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

1. 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
2. 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
3. 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
4. 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

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Analysing genetic variants in cancer tissue, or tumor profiling, is the cornerstone of precision oncology. It is based on the fact that mutations have a critical role in driving a specific patient’s cancer. The analysis allows clinicians to perform more accurate diagnoses, to apply better targeted therapies, and to provide better prognostics.

Tumour diagnostics based on next-generation sequencing (NGS) is increasingly expanding its scope and application within oncology with the aim of enhancing the efficacy of precision medicine for patients with cancer. The use of NGS in oncology differs from its use in constitutional genetics in being focused on treatment actionability. Action refers here to selection of drug treatments, patient enrolment in clinical trials and promotion of drug development (Mosele et al. 2024).

Sequencing of patient tumor samples generates vast volumes NGS data. Without a structured framework for interpretation and reporting, these data remain difficult to translate into meaningful clinical action. In cancer diagnostics and disease monitoring, the challenge is even greater: results must inform diagnosis, prognosis, therapeutic choice, and sometimes resistance monitoring, often under time pressure.

Somatic NGS reporting requires the integration of technical performance metrics, bioinformatic processing, biological interpretation, and clinical relevance into a concise yet comprehensive document. A well-designed report supports variant interpretation, transparent communication with oncologists and pathologists, and traceability for regulatory and quality purposes.

This article outlines the fundamental elements of a somatic NGS report, from raw data generation through variant classification and clinical interpretation. While no single universal guideline defines the exact structure of a somatic NGS report, multiple professional standards provide clear recommendations on report content. These include the AMP/ASCO/CAP standards, CAP accreditation checklists, ISO 15189 requirements, and national best-practice guidance. Together, they define the essential elements needed to ensure clarity, reproducibility, and clinical usability of somatic variant reports.

What are somatic tests in the context of cancer?

Somatic genetic tests aim to identify acquired genetic alterations that arise in cells during a person’s lifetime. These so called somatic alterations are not inherited and are typically confined to cancerous tissue.

In its simplest form, a somatic diagnostic test addresses a focused clinical question:

• Is there a molecular alteration that explains the observed phenotype?
• Does this tumour harbour an actionable mutation?
• Are there biomarkers predictive of therapy response or resistance?

For example, identifying activating EGFR mutations in lung adenocarcinoma directly informs the use of tyrosine kinase inhibitors. Similarly, detection of BRAF V600E in melanoma can determine eligibility for targeted therapy. The comprehensiveness of tumor profiling is a matter of both pragmatism and of costs: how much information is needed for supporting good actionability? An NGS test can be performed across tens, hundreds, or thousands of genes or even over a  whole-genome. More data is particularly relevant in metastatic cancers of unknown primary tumour, or refractory disease where standard treatments have failed.

Somatic NGS reports serve as the primary communication channel between the molecular diagnostic laboratory and the clinical team, translating genomic observations into clinically actionable knowledge.

Somatic NGS tests are not always diagnostic

Not all somatic NGS tests are strictly diagnostic. Some are profiling or screening assays, designed to identify molecular alterations without necessarily establishing a definitive diagnosis.

Examples include:

• Broad tumor profiling panels used for therapy matching
• Liquid biopsy assays (ctDNA detection) for minimal residual disease monitoring
• Research-oriented sequencing to identify eligibility for clinical trials
• Clonal hematopoietic (CHIP) mutation screening (Reed et al. Clin Cancer Res. 2023)

While the underlying sequencing technology may be identical, the intent of testing fundamentally affects reporting. Diagnostic reports emphasize validated clinical relevance, whereas screening or profiling reports may include exploratory findings, emerging biomarkers, or variants of uncertain significance with appropriate disclaimers.  Clear communication of test intent is therefore essential to avoid misinterpretation of results.

NGS as a measurement technique for somatic variation

NGS measures DNA sequences by repeatedly sampling short fragments from a heterogeneous mixture of molecules. In somatic testing, this heterogeneity is amplified by factors such as:

• Tumor purity and stromal contamination
• Subclonal populations
• Copy number variation and aneuploidy

Each sequencing read represents a probabilistic observation of a molecule drawn from this mixture. Variant allele frequency (VAF) therefore becomes a critical parameter, reflecting both biological and technical factors.

Somatic NGS reporting must address not only what variants were detected, but also with what confidence. Coverage depth, base quality, strand bias, and detection limits are essential metrics to contextualize findings and to explain the absence of expected alterations.

Tools like omnomicsQ can help laboratories systematically capture and present these quality metrics in reports. By integrating VAF, coverage statistics, and other technical parameters, omnomicsQ supports transparent reporting of variant confidence and assay limitations, ensuring that clinicians can interpret results accurately and reliably.

Tumour genomes and intratumoral complexity

Unlike the relatively stable germline genome, tumor genomes are highly dynamic. They accumulate point mutations, insertions and deletions, structural rearrangements, copy number changes, and chromosomal instability. Protein domains are functional and structural units of proteins. They are responsible for specific functions that contribute normal cellular differentiation, development, and cell interactions such as signaling cascades. Because of this essential role, many actionable variants occur in protein domains (Emerson and Chitluri. Database. 2021).

Tumours are rarely genetically uniform. Subclonal architectures mean that clinically relevant variants may be present in only a fraction of tumor cells, complicating detection and interpretation.

Certain genomic regions are inherently difficult to sequence due to GC content, repeats, or pseudogenes. In somatic reporting, these limitations must be explicitly stated, particularly when negative findings could influence clinical decisions.

The rapidly evolving knowledge landscape in oncology

Somatic variant interpretation depends heavily on continuously evolving biomedical knowledge. New therapeutic targets, resistance mechanisms, and biomarker-drug associations are reported at an unprecedented pace.

A variant classified as non-actionable today may become clinically relevant tomorrow. Conversely, early evidence may later be downgraded. Somatic NGS reports therefore represent a snapshot in time, tied to the databases, guidelines, and literature available at the moment of analysis. Many laboratories explicitly state that reinterpretation may be warranted as knowledge evolves.

What is “normal” in a tumour context?

In somatic testing, “normal” has multiple meanings. Variants are typically identified by comparison to:

• A human reference genome (e.g. GRCh38)
• Matched normal tissue from the same patient (when available)
• Population databases such as gnomAD to exclude common germline variants

Tumors, however, may carry alterations that are rare or absent in population databases but still biologically neutral. Conversely, some pathogenic driver mutations may appear at low frequency due to subclonality or technical limitations.

