Introduction
Ensuring quality in Next-Generation Sequencing (NGS) data is important so that you can trust what comes off the instrument and what happens downstream in the pipeline. That trust is earned through quality control (QC): a structured way to detect issues early and to ensure that results are reliable and reproducible, especially in regulated clinical contexts. Missteps during quality control can lead to wasted time, unreliable results, and downstream errors.
Automated tools and software are making this process faster and less prone to human oversight, but it’s not always clear which features are worth prioritizing. This article breaks down the key practices and tool capabilities that streamline NGS data quality control.
Importance of Quality Control in NGS Data Analysis
Quality control (QC) is a foundational step in next-generation sequencing (NGS) data analysis. Without it, the reliability and accuracy of your results are compromised, which can have significant consequences, especially in clinical or diagnostic applications. The quality control process ensures that your data is of high quality and suitable for downstream analyses, making it an essential component of any NGS workflow.
QC is for maintaining sample integrity and data throughout complex genomic workflows. NGS experiments often involve multiple preparation stages, such as DNA/RNA extraction, library preparation, and sequencing itself. Each of these steps introduces risks of contamination, degradation, or processing errors.
For example, poor sample handling during extraction can lead to sample degradation, while issues in library preparation might result in uneven sequencing coverage. Misconfigured bioinformatics workflows might cause missed variant calls or sequencing artifacts called as variants. Comprehensive QC checkpoints help you detect these issues early, allowing for corrective actions before they impact your final data:
- Catch technical failures early: run issues, chemistry problems, index hopping, and contamination.
- Protect sensitivity and precision by verifying that read quality, mapping performance and coverage support the assay’s intended use. False positives or false negatives can lead to incorrect diagnoses or treatment plans.
- Enable comparability over time and over different sequencers, kits, operators, and SOP changes.
Adhering to robust QC practices is required by regulatory frameworks such as the EU In Vitro Diagnostic Regulation (IVDR) as well as quality management standards like ISO 13485. These requirements are particularly important in regulated settings such as clinical genomics, where clear criteria for data quality, traceability, and reproducibility help ensure that results are reliable, auditable, and compliant.
Comprehensive QC processes, including the use of validated tools and standardized protocols, help you meet these strict requirements while improving the credibility of your research or clinical findings.
Challenges Associated with Manual Quality Assessment
Manual quality assessment in NGS workflows can become a bottleneck. One of the primary issues is its susceptibility to errors caused by subjective interpretation: Different analysts might interpret quality metrics differently, leading to inconsistencies in results. Additionally, workflows often vary between laboratories, and even within the same lab, steps can be executed differently depending on the operator. This variability introduces further uncertainty into the data quality control process, where inconsistencies can compromise the reliability of downstream analyses.
Another critical limitation is the time-intensive nature of manual quality assessment. NGS datasets can be massive. Manually inspecting and processing large numbers of samples requires considerable effort and time. Delays may make manual processes impractical for labs working under tight deadlines.
Standardization across different labs and sequencing platforms also remains a persistent challenge (Endrullat et al., 2016). Each lab might employ unique protocols, and sequencing instruments can generate data with platform-specific biases. This lack of uniformity makes it difficult to establish consistent quality benchmarks, further complicating the manual assessment process. Without standardized workflows, comparisons of results across projects or collaborations can become unreliable, limiting the reproducibility of findings.
Best Practices for NGS Data Quality Control
1. Define a QC plan tied to the assay’s intended use
Establishing clear quality metrics and thresholds for them is important for evaluating the integrity of NGS data. Without a well-defined quality SOP, it becomes difficult to consistently assess whether the sequencing output meets the standards required for reliable downstream analysis.
Define:
- Which metrics you will evaluate
- Pass/warn/fails thresholds for each
- What actions you will take for each failure
Key quality metrics should be quantified and monitored throughout the process.
- GC Content: The percentage of guanine (G) and cytosine (C) bases in the data affects sequencing performance. Deviations from expected GC content can indicate contamination or biases in the library preparation process.
- Base Quality: Quality scores, typically measured on the Phred scale, estimate the likelihood of incorrect base calls. High base quality is critical, as lower scores increase the probability of sequencing errors.
- Read Depth Coverage and Uniformity: Adequate sequencing coverage ensures that genomic regions are sufficiently represented. Low coverage can lead to missed variants, while uneven coverage might indicate biases in amplification or sequencing.
