handoff, every plate-to-plate swap adds variance. At small scale, that variance is irritating. At scale, it surfaces as batch effects, failed runs, and re-prep cycles that quietly consumes your budget.
With seqWell streamlined workflows you can expect:
- More consistent yields and insert-size profiles across samples
- Lower CV across plates and operators
- Cleaner cross-site comparisons in multi-center studies
Data quality isn’t a downstream bonus — it’s an upstream design choice.
Productivity and Cost Efficiency: Hidden Inputs Add Up
In high-sample environments, small inefficiencies multiply quickly. A few extra minutes per sample, a few extra tips, a few extra QC checkpoints — they compound across plates and across the year.
A leaner workflow lets a lab process more samples, generate more data, and take on more projects without adding headcount or expanding consumable budgets.
seqWell workflow design drives:
- Lower labor and consumable cost per library
- More samples processed per FTE per week
- Higher useful data output per sequencing run
- Fewer operational pinch points
The seqWell result: Do more with less.
Explore the Hidden Savings Infographic
Accessibility: Lowering the Barrier to Entry
As NGS spreads into translational labs, core facilities, and cross-functional teams, not every group has years of library prep expertise on staff. Complex protocols create dependency on specialists and limit which sites or collaborators can participate.
A simpler workflow means a competent bench scientist can generate high-quality libraries without months of ramp-up. That changes who can use NGS, where it can be deployed, and how quickly new applications can stand up.
The Takeaway: Workflow Matters
Across every NGS application, the same pattern shows up: workflow design defines what’s feasible. Simplicity reduces friction. Scalability keeps that simplicity intact as sample numbers grow. Together, they change the math that can hold you back.
Simplicity AND Scalability: When these elements come together, the impact is significant.
- Run larger, more statistically powered experiments
- Compress timelines from sample to insight
- Improve data reliability and cross-site comparability
- Lower total cost of operation
- Ask questions that weren’t feasible before
Ready to Move Past Workflow as the Limiting Reagent?
Turn library prep into an advantage rather than a bottleneck.