Distinguishing somatic from germline variants is a central challenge, particularly in tumor-only sequencing. Reports typically indicate if matched normal samples were analysed and describe the method used for germline variant filtering.

From tumour DNA to list of somatic variants

The path from tumor tissue acquisition to somatic variant list involves multiple critical steps:

• Sample acquisition and fixation (e.g. FFPE-related artifacts)
• DNA extraction and library preparation
• Sequencing and base calling
• Alignment to a reference genome
• Variant calling, filtering, and annotation

Somatic variant calling is inherently heuristic and probabilistic, particularly at low allele frequencies. Rigorous quality control is therefore essential to ensure both sensitivity and specificity. Variants are described using established standards, notably HGVS nomenclature.

Reports typically document key quality metrics such as, but not limited to:

• Read depth
• Variant allele frequency
• Coverage
• Tumor purity estimates
• Assay-specific limitations

Are all somatic variants clinically relevant?

Most detected somatic variants are passenger mutations without direct clinical consequence. Only a subset represents driver alterations with diagnostic, prognostic, or therapeutic relevance.

Two complementary concepts are central:

• Variant classification: assessing oncogenicity or biological relevance
• Variant prioritisation: ranking variants according to clinical importance in the specific tumour and patient context

A variant may be clearly oncogenic but clinically irrelevant for a given cancer type, while another may be weakly characterised but therapeutically actionable. For example, BRAF V600E is a canonical activating oncogenic mutation in both colorectal cancer and melanoma. However, its therapeutic relevance is highly tumour-type dependent. In colorectal cancer, single-agent BRAF or MEK inhibition is largely ineffective due to rapid EGFR-mediated feedback reactivation of the MAPK pathway, making BRAF V600E clinically irrelevant as a standalone target (Prahallad et al. Nature, 2012; Corcoran et al. Cancer Discovery. 2012). In contrast, in melanoma, BRAF V600E is highly actionable, with combined BRAF and MEK inhibition demonstrating substantial and durable clinical benefit, leading to regulatory approval of multiple BRAF/MEK inhibitor combinations (Long et al. New England Journal of Medicine. 2014; Dummer et al. NEJM. 2018).

Guidelines for asserting somatic variant significance

Professional guidelines support standardised somatic variant interpretation:

• AMP/ASCO/CAP guidelines for somatic variant interpretation in cancer, defining four tiers of clinical significance. Focuses on clinical utility in the specific tumor context.
  
• ClinGen/VICC frameworks for oncogenicity classification focus on oncogenicity and evidence curation, classify variants as oncogenic, likely oncogenic, uncertain, likely benign, or benign.Useful for biological interpretation even if therapeutic relevance is limited. Hosted on the ClinGen Cancer Variant Interpretation working group portal, which coordinates somatic variant curation: ClinGen Cancer Variant Interpretation Committee (CVI) page

These systems distinguish between strong clinical evidence, emerging evidence, unknown significance, and benign findings. Adherence to such frameworks improves consistency, transparency, and clinical trust.

From interpretation to the clinical NGS report

The recommendations outlined by Li et al. (2017) describe the essential elements that should be included in a high-quality somatic report. These include, but are not limited to:

• Patient and sample identifiers
• Test indication and assay description
• Concise summary of key findings
• Variant listings with HGVS nomenclature, transcript references, and clinical classification
• Variant allele fraction (VAF) and sequencing coverage
• Clinical interpretation, including potential diagnostic, prognostic, or therapeutic relevance
• Methodological limitations and appropriate disclaimers

Primary findings are often separated from secondary or incidental findings, each with their own interpretation. References to databases, guidelines, and software versions used should be clearly documented. Furthermore, interpretation should not be limited to positive findings Somatic reports should not focus solely on positive findings. Clinically relevant negative results should be reported in a disease-specific context, particularly for Tier I drug–cancer pairs, where the absence of a mutation directly influences treatment decisions. For example, documenting the absence of an EGFR mutation in lung cancer or a BRAF mutation in melanoma is critical for appropriate therapy selection.

Finally, while molecular laboratories provide interpretation, therapeutic decisions remain the responsibility of the treating clinician, often in the context of a multidisciplinary tumor board.

Somatic NGS report generation in omnomicsNGS

Somatic variant interpretation and report generation using omnomicsNGS consolidates interpreted molecular findings into a structured clinical document intended to support oncology decision-making. omnomicsNGS provides integrated support for both variant interpretation according to best practice guidelines and standardised report generation, ensuring that findings are classified, evidence-backed, and presented clearly for medical professionals. Each report is timestamped, indicating the date of sample receipt and report issuance, thereby defining the temporal context of the underlying biomedical knowledge and database versions used.

Figure 1: Example of report section on patient, orderer, and empiricist

The report starts with a test description, offering context about the sample and the methods employed. This is followed by an interpretation and recommendations section, where the molecular findings are summarised and placed in clinical context. When relevant, results from additional assays for example, PCR, IHC, FISH, or other non-NGS tests can also be included to provide a comprehensive overview.

Figure 2. Example of report section on test description and interpretation and recommendations

The summary table provides a snapshot of reported Tier I and Tier II variants, with details such as gene name, HGVS notation, transcript ID, VAF %, and sequencing depth.

Figure 3. Example Summary table of a somatic report

A list of evidence supporting Tier I and Tier II variants follows the summary table, sorted in descending trust level from the highest down to level 1.

A.

B.

Figure 4. Example of list of evidence supporting tier I (A), and tier II (B) variants.

omnomicsNGS provides a curated list of EMA and FDA approved drugs (targeted therapies) based on user-provided ICDO-3 morphology and topography codes. Additionally, it includes ongoing clinical trials for selected diseases (DOID), which can be incorporated into the report.

Figure 5. Example of list of ongoing clinical trials for the selected disease and EMA and FDA approved drugs for the selected ICDO-3 codes.

Relevant sample metadata for example results from MSI, HRD, and TMB analysis performed outside of omnomicsNGS can be included in the report to provide comprehensive context for each case.

Figure 6. Example of “Sample metadata” section from somatic report.