Using tools like omnomicsQ enables real-time monitoring of these metrics, providing immediate insights into data quality. These tools automate the evaluation process, flagging deviations early to allow prompt corrective actions. Continuously tracking key quality metrics such as GC content, quality scores, and coverage depth and uniformity reduces the risk of overlooking problematic data.
2. Perform QC at multiple layers (FASTQ, BAM, VCF)
A common pitfall is relying only on FASTQ level quality checks. The sequencing data can look good while alignment and coverage can be poor. The best practice is to perform quality checks at multiple levels and consider multiple variables in conjunction (Sprang et al., 2021). Examples of relevant QC metrics include:
FASTQ layer (raw reads)
- Per-base quality,
- %≥Q30
- GC content
- N content,
- Read length distribution
- Number of reads
BAM layer (aligned reads)
- Mapping rate,
- Properly paired %
- Insert size
- Duplicate rate
- Coverage depth
- Coverage uniformity
VCF layer (variant calls)
- Variant counts and type distribution
- Call quality and depth distribution
- Strand bias
- Ti/Tv ratio
- Hom/het ratio
3. Follow Best Practice Guidelines
Adhering to established guidelines further improves consistency and reliability. The joint recommendation from the Association for Molecular Pathology and the College of American Pathologists (Roy et al., 2018) provides standards for validating NGS bioinformatics pipelines, while American College of Medical Genetics and Genomics technical standard (Rehder et al., 2021) covers best practices for clinical NGS laboratory workflows. Together, these guidelines offer standardized protocols for data quality, ensuring reproducibility and compliance in a clinical setting.
Aligning your practices with these recommendations helps ensure that your data meets quality benchmarks, improving confidence in your results.
4. Preserve metadata and provenance and track QC trends over time
QC metrics become meaningful when they are interpreted in context, so it is essential to preserve metadata and provenance alongside every sample. Record details such as the instrument ID, kit and protocol version, as well as the bioinformatics pipeline version used and the specific QC threshold set applied. Once this information is captured consistently, you can move beyond one-off pass/fail checks and start tracking QC trends over time. Monitoring for drift in key metrics, kit-lot effects, lane or batch effects and changes tied to operators or SOP updates help you spot emerging issues early and maintain stable, reproducible performance.
5. Validate the assay and re-verify on a schedule and after changes
To ensure accurate and reliable next-generation sequencing (NGS) results, validate your assay and analysis pipeline using well-characterized reference samples matching intended clinical use and sample type and revalidate after any significant change to the workflow (Roy et al., 2018).
Reference materials give a ground truth baseline for sensitivity, precision and reproducibility, so you can detect result drift.
- Choose fit-for-purpose reference materials. Use controls that match your assay type and variant spectrum. Publicly characterized resources such as NIST Genome in a Bottle materials are commonly used for benchmarking germline calling and commercial controls are often used for somatic/oncology contexts.
- Define acceptance criteria up front. Document concordance to truth sets, establish minimum required coverage and uniformity on clinically relevant regions, expected VAF level of detection for somatic controls, duplicate rate limits, contamination threshold.
- Validate the whole end-to-end workflow. Include library prep, sequencing and bioinformatics. Many issues only surface at the BAM and/or VCF stage.
- Verify on a cadence, not just once. Run controls at a defined frequency, track metrics over time and detect trends and deviations. Detect drifts early rather than letting failures accumulate.
- Re-verify after any meaningful change. Treat change control as a trigger for verification. Common triggers include for example a new reagent or kit lot, updated protocol, instrument service, changing the flowcell type, pipeline or tool updates, parameter changes and database updates.
Done consistently, reference-sample validation turns QC from a checklist into an ongoing “heath check” of both the web lab and the bioinformatics pipeline, ensuring that performance stays stable.
6. Participate in External Quality Assessment
Even a well-controlled internal QC program can miss “blind spots” that only become obvious when your results are compared against peers. That’s where participating in proficiency testing (PT), also known as external quality assessment (EQA) programs such as those from EMQN (European Molecular Genetics Quality Network) and GenQA (Genomics Quality Assessment) is highly recommended. These programs support cross-laboratory standardization by benchmarking your results against those from other labs.
- Detects systematic discrepancies: EQA can reveal systematic issues, such as coverage gaps or variant-calling biases.
- Validates real-world performance: EQA samples challenge workflows and reduce confirmation bias.
- Aligns with industry expectations: Successful participation demonstrates that your processes are consistent with broader best practices, improving accreditation readiness and stakeholder trust.
Participating in EQA helps you identify discrepancies in your processes and ensures alignment with industry best practices, strengthening your confidence in data quality.
