PromethION DRS Throughput and Turnaround

At a glance:

• When Will We Get Results — The Planning Bottleneck
• Key takeaways
• PromethION DRS Throughput — Capacity Versus Usable Output
• Turnaround Time Breakdown — A Transparent End-to-End Timeline
• Plan Throughput by Question Type — Isoforms First
• Batching Strategy — Scale Without Losing Comparability
• Throughput Risk Register — What Reduces Output and How to Mitigate
• Setting Realistic Delivery SLAs — What You Can Promise
• FAQ — PromethION DRS Throughput and Turnaround
• Action — Plan Your Pilot Costs, Timelines, and Risks

When Will We Get Results — The Planning Bottleneck

Project managers don’t struggle with running a PromethION; they struggle with planning PromethION Direct RNA Sequencing (DRS)throughput and setting a realistic turnaround time. In direct RNA sequencing pilots, one failed input QC cycle can push the turnaround time by weeks if the plan lacks buffers. This guide shows how to translate PromethION DRS throughput into usable output and a defensible turnaround time for isoform discovery, with balanced assumptions and practical gates.

Key takeaways

• Capacity is not the same as usable output. Define usable reads and set filters aligned to isoform discovery.
• DRS lacks multiplexing; plan cohorts across flow cells and use bridging samples to maintain comparability.
• Balanced turnaround windows (intake → QC → prep → run → basecalling → delivery) keep stakeholders aligned.
• Insert explicit buffers at the QC gate, batching window, and rerun contingency to limit rework.
• Use mod-aware basecalling models that match chemistry; validate pipelines before scaling.
• Document decisions and QC gates to make reports audit-friendly.

PromethION DRS Throughput — Capacity Versus Usable Output

Raw capacity (reads or gigabases per flow cell) is a starting point, not the finish line. For isoform discovery, “usable output” means analysis-ready reads that meet your length and quality thresholds and survive alignment/QC filters. Oxford Nanopore reported that RNA004 “produces ～30 million reads per PromethION flow cell for a human transcriptomic sample,” reflecting chemistry and motor improvements; use it as a directional example, not a guarantee. See the vendor’s announcement in the latest RNA004 kit overview (2024).

Practical planning notes for isoform discovery:

• Define a usable-read threshold (for example, ≥500–1000 nt, with acceptable basecalled quality) and track the read length distribution (N50 is helpful).
• Expect some attrition through QC and filtering (e.g., 25–35% of total reads removed in stricter pipelines); tune to sample type and goals.
• Match the Dorado model to chemistry (e.g., RNA004-compatible models) to avoid downstream QC failures; see Dorado model documentation.
• Input integrity and purity largely determine the read length distribution. ONT’s Chemistry Technical Document outlines how contaminants impair performance.

For readers comparing isoform-focused planning with modification studies, a broader context on long-read epitranscriptomics is available in Long Read Sequencing for Epigenomics and Epitranscriptomics.

Turnaround Time Breakdown — A Transparent End-to-End Timeline

A predictable timeline keeps teams and stakeholders aligned. Use the sequence below as a reusable scaffold; adjust durations to your facility.

• Sample intake and metadata validation
• RNA QC gate (pass / conditional / reject)
• Library build and readiness
• Sequencing run window
• Data processing and QC reporting
• Final delivery package (FASTQ + QC + summary)

Planner-balanced windows for isoform discovery (examples, not guarantees):

• Intake and metadata: 0.5–1 business day
• RNA QC: 1–3 business days (includes potential re-extraction)
• Library preparation: ～140 minutes hands-on; allow one business day for scheduling (source: SQK-RNA004 protocol and RNA004-XL)
• Sequencing run: 24–72 hours depending on depth and flow cell performance; washing will not restore RNA-blocked pores (see Flow Cell Wash Kit know-how)
• Basecalling + primary QC: 4–24 hours depending on dataset size and compute (see ONT data analysis overview)
• Reporting and deliverables: 1–3 business days

Where delays usually occur:

• QC failures and re-extractions
• Batching constraints (no multiplexing) and run scheduling
• Downstream pipeline QC mismatches (models, filters)

Plan Throughput by Question Type — Isoforms First

This guide centers on isoform discovery/full-length transcripts. That focus drives three planning choices:

• Stricter usable-read definition: prioritize longer reads and higher quality to resolve isoforms.
• Heavier reliance on input integrity: degraded RNA shifts read length distributions downward.
• Cross-run comparability: use bridging samples across runs to normalize.

For contrast only:

• Modifications/epitranscriptomics: plan stricter controls and mod-aware basecalling; see Direct RNA Sequencing Methylation Detection for method boundaries.
• Expression quantification: typically more tolerant of shorter reads; normalization across runs becomes vital because DRS lacks multiplexing.

If input readiness is your main uncertainty, the generic NGS QC primer can help frame gates while a DRS-specific checklist is developed: Sample Quality Control for NGS. Note: a PromethION DRS input checklist is a content gap to be filled.