If quality control metrics are provided by omnomicsQ, they will be included in the report to ensure the reliability of the results.

Figure 7. Example of report section on quality control of NGS data

Finally, the report includes limitations of the test, variant types along with databases and their versions used for the variant analysis.

Figure 8. Example of report section on limitations of the test and applied data sources for the annotations

Reporting of structural variants and fusions follows the same format as described above, except that summary of CNV variants table contains “Protein-coding genes” and “Dosage-sensitive genes” and copy number (CN) value as indicated in input VCF file.

Figure 9. Example of a summary table for CNV variants

Conclusion

A well-structured somatic variant interpretation report is essential for translating complex NGS data into actionable clinical insights. From sample processing and sequencing to variant calling, annotation, and interpretation, each step contributes to the reliability and clarity of the final report. Incorporating standardised frameworks, quality control metrics, and evidence-backed classification ensures that reports are both clinically meaningful and compliant with professional guidelines. Tools like omnomicsQ  and omnomicsNGS streamline this process, supporting consistent, transparent, and traceable reporting. Ultimately, comprehensive somatic NGS reports support oncologists and multidisciplinary teams such as molecular tumor boards to make informed decisions, optimise patient care, and adapt to the rapidly evolving landscape of cancer genomics.

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Introduction

Genetic data generated through Next-Generation Sequencing (NGS) is highly sensitive and requires stringent security measures. Laboratories, research institutions, and biotech companies must protect this data from unauthorised access, cyber threats, and data breaches while ensuring compliance with regulatory frameworks. However, securing vast volumes of genomic data while maintaining operational efficiency and regulatory alignment presents significant challenges.

For organisations handling NGS data, ensuring confidentiality, integrity, and availability is a business and compliance imperative. This is where ISO/IEC 27001, the international standard for information security management systems (ISMS), plays a critical role. ISO 27001 provides a systematic approach to information security management, helping organisations implement structured policies, access controls, encryption methods, and risk management strategies. By adhering to ISO 27001, NGS facilities can strengthen data protection, enhance regulatory compliance, and mitigate cybersecurity risks in genomic research and diagnostics.

This article explores how ISO 27001 enhances the security and compliance of NGS data, ensuring its confidentiality, integrity, and availability in genomic analysis workflows.

What is ISO 27001?

ISO 27001 is an internationally recognised standard that outlines best practices for establishing, operating, and continually strengthening an Information Security Management System aligned with an organisation’s context and objectives. It defines a structured, risk-based approach for identifying, evaluating, and treating information security risks, ensuring the confidentiality, integrity, and availability of sensitive data.

The standard is designed to be universally applicable, regardless of an organisation’s size, type, or industry, making it highly adaptable to complex environments such as genomic research and diagnostics. In the context of NGS, where genetic data is both personally identifiable and biologically sensitive, ISO 27001 provides a consistent framework to mitigate risks such as unauthorised access, data breaches, and improper use of sequencing datasets through tailored controls and continuous improvement.

While ISO/IEC 27001 defines the requirements for establishing, operating, and improving an Information Security Management System (ISMS), ISO/IEC 27002:2022 provides the detailed guidance to meet those requirements. It describes a comprehensive set of information security, cybersecurity, and privacy protection controls that organisations can select and tailor based on their risk assessment.

Key ISO 27001 Measures for Protecting Genetic Data

• Risk assessment and control measures: Identifies potential security threats in NGS data processing and storage and applies mitigation controls.
• Access control: Restricts access to authorised personnel, minimising the risk of data leaks, manipulation, or unauthorised sharing.
• Data encryption: Protects genetic data at rest and in transit, ensuring security during storage, transmission, and cross-platform integration.
• Incident management: Establishes detection, reporting, and response mechanisms for handling cybersecurity threats, breaches, and unauthorised data access.
• Regulatory compliance: Aligns with GDPR, HIPAA, and IVDR, ensuring legal and ethical handling of sensitive biological information.

By implementing ISO 27001, laboratories, research institutions, and biotech companies handling genetic data can establish a structured, regulatory-compliant security framework that minimizes risk, enhances trust, and ensures data protection across genomic workflows.

Key Security Challenges in NGS Data

NGS generates vast amounts of highly sensitive genetic data, making secure storage, controlled access, and regulatory compliance essential. Protecting this data is critical due to its personally identifiable nature and potential for misuse. However, several security challenges complicate NGS data protection and management.

1. Large-Scale Data Storage and Transfer Risks

NGS generates datasets ranging from gigabytes to several terabytes, requiring secure storage infrastructure and efficient data transfer mechanisms. The key challenges include:

• Data Storage: Secure on-premise and cloud-based solutions must incorporate encryption, redundancy, and controlled access to prevent unauthorised access and data loss.
• Data Transfer: Moving large genomic datasets across networks poses risks of interception, data corruption, and regulatory non-compliance if security measures are inadequate.

2. Privacy Risks and Ethical Considerations

NGS data contains unique genetic markers that can be linked to individuals and their relatives. Unauthorised access or leaks can result in:

• Privacy Violations: Exposure to personal health information can lead to discrimination, insurance misuse, and employment risks.
• De-Identification Challenges: Unlike standard medical records, anonymising genomic data while preserving research value is complex and requires advanced techniques.
• Strict Access Controls: Implementing multi-factor authentication (MFA) and role-based access controls (RBAC) minimizes unauthorised access to genetic databases.

3. Compliance with Multi-Layered Regulations

Genetic data is governed by multiple regulatory frameworks, each focusing on different aspects of data security and patient safety:

• GDPR (Europe): Emphasises data protection and patient consent for genomic data processing.
• HIPAA (U.S.): Ensures the confidentiality and integrity of patient-related genomic data.
• IVDR (EU): Regulates the safety and reliability of diagnostic tools used in NGS-based testing.
• ISO 13485 Compliance: Ensures that NGS-related  IVD medical devices and software meet strict quality control standards, a prerequisite for IVDR certification.
• External Quality Assurance (EQA): Programs like EQA also known as Proficiency Testing (PT) run by the organisations EMQN and GenQA enhance cross-laboratory standardisation and ensure compliance with international standards.

