NGS Sequencing Run QC Metrics
Run QC tells you whether a sequencing run and each sample can be reported. CLIA does not publish numeric NGS thresholds; your lab defines acceptance criteria during validation and documents them in SOPs.
SLC is an independent, nationwide clinical laboratory consulting network. We are not affiliated with, authorized by or endorsed by any sequencing platform maker, kit supplier or accrediting organization.
Base quality (Q30)
Q30 is the percentage of bases with a Phred score of 30 or higher, an estimated error rate of 1 in 1,000. Platforms provide manufacturer specifications; your validated minimum may differ.
Cluster density and pass filter
On cluster-based platforms, over- or under-loading lowers yield and quality. Percent passing filter reflects usable clusters. Track per run.
Yield and index balance
Total output and reads per sample; flag samples below the validated minimum.
Coverage depth and uniformity
Mean depth, percentage of targets at or above minimum depth, and uniformity metrics. Regions below minimum are reported as not covered or rescued with another method.
Duplicates, on-target rate, insert size
Useful library-quality indicators; trend them.
Controls
Positive control with known variants, negative/no-template control, and optional sample identity checks.
Documenting acceptance
Write criteria into the SOP, record values for every run and sample, document review and sign-off, and log corrective actions. Trend metrics over time to catch drift.
Run-level versus sample-level QC
Run-level metrics tell you whether the sequencing run as a whole performed: total output, quality score distribution, pass-filter or usable read rate, and control results. Sample-level metrics tell you whether each patient sample has enough good data to report: read count, coverage of required regions, duplication, on-target rate, contamination estimates and identity checks. A run can pass while individual samples fail, so review both.
Setting thresholds during validation
During validation, record each metric for every run and sample along with whether the final results were correct. Use that data to set minimums that separate reliable from unreliable data with margin, informed by the manufacturer's specifications and published professional guidance. Write the thresholds into the QC SOP with the rationale, and revisit them when you have more data or change the method.
Required regions and coverage gaps
For each test, list regions that must meet coverage to call the test complete, such as all coding exons of reported genes. When a required region falls below the minimum, the lab must either fill the gap with another method, repeat the sample, or report the region as not assessed. Reports should make gaps clear so clinicians are not falsely reassured.
Contamination and identity metrics
Estimate cross-sample contamination using allele balance or dedicated tools, monitor negative controls for reads, and compare sample genotypes with an independent identity test where used. Set action limits for each.
Trending and corrective action
Plot key metrics over time per instrument and reagent lot. Gradual declines in quality or rising duplicate rates often precede failures. Define what triggers investigation and document corrective actions such as instrument service, reagent lot rejection or retraining.
How SLC helps
SLC helps labs choose which metrics to monitor, analyze validation data to set lab-defined thresholds, write QC SOPs and review forms, and build trend reports for director review. Plans from $500.
Platform differences
Metric names and typical values differ between platforms. Cluster-based short-read systems report cluster density and pass-filter rates; semiconductor systems report chip loading and usable reads; long-read systems report read length distributions and per-read accuracy. Define metrics in platform terms in your SOP and avoid copying thresholds from another platform.
Controls in each run
A positive control with known variants checks the whole workflow; a negative or no-template control checks for contamination; and some labs add a sequencing-level control spiked into each run. Define frequency, expected results and what happens when a control fails, which usually means the affected samples cannot be reported.
QC review form
Run ID, instrument, date, reagent lots and pipeline version
Run-level metrics with pass or fail against SOP limits
Control results
Sample-level metrics and identity checks per sample
Coverage gaps and actions taken
Reviewer signature and date
Common QC findings
Inspectors frequently find thresholds that exist only in someone's head, QC reviews not documented before reporting, and failed controls without corrective action. Writing criteria down and signing every review prevents most of these.
Library-level metrics that predict run quality
Library size distribution, concentration accuracy and adapter dimer content strongly influence run metrics. Poor quantification leads to over- or under-loading. Track library QC alongside run metrics so you can see which upstream step caused a failed run.
Documenting deviations
When a run or sample misses a criterion but the director decides results can be reported with a comment, document the deviation, rationale, any limitation added to the report, and who approved it. Repeated deviations signal that the method or the threshold needs review.
Metric glossary
Q30: share of bases with a Phred quality score of 30 or above (estimated 1 error in 1,000 bases)
Pass filter: share of clusters or reads passing the instrument's quality filter
Mean depth: average number of reads covering each targeted base
Percent target at minimum depth: share of required bases meeting your coverage threshold
Uniformity: how evenly reads are distributed across targets
Duplication rate: share of reads that are PCR or optical duplicates
On-target rate: share of reads mapping to intended regions
Software: compliance tracking with myLabCompliance
Paper binders and spreadsheets break down once a lab runs multiple instruments, shifts and testing personnel. We recommend myLabCompliance, a web-based CLIA compliance management platform for technical consultants, lab directors, lab owners and quality teams. Its public site describes tools for personnel management, validation, quality control, equipment, document control, scheduling, analytics and audit-ready workflows.
QC logs and quality control records kept in one place
Personnel records and competency tracking
Validation records for new instruments and methods
Equipment records for maintenance and calibration
Document control for SOPs and policies
Scheduling for recurring tasks such as PT events and competency
More NGS resources from SLC
Frequently asked questions
What Q30 should a clinical run meet?
The lab sets its minimum during validation, informed by platform specifications.
Does CLIA set NGS coverage thresholds?
No; labs establish them during validation.
Sound Lab Consultants (SLC), PO Box 2135, Chandler, AZ 85244. A nationwide clinical laboratory consulting network. Plans from $500.