Batching Strategy — Scale Without Losing Comparability

Because SQK‑RNA004 currently does not support multiplexing, each PromethION RNA flow cell generally carries one sample. To scale without losing comparability:

• Use bridging samples: repeat one reference RNA across run blocks to benchmark performance and enable normalization.
• Maintain plate discipline: standardize extraction kits, operators, and handling times.
• Handle late arrivals with run blocks: isolate and bracket with bridging controls; document deviations in run logs.
• Consider calibration strands: ONT’s RNA Calibration Strand (RCS) can support QA in DRS workflows.

Throughput Risk Register — What Reduces Output and How to Mitigate

Below is a planner-friendly register. Treat each entry as a gate with clear actions.

• Early signal: Smeared electropherogram; low RIN.
  • Likely cause: RNA degradation.
  • Impact: Shorter read distribution; fewer full-length isoforms; longer turnaround.
  • Mitigation: Re-extract with RNase-free discipline; consider poly(A) enrichment; downgrade expected output and add buffer.
  • Stop rule: If repeat QC fails, halt and re-source RNA.
• Early signal: Low 260/230 or 260/280; poor pore counts.
  • Likely cause: Phenol/protein carryover; DNA contamination; inhibitors.
  • Impact: Reduced pore occupancy; lower output.
  • Mitigation: Additional cleanup; alternative extraction; confirm purity prior to prep. Guidance in Chemistry Technical Document.
  • Stop rule: If purity remains out of spec after cleanup, do not proceed.
• Early signal: Underpowered input mass.
  • Likely cause: Below 300 ng poly(A) RNA or <1 µg total RNA.
  • Impact: Low yield; more time to meet targets.
  • Mitigation: Enrich poly(A); concentrate; re-extract. Inputs summarized in SQK-RNA004 docs.
  • Stop rule: If minimum input cannot be met, reschedule rather than under-load.
• Early signal: Batch inconsistency.
  • Likely cause: Mixed matrices or protocols.
  • Impact: Cohort drift; potential reruns.
  • Mitigation: Standardize protocols; include bridging controls; document variances.
  • Stop rule: If drift exceeds acceptable QC bounds, quarantine affected samples and re-run with controls.
• Early signal: Pipeline QC failures.
  • Likely cause: Wrong basecalling model or filters.
  • Impact: Reprocessing time; possible reruns.
  • Mitigation: Verify chemistry-model compatibility (e.g., RNA004 → compatible Dorado model); dry-run subset first; see Dorado models.
  • Stop rule: If alignment/error metrics remain out-of-bounds after reprocessing, escalate to rerun contingency.

Planner concept inputs and outputs (example fields):

• Inputs: number of samples; isoform target; per-sample usable-read target; expected QC pass rate; batching window; contingency buffer.
• Outputs: estimated run blocks; estimated turnaround range.

Disclosure: CD Genomics is our product. In practice, their planning templates and consultations can be used to structure pilot throughput targets and buffers without changing your lab protocols.

Setting Realistic Delivery SLAs — What You Can Promise

SLA wording should reflect gates and buffers, not rigid promises. Examples:

• “Delivery is contingent on RNA QC pass. Conditional passes extend turnaround by the buffer window.”
• “We will schedule sequencing within the defined run window (24–72 h) once the library is ready. Rerun contingencies are pre-allocated but only triggered by QC failure.”
• “Final deliverables include FASTQ, QC summary, and a run report with decisions and gates logged.”

FAQ — PromethION DRS Throughput and Turnaround

• What is a realistic turnaround for a DRS pilot?
  Balanced pilots often complete in 1–3 weeks from intake to deliverables, assuming one run block and no re-extraction.
• What causes the biggest delays?
  Input QC failures, batching constraints (no multiplexing), and pipeline model mismatches.
• How should we batch samples to avoid cohort drift?
  Use bridging samples across run blocks, keep protocols and operators consistent, and document deviations.
• How much buffer should we add for re-QC or reruns?
  Add a buffer equal to at least one QC cycle (1–3 business days) plus a rerun block (24–72 h) for critical cohorts.
• Can low-quality or clinical RNA still be scheduled reliably?
  Yes, but use conditional gates and adjust targets; expect shorter read distributions and plan buffers.
• What deliverables should we expect at each milestone?
  Intake confirmation; QC gate result; library-ready note; run log; basecalling/QC summary; final FASTQ + report.

Action — Plan Your Pilot Costs, Timelines, and Risks

Move from rough estimates to a concrete pilot plan with explicit buffers, milestones, and KPIs. A dedicated “Plan DRS Pilot Costs, Timelines, and Risks” template is a content gap slated for production; in the meantime, use the timeline above and the throughput planner concept to avoid surprises.

If sample readiness is the main uncertainty, revisit the input gate with the generic QC primer: Sample Quality Control for NGS. If your success criteria depend on isoforms or modifications, review foundational context: Long Read Sequencing for Epigenomics and Epitranscriptomics and Direct RNA Sequencing Methylation Detection.

Author: CD Genomics Long‑Read Sequencing Team — CD Genomics’ long-read operations and bioinformatics group. Collective 15+ years’ experience running ONT PromethION DRS workflows, library preparation, and Dorado-enabled basecalling for isoform discovery projects. For inquiries and methodology questions, contact longseq@cd-genomics.com.

Poly(A) Tail Length Analysis

Full-Length Transcript Sequencing (Iso-Seq)

Nanopore Direct RNA Sequencing

Nanopore Full-Length cDNA Sequencing