4. Cybersecurity Threats and Data Breaches

NGS data is a high-value target for cyber attacks, increasing the risks of:

• Ransomware and Data Theft: Cybercriminals target genomic databases for financial gain or misuse.
• Malware Attacks: Unsecured laboratory IT infrastructure is vulnerable to phishing, malware, and unauthorised access attempts.
• Incident Response and Recovery: Organisations must establish incident detection, reporting, and mitigation plans to address security breaches proactively.

Addressing These Challenges

To mitigate these risks, NGS facilities, diagnostic labs, and research institutions must implement ISO 27001-aligned security measures, including:

1. Strong Access Control and Identity Management

ISO 27001 requires organisations to implement strict access controls based on the principle of least privilege. For genetic data, this means:

• Limiting access to sequencing data, raw reads, and variant files to authorised roles only
• Enforcing MFA for systems storing or processing NGS data
• Regularly reviewing and revoking access when roles change

This significantly reduces the risk of insider threats and unauthorised data exposure.

2. Encryption of Data at Rest and in Transit

Genetic data is valuable both in storage and during transmission. ISO 27001 emphasizes cryptographic controls to protect data throughout its lifecycle, including:

• Encryption of NGS datasets stored in databases, file systems, and cloud environments
• Secure transmission protocols when sharing data between labs, partners, or analysis pipelines
• Key management policies to ensure encryption keys are protected and rotated

Encryption helps ensure that even if data is intercepted or accessed improperly, it remains unusable.

3. Risk-Based Security Management

One of ISO 27001’s greatest strengths is its risk-based approach. Organisations must:

• Identify information assets such as raw sequencing data, annotated genomes, and metadata
• Assess risks related to data breaches, loss, or unauthorised modification
• Apply controls proportionate to the sensitivity and impact of genetic data exposure

This ensures that security efforts are focused where the risks are highest, rather than applying generic controls.

4. Compliance with Global Data Protection Regulations

ISO 27001 does not replace legal requirements, but it strongly supports compliance with regulations such as:

• GDPR (EU), by enforcing data minimisation, access control, and breach management
• HIPAA (USA), through safeguards for confidentiality and integrity of health-related genetic data
• Local and international research regulations, particularly for cross-border data transfers

Certification provides external validation that your organisation follows internationally accepted best practices.

5. Incident Response and Breach Preparedness

Genetic data breaches can have irreversible consequences. ISO 27001 mandates documented and tested incident response procedures, including:

• Clear roles and responsibilities during a security incident
• Rapid detection and containment of breaches
• Communication and reporting processes aligned with regulatory timelines

Being prepared reduces downtime, limits damage, and demonstrates accountability to stakeholders.

6. Secure Collaboration and Third-Party Management

NGS workflows often involve external partners, cloud providers, and bioinformatics vendors. ISO 27001 requires organisations to assess and manage third-party risks by:

• Defining security requirements in contracts
• Monitoring supplier compliance
• Ensuring genetic data shared externally remains protected

This is especially critical when data crosses organisational or geographic boundaries.

A robust security framework for NGS data combines end-to-end encryption for data at rest and in transit, strict access controls such as RBAC and MFA, and regular regulatory audits to maintain compliance with GDPR, HIPAA, and IVDR. The use of automated tools like  omnomicsV for validation and omnomicsNGS further strengthens security, data quality, and compliance. Together, these measures reduce the risk of breaches and privacy violations while ensuring data integrity, regulatory adherence, and responsible genomic research.

How ISO 27001 Secures NGS Data

1. Access Control 

Protecting NGS data requires a combination of digital and physical access controls to prevent unauthorised access, data misuse, equipment tampering, and environmental threats. ISO/IEC 27001 integrates these controls to safeguard genetic data wherever it is stored, processed, or accessed.

Digital access control is enforced through RBAC and MFA. RBAC limits user permissions to defined job responsibilities, ensuring that researchers, bioinformaticians, and system administrators only access data and functions necessary for their roles. MFA adds an additional layer of protection by requiring multiple forms of identity verification, reducing the risk of credential-based attacks.

MFA strengthens security by requiring users to verify their identity through multiple authentication factors. A typical setup combines:

• Something the user knows (password)
• Something the user has (security token, mobile authentication app)
• Something the user is (biometric verification, such as fingerprint or facial recognition)

This prevents unauthorised access, even if login credentials are compromised.

To further enhance security, audit logging mechanisms record all access attempts and modifications to genetic data. These logs track:

• Who accessed the data
• What actions were performed
• When these actions occurred

Physical access control protects facilities such as data centers, laboratories, and secure storage areas. Measures include controlled entry using biometric or RFID systems, surveillance and monitoring, and visitor management procedures to ensure only authorised personnel can access NGS facilities.

To support accountability and threat detection, audit logging and monitoring track access attempts and data-related activities, enabling the identification of anomalies or unauthorised behavior.

Environmental safeguards further protect NGS infrastructure through fire suppression systems, backup power supplies, and climate controls to maintain system availability and equipment integrity.

Together, these controls enforce the principle of least privilege, strengthen resilience against both digital and physical threats, and support compliance with ISO 27001 requirements for secure and regulated handling of genetic data.

2. Cryptography 

Cryptography plays a foundational role in securing NGS data under ISO 27001. Genetic information is highly sensitive, and unauthorised access, tampering, or leakage can have severe ethical, legal, and scientific consequences. To mitigate these risks, ISO 27001 mandates strong cryptographic controls to ensure data confidentiality and integrity in both storage (data at rest) and transmission (data in transit).

Encryption is the primary method used to protect NGS data. When stored, encryption converts raw genetic data into an unreadable format, which can only be decrypted with a secure cryptographic key. This ensures that even if an attacker gains physical or network access, the data remains inaccessible. During transmission, encryption protects data moving between sequencing platforms, bioinformatics pipelines, and cloud storage, preventing interception or eavesdropping.

Strong encryption standards such as AES-256 (Advanced Encryption Standard with a 256-bit key) provide robust protection and are widely adopted for securing genomic databases.

However, effective encryption also requires proper cryptographic key management to prevent unauthorised decryption. If encryption keys are mishandled, the security of genetic data is compromised. Secure key management involves:

• Storing encryption keys separately from encrypted data to prevent unauthorised access.
• Using Hardware Security Modules (HSMs) or Key Management Systems (KMS) for secure key generation, storage, and lifecycle management.
• Enforcing strict access controls to limit key usage to authorised personnel and systems.
• Regularly rotating encryption keys to minimize long-term exposure risks.