Core Functionalities of Automated NGS Quality Control Tools
Standalone tools are good at calculating metrics. Automated QC platforms go beyond that by operationalizing those metrics across a lab.
1. Centralized storage of QC data
Centralizing QC data means collecting QC metrics across all sequencing devices, assays, runs and samples into a single system where they can be searched, compared, and trended consistently. This enables a single source of truth for standardized QC metrics, enabling cross-run and cross-instrument comparability and provides a foundation for automation.
2. Configurable QC rules for flagging warnings and failures
Because “good quality” is context-dependent, automated QC tools let you define, document, and apply assay and application specific thresholds tailored by sample type, sequencing platform, and application requirements. Configurable QC rules make pass/warn/fail decisions consistent and auditable.
3. Data visualization, trend analysis and quality dashboards
Clear visualization turns QC from a collection of metrics into actionable insight. Quality dashboards provide at-a-glance views key quality metrics, highlighting pass/warn/fail status and enabling comparisons across kits, instruments and time, making it easier to spot systematic issues
4. Workflow integration
Seamlessly integrating quality control (QC) tools within the overall data analysis pipelines is crucial for maintaining an efficient and streamlined NGS workflow. This ensures that QC-verified data transitions directly into analytical processes without manual intervention or delay.
Automation enables continuous data transfer between systems, ensuring that clean, validated data feeds into downstream tools for tasks such as variant interpretation or clinical reporting. Integration improves efficiency, saves time, and supports compliance with regulatory requirements like ISO 13485 and IVDR, which demand traceable data handling.
Conclusion
Accurate NGS data analysis starts with uncompromising quality control. It’s both a technical challenge and a foundational necessity. Automation ensures efficiency and precision, reducing manual errors while streamlining workflows.
Making use of robust tools allows extracting meaningful insights instead of grappling with avoidable data issues. The future of genomic analysis depends on building reliability from the ground up—and quality control is where it all begins.
Euformatics is a leading provider of advanced solutions for NGS data quality control, validation, and interpretation. Tools like omnomicsQ, omnomicsV, and omnomicsNGS ensure accurate and efficient genomic workflows while adhering to industry standards. With the Genomics Hub price configurator, you can easily estimate the costs tailored to your laboratory’s specific needs, ensuring transparency and informed decision-making. Ready to optimize your genomic workflows? Book a demo today to see how Euformatics can elevate your NGS data quality control processes.
FAQ
What Is QC in NGS?
QC in NGS is the set of measurements and checks used to confirm that sequencing data meets defined quality standards, so that conclusions drawn from the data can be reliable and reproducible.
What Are the Three Levels of NGS Data Analysis?
- Primary Analysis: Processes raw data, including base calling and quality scoring.
- Secondary Analysis: Aligns data and identifies variants.
- Tertiary Analysis: Interprets results through functional analysis and visualization.
How Is the NGS Quality Score Calculated?
NGS quality scores (Phred scores) indicate base-call accuracy, calculated from the probability of P of an incorrect base call using the formula Q = -10 log10(P). High scores mean fewer errors.
What Are the Most Common NGS Data Quality Metrics and How Are They Interpreted?
Key metrics include Phred scores, GC content, duplication rates, and mapping quality. High scores and proper metrics ensure accurate alignment and reliable data. Automated QC tools flag issues and streamline workflows.
References
- Endrullat, C., Glökler, J., Franke, P., & Frohme, M. (2016). Standardization and quality management in next-generation sequencing. Applied & translational genomics, 10, 2–9. https://doi.org/10.1016/j.atg.2016.06.001
- Sprang, M., Krüger, M., Andrade-Navarro, M. A., & Fontaine, J. F. (2021). Statistical guidelines for quality control of next-generation sequencing techniques. Life science alliance, 4(11), e202101113. https://doi.org/10.26508/lsa.202101113
- Roy, S., Coldren, C., Karunamurthy, A., Kip, N. S., Klee, E. W., Lincoln, S. E., Leon, A., Pullambhatla, M., Temple-Smolkin, R. L., Voelkerding, K. V., Wang, C., & Carter, A. B. (2018). Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. The Journal of molecular diagnostics : JMD, 20(1), 4–27. https://doi.org/10.1016/j.jmoldx.2017.11.003
- Rehder, C., Bean, L. J. H., Bick, D., Chao, E., Chung, W., Das, S., O’Daniel, J., Rehm, H., Shashi, V., Vincent, L. M., & ACMG Laboratory Quality Assurance Committee (2021). Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics, 23(8), 1399–1415. https://doi.org/10.1038/s41436-021-01139-4