A well-implemented cryptographic strategy not only secures NGS data but also ensures compliance with international regulations, including GDPR, HIPAA, and IVDR. Genomic analysis platforms, such as omnomicsNGS, integrate strong encryption mechanisms to protect variant interpretation data while ensuring compliance with regulatory standards.

3. Secure Operations 

Ensuring the secure operation of systems processing NGS data is essential for protecting its integrity, confidentiality, and availability. ISO 27001 establishes operational controls to detect, mitigate, and prevent security threats, ensuring continuous system protection against vulnerabilities.

Regular security updates and patch management are critical for addressing software vulnerabilities in NGS pipelines, genomic databases, and bioinformatics tools. Unpatched systems are prone to exploitation, leading to unauthorised access or data corruption. To enhance security, organisations should:

• Implement automated patch deployment to reduce the risk of human error and delayed updates.
• Establish a structured patch management policy, including scheduled updates to mitigate newly discovered threats.
• Conduct pre-deployment testing to ensure that patches do not disrupt critical NGS workflows.

Intrusion detection and continuous monitoring are key to identifying unauthorised access attempts or suspicious activity within genomic data systems. Advanced security information and event management (SIEM) tools analyse network traffic, system logs, and access records in real time. Effective security monitoring should include the following:

• Real-time alerting mechanisms to notify security teams of potential breaches.
• Behavioral analytics to detect anomalous activities, such as unauthorised data transfers or unusual access patterns.
• Periodic log audits to identify security gaps and refine threat detection strategies.

5. Third-party Security 

Many organisations handling NGS data rely on third-party vendors and cloud service providers for data storage, processing, and variant interpretation. However, outsourcing these tasks introduces security risks that must be proactively managed to prevent data breaches and ensure regulatory compliance. ISO 27001 establishes guidelines for securing external partnerships and enforcing vendor accountability.

Before engaging a third-party provider, organisations should conduct comprehensive security assessments to ensure compliance with ISO 27001, GDPR, and  HIPAA. Evaluations should include:

• Data protection policies and access control mechanisms to verify the secure handling of genomic datasets.
• Encryption protocols are used to protect NGS data at rest and in transit.
• Incident response capabilities to assess how vendors handle security breaches and data recovery.
• Compliance with quality management, ensuring that vendors meet the necessary standards for medical and IVD device development.

To enforce continuous security, organisations should:

• Require vendors to maintain ISO 27001 certification and provide regular security compliance reports.
• Conduct periodic security audits to verify adherence to data protection standards.
• Evaluate storage and processing infrastructures for vulnerabilities and ensure strong encryption and access controls.

Legal agreements play a crucial role in ensuring accountability. Contracts should define strict data protection obligations, covering:

• Data ownership and access restrictions to prevent unauthorised sharing.
• Breach notification requirements, mandating that vendors report security incidents immediately.
• Penalties for non-compliance, ensuring that contractual obligations are enforced.

Tools such as omnomicsV for validation and omnomicsNGS for variant interpretation work with third-party service providers that operate in accordance with genomic security and quality assurance standards.

Compliance Aspects of ISO 27001 for Genetic Data

Ensuring compliance with data protection regulations is critical for organisations handling NGS data, as genetic information is both highly sensitive and subject to strict legal requirements. ISO 27001 provides a structured framework that enables organisations to align with global regulatory mandates, demonstrating their commitment to data security, privacy, and ethical handling of genomic data.

Genetic data is governed by multiple legal and ethical frameworks, each with a specific focus. ISO 27001’s risk-based approach helps organisations implement technical and procedural controls that support compliance with the following key regulations:

• General Data Protection Regulation (GDPR) – Enforces stringent safeguards for personal and genetic data within the European Union. Organisations handling genomic data must implement technical and organisational security measures to prevent unauthorised access and misuse. 
• Health Insurance Portability and Accountability Act (HIPAA) – Regulates the protection of patient health information (PHI) in the United States, including genetic data used in clinical diagnostics. ISO 27001 supports HIPAA compliance by establishing security controls that meet HIPAA’s administrative, physical, and technical safeguards for data confidentiality and integrity.
• In Vitro Diagnostic Regulation (IVDR) – Focuses on ensuring the safety and performance of diagnostic products, including those used in NGS-based testing. Compliance with ISO 13485, which governs medical and IVD device development, is a prerequisite for IVDR certification. Laboratories handling NGS data for clinical applications must integrate ISO 27001 security controls to protect patient data while ensuring regulatory adherence.
• Country-Specific Genetic Data Regulations – Many countries have national laws governing the collection, storage, and sharing of genetic data. ISO 27001 provides a flexible framework that allows organisations to adapt to evolving legal requirements while maintaining global security standards.

Achieving ISO 27001 certification serves as verifiable proof of regulatory compliance for organisations processing genetic data. An independent accredited certification body evaluates an organisation’s security policies, risk management practices, and data protection measures to confirm adherence to ISO 27001. By implementing ISO 27001, organisations handling NGS data can establish a robust security framework that meets regulatory obligations, ensures data integrity, and maintains ethical standards in genomic research and diagnostics.

Conclusion

Protecting NGS data requires both robust security measures and strict regulatory compliance. ISO 27001 provides a structured framework to achieve this, addressing access controls, encryption, operational security, physical protections, and third-party risk management. Its implementation not only mitigates risks but also ensures alignment with legal and ethical obligations. The increasing value and sensitivity of genetic data make security a long-term priority. A well-executed ISO 27001 strategy strengthens trust, safeguards data integrity, and supports continued scientific and clinical advancements.

Euformatics is a leading provider of genomic data analysis and quality management solutions, offering end-to-end tools that enhance NGS security and compliance. By integrating ISO 27001-aligned security measures, Euformatics ensures that genetic data remains protected while meeting regulatory requirements such as GDPR, HIPAA, and IVDR. To simplify cost estimation, Euformatics provides a transparent Genomics Hub price configurator, allowing laboratories to customize pricing based on their specific NGS validation, quality control, and analysis needs. Explore the pricing tool here.

Book a demo today to see how Euformatics can help secure and streamline your NGS data workflows.

FAQ

Does ISO 27001 Cover Data Protection?

Yes, ISO 27001 provides a framework for managing security risks and ensuring data confidentiality, integrity, and availability. While it doesn’t specifically address genetic data, it helps protect NGS data through access controls, encryption, and compliance with regulations like GDPR.

What Is the ISO 27001 Data Security Policy?

It defines how an organisation protects sensitive data, including NGS data, using encryption, access controls, and risk management. It ensures security, compliance, and protection against breaches.

What Is the Difference Between FedRAMP and ISO 27001?

FedRAMP is a United States federal government-wide compliance program that provides a standardised approach to security assessment, authorisation, and continuous monitoring for cloud products and services, while ISO 27001 is a global security standard applicable across industries. ISO 27001 is key for protecting NGS data through strong security controls and risk management.

What Is the ISO 27001 Standard for Information Security?

ISO 27001 is an international standard for managing information security. It protects NGS data through risk-based controls, encryption, and continuous monitoring, ensuring compliance and safeguarding genetic information.

How Does ISO 27001 Help Protect Genetic Data?

It secures genetic data by implementing access controls, encryption, and risk assessments. Organisations can protect NGS data from cyber threats and breaches while ensuring regulatory compliance and continuous security improvements.

References

• Clayton, Ellen Wright, Barbara J. Evans, James W. Hazel, and Mark A. Rothstein. “The law of genetic privacy: applications, implications, and limitations.” Journal of Law and the Biosciences 6, no. 1 (2019): 1-36.
• Martinez-Martin, Nicole, and David Magnus. “Privacy and ethical challenges in next-generation sequencing.” Expert review of precision medicine and drug development 4, no. 2 (2019): 95-104.

The post How ISO 27001 Enhances Security & Compliance for Genetic Data appeared first on Euformatics.

We are now introducing omnomicsNGS version 2.13.0, bringing full support for somatic variant classification using the ClinGen/VICC/CGC guidelines, enhanced CIViC annotations, expanded structural variant analysis and flexible reporting workflows. This release delivers powerful new capabilities along with many refinements based on customer feedback and has a broader VCF compatibility for structural variants.

1       Improvements to somatic variant interpretation

1.1    New ClinGen/CGC/VICC oncogenicity classification

Gain deeper insights into somatic variants with automated oncogenicity classification based on the latest consensus guidelines. The system evaluates key evidence categories, applies standardised criteria, and provides transparent classification results with full editability and history tracking.

1.2    Expanded CIViC annotations including functional and oncogenic annotations

CIViC annotations now include functional impact and oncogenicity assessments, offering richer context for variant interpretation. Linking to each CIViC evidence makes it easier to review clinical relevance, supporting more confident decision-making.

2       Support for Structural Variants (SVs) and Uniparental disomy (UPD)

2.1    gnomAD 4.1 frequencies for CNVs

Copy-number variants (CNVs) now include population frequency data from gnomAD 4.1, enabling more accurate interpretation and easier distinction between common and rare events

2.2    Improved handling of INV, CTX, and breakend-based events

The platform now processes complex structural variants: Breakend-based SVs including inversions (INV), insertions (INS) and translocations (CTX) are properly grouped, annotated, and displayed

2.3    Enhanced LOH and compound heterozygosity analysis

Loss-of-heterozygosity (LOH) and compound heterozygous variants are detected and summarized on SNP/Indel and SV workbenches.

2.4    UPD detection with clearer region identification

Uniparental disomy events are now automatically detected. UPD can be visualised on the sample page with an editable option for setting up thresholds for the number of homozygous variants.

3       Broader VCF compatibility for structural variants

This version adds support for importing in particular structural variants (CNV, fusions, breakends) of various kinds, from VCF files of a wider range of variant callers, reducing duplication errors and ensuring accurate representation in the workbench for reliable analyses.

3.1    Combined SNP + InDel + CNV

Import combined SNP/Indel, CNV, and fusion variants seamlessly. This affects among other import from Thermo Fisher / Ion Torrent Genexus pipelines.

3.2    Fusions

Support for various fusion format interpretations of the VCF standard. This affects among other Arriba secondary RNAseq analysis, Dragen, Delly, and Oncomine Comprehensive Assay files.

3.3    Breakend SVs, tandem repeats, and other complex variants

Structural variants with breakends, inversions, translocations, or tandem repeats are now supported.

4       Automation & Command-Line Interface (CLI) support

We have further worked on automation of data input via the omnomicsNGS API. This allows automation of not only VCF file input, but also additional metadata. This facilitates computational processing of among other but not only additional non-vcf files with MSI, TMB, HRD information or sex and family structures for trio or larger analysis.

This affects the seamless ingestion of DRAGEN Somatic v4.3 and TSO500 output directories in a single step, but also allows a more automated transfer of any sample from a sequencer or a secondary pipeline to omnomicsNGS.

This feature has been a requirement in the EU-financed PCP Instand-NGS4P project on integration and standardisation of NGS Workflows for personalised therapy.

5       Improvements to Workbench

1. AI flag-based filtering for faster and reliable detection of clinically relevant variant
2. Real-time variant counts displayed before and after filtering
3. Export filtered results directly to VCF for seamless downstream analysis
4. Optimized user interface is now faster when changing workbench pages
5. Structural variant display improvements

6       Improvements to Reporting

 This version brings significant upgrades to ACMG/AMP and AMP/ASCO/CAP PDF reports:

1. Updated styling, colors, and layout
2. A separate SV/CNV table for clear presentation
3. Addition of Quality Metric data (if available)
4. EMA & FDA approved drug lists (for somatic report)
5. Add ongoing and clinical trials with ability to filter based on country of recruitment and phase of the trial (for somatic report)
6. Editable gene descriptions from NCBI (for somatic report)
7. Language selection for PDF reports as an optional enhancement

7       Conclusion

Together, this release delivers faster, accurate, and reliable variant analysis and reporting, making it easier to turn data into actionable insights. If you have any questions, please contact us at support@euformatics.com.

The post New release: omnomicsNGS version 2.13.0 brings ClinGen-aligned somatic classification, enhanced CIViC annotations, structural variant analysis, and support for VCFs from more variant callers appeared first on Euformatics.

Clinical gene testing easily generates vast volumes of raw sequencing data. Without proper perspective of its utility and of a reporting frameworks, these measurements remain dumb and difficult to report in a compact and relevant way. Therefore, laboratories must consider how to convert the data into structured information that supports reproducible interpretation and transparent communication with patients, clinicians, and the clinical research community at large.

Reporting on NGS observations requires an organisation of technical, analytical, and interpretive details into a structured, possibly even standardised document that will inform diagnosis, prognosis, or therapeutic decision-making. Reporting also means knowing and stating limitations about what remains outside the capabilitie of the measurement technology. Reports that follow established either national or otherwise best practice guidelines and regulatory expectations reduce variability, ensure compliance, and safeguard patient outcomes.

Without pretending there is one framework that fits all needs, this article attempts to identify central elements of a good report of an NGS-based genetic test. It outlines elements pertaining to the DNA preparation, sequencing, bioinformatic pipeline, annotation and prioritisation components.

2      What genetic diagnostic tests?

In its simplest form a well targeted NGS-based genetic diagnostic test should answer a single straightforward medical question. The test is specific to a given condition and serves to confirm a diagnosis and trigger some actions. Is the decreased production of hemoglobin resulting in anemia or a chronic heart problem an inherited condition for the patient or is it the consequence of some other physiological problem? Will a particular drug be suitable for treating a patient or not is another typical test case where a genetic test might provide an answer.

Making the diagnosis of Alport syndrome is critical because effective inexpensive treatment with renin-angiotensin-aldosterone system (RAAS) blockade delays the development of kidney failure. A genetic test for mutations in a few specific genes can be highly informative (Gross et al. 2020, DOI: 10.1016/j.kint.2019.12.015). The test report then serves as the communication medium between the sequencing laboratory and the clinician or researcher who applies the results in a medical or scientific context.

Now the human genome has potentially multiple versions of about 20’000 genes, and therefore, in more complicated cases, comprehensive genetic testing over multiple, even thousands of genes, or even in regulatory regions outside of genes can be the only way to get a sufficient – albeit seldom complete – understanding of the underlying factors of a condition. We are here talking typically about either rare diseases where very little of the genetics is known, or about situations where a faulty interplay between multiple genes can lead to the medical condition of a patient, for example cancer.

And this is without considering that the genetic component can be insufficient on its own in explaining a medical condition, the environmental component playing its own part in the whole. Making the diagnosos of ASD (autism spectrum disorder) is important for gaining a better understanding of an individual’s strengths and challenges, and for accessing needed support and services. For children, early diagnosis allows for timely intervention, which can lead to improved developmental outcomes. For adults, it can provide self-acceptance and access to accommodations in education and employment, and help them connect with support communities. However, as ASD is a multifactorial neurodevelopmental condition there is no single test for autism, even less so a genetic test, and much effort is dedicated to the understanding of it (Salenius et al. 2024, DOI: 10.1186/s12888-024-06392-w ). In analogous cases, the genetic test report can at most be a part of an otherwise more comprehensive report.

Reporting on such different situations will obviously require some sections of the report to be adapted to the purpose of the test and case at hand.

3      Genetic screening tests are not diagnostic

There are of course other situations where the analysis of a part of the genome can be useful. Forensic testing in the sense of identifying someone is a prime example, but there are also other situations where a genetic test carries a considerable amount of value. While a diagnostic test identifies a specific genetic conditions in an individual, often performed when some symptoms are present, a genetic screening test identifies individuals in a population. Generally the aim of a screening is to identify among symptomless individuals those who may be at risk for a specific genetic condition. A specific type of screening is the testing of two persons (or donors) in view of identifying carriers of risk factors that could be inherited by a descendant.

Due to this fundamental difference in purpose between diagnostic and screening tests, reporting on the latter is quite different from reporting on genetic diagnostic tests, although the underlying methodology is identical.

4      What about NGS as a measurement technique?

Next generation sequencing as a measurement principle targeting DNA implies to repeatedly trying to read short pieces from a mixture of millions of molecules originating from the region of interest defined by the purpose of the test. Each piece of the region of interest of the genome is potentially slightly different, and so is the reading of each piece. Therefore, one can say that next generation sequencing is about gathering sufficient data about multiple fragments of DNA representing the region of interest. The process of so to say reading every piece in the mixture is not failproof, and the method therefore aims at providing a large amount of slightly different readings of slightly different molecules. The NGS method is thus based on a stochastic process integrating data provided from a collection of random variables, representing this double diversity in a probabilistic way. A key question therefore boils down to assessing not only the the genetic test raw data but also the probability of it being correct, something to document in the report.

5      The complexity of the genome

The genome region of interest for a test is, if possible, defined on the basis of functional information that is available about the genome. The genome is far from homogenous and regular both from a functional organisation and molecular DNA content such as the frequency of the different bases or the complexity of the sequence formed by them. The human genome is the result of an evolution spanning a very long time period and as a result of this evolution some parts of the genome have gained in functional importance while others have lost or even become redundant. There are thus some regions of the genome that are more difficult to sequence due to their DNA sequence repetitivity and one cannot be totally sure whether the sequencing process has captured data from the region of interest or from another region with high similarity but less relevant from a functional perspective. This basic biological complexity has its own bearing on the reporting as well.

6      The ever changing knowledge landscape

A genetic test generally reports variants in the gene sequence, in other words divergence, and the understanding of the functional implications of this divergence. Interpretation of the measured divergences is based on ever growing understanding that is available from fundamental research on the genome and the function of its different regions. As this understandings evolves and more information becomes available, so will the interpretation. The genetic test report is therefore strongly bound to the biomedical and functional knowledge about the genome that is available at the time of analysis. A renewed analysis can therefore at a later time point bring understanding not accessible at the moment of the original report.

7      After all, what is normal?

Divergence, in other words genetic variants have to be measured in comparison to something – or to many things. In gene testing one compares divergence to what is called a reference genome. Any divergence from the reference is a variant. The first version of an incomplete human genome was published at the beginning of the millenium. Since then the sequence of this reference genome has been updated multiple times, bringing in corrections to existing errors and new sequence to parts of the genome that had not previously been sequenced (https://www.ncbi.nlm.nih.gov/grc/human).

The latest clinically relevant human genome version is GRCh38.p14 (November 2022). It does not any more represent any single human being but is in stead of compilation of the most ‘normal’ or frequent sequences based on a multitude of data sources and considerations. There will continue to be updates publicly available at regular intervals in the form of patch releases to the main version 38. However, it has been decided to indefinitely postpone next coordinate-changing update (GRCh39) while the genome consortium evaluates new models and sequence content from ongoing efforts to better represent the genetic diversity of the human pangenome, including those of the Telemore-to-Telomere Consortium and the Human Pangenome Reference Consortium.

Different populations show different type of ‘normality’, in other words any identified variant in a patient can well have different level of presence in different populations. This means that the relevance of a variant in a patient gene test is not only a function of the variant itself but also of the underlying population frequency of that variant. Normality in South-East Asian populations is different from that in a European one. Some populations even show small pockets of very ‘unusual’ normality (Charoenngam et al. 2025, DOI: 10.1186/s13023-025-04160-x ) in terms of variant frequencies and relevance.

ClinGen provides regularly updated guidance regarding the use of variant population frequencies provided by gnomAD version 4 (https://clinicalgenome.org/site/assets/files/9445/june_2025_communication_to_clingen_vceps_from_clingen_vcep_review_committee.pdf). These guidelines are frequently assessed in various specific contexts, showing that their use is not black and white but requires contextualised additional knowledge to discern what is normal and what not (Wang et al. 2024, DOI: 10.1016/j.ejmg.2024.104909).

8      From DNA via raw NGS data to genetic variants

Not only the quality of the wet lab work, DNA purification, library construction are critical; also the sequencing itself and the ensuing data analysis are. Computational tools are used to identify the divergence from the reference genome by using the measurement raw data. It takes a few critical bioinformatic software procedures to convert the raw sequence data to a list of identified variants. Some of the computational steps are heuristic, meaning that the alignment and variant calling is proceeding by trial and error or by rules that are only loosely defined. By applying strict and comprehensive quality control it is possible to ensure that the identified genetic variants can be trusted and that the absence of variants does not just depend on a badly covered region during sequencing. A good report will include relevant metrics to document the correctness of the procedure of the genetic test.

The observations shall also be recorded using formats and nomenclature that can be understood and this means in practice to apply certain standards that have been developed by professionals. The HGVS Nomenclature is an internationally-recognised standard for the description of DNA, RNA, and protein sequence variants. It is used to convey the definition of variants in clinical reports and to share variants in publications and databases. There is an HGVSg standard for genomic location, HGVSc for transcript location, and HGVSp for amino acid location. The latter two are dependent on the selected transcript and can therefore not be used without also specifying the transcript for which the numbering applies. There is a general understanding of using the canonical or the MANE transcript, but this has sometimes to be adjusted as non-canonical transcripts are more abundantly used in some tissues than in others. The HGVS Nomenclature is administered by the HGVS Variant Nomenclature Committee (HVNC) under the auspices of the Human Genome Organization (HUGO) (den Dunnen et al. 2016, DOI: 10.1002/humu.22981; Hart et al. 2024, DOI: 10.1186/s13073-024-01421-5).

The VCF, or Variant Call Format, is used to ensure precise inter-system exchange of variant call data for research and clinical applications, together with any additional quality-related informatio. It is a standardised text file format used for representing SNP, indel, and structural variation calls. The VCF specification and other bioinformatic file specifications are now managed by the Genomic Data Toolkit team of the Global Alliance for Genomics and Health (Wagner et al. 2021, DOI: 10.1016/j.xgen.2021.100027).

Reporting should abide to these established formats to maximise the usability of a report.

9      Is every genetic variant equally relevant?

Obviously, every observed variant in a gene test does not carry the same importance from a functional perspective or in the given context of the condition of the tested person. Concerning relevance it is useful to discern two concepts present in normal language usage that have taken more specific significance when used for genetic variants. Firstly, variant classification is about assigning pathogenicity of variants based on established physical and functional criteria. Secondly, prioritisation is about ranking variants in terms of clinically significant in a given patient context, combining information about the patient with factors like gene, phenotype and condition, as well as inheritance patterns and variant occurrence in the population. The relevance of a variant is thus defined both in terms of classification and of prioritisation.

Figure 1: Assertion guidelines and their interdependence with particular focus on somatic variants but not only

10   Guidelines for asserting variant relevance

Variant analysis and reporting can follow different combinations of guidelines depending among other on whether the genetic test is about germline (constitutional) variants or about somatic ones (Fig.1). Predisposing assertion utilises generally the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines (Richards et al. 2015, DOI: 10.1038/gim.2015.30; Brandt et al. 2020, DOI: 10.1038/s41436-019-0655-2). In this system, variants are classified in five tiers from pathogenic to benign. For somatic predisposing variants there are the ClinGen/CGC/VICC guidelines (Mehta et al. 2021, DOI: 10.1007/s40291-021-00540-8; Horak et al. 2022, DOI: 10.1016/j.gim.2022.01.001). In this system, variants are classified in five tiers from oncogenic to benign via variants of unknow significance. National guidelines exist, such as those from the UK Association for Clinical Genomic Science (ACGS, https://www.acgs.uk.com/quality/best-practice-guidelines/).

Some – partially overlapping – guidelines are instructed by the condition of the patient as well, such as the ASCO/AMP/CAP guidelines. These provide a tiered system for classifying somatic variants in cancer based on their clinical significance for a given cancer type, using a four-tiered approach: Tier I (strong clinical significance), Tier II (potential clinical significance), Tier III (unknown clinical significance), and Tier IV (benign or likely benign). These guidelines, developed by the Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP), aim to standardise the interpretation and reporting of molecular results for cancer diagnosis, prognosis, and treatment.

11   Now to the report generation itself

Report generation for a genetic diagnostic test compiles the interpreted results into a formal document. There will be the primary results, but possibly also secondary findings, carrier information, or variant risk alleles. The target reader can be either the patient itself, and/or the medical professional, in which case there will be differently adapted content. The date of the document will be indicating when it was created or became effective, and possibly implicitly or explicitly talk about update (see databases below). The document will contain information about the patient, the orderer of the test, the source of the biological material used, the test itself and the reason for applying it to the patient (Fig.2, example from the omnomicsNGS reporting system).

Figure 2: Example of report section on patient, orderer, and epicrisis

The report will then contain test result data organised into different sections. Firstly, the molecular sample preprocessing methodology, the sequencing itself, and the bioinformatic post-processing as well as variant interpretation shall be described enough to convey a general understanding of its overall power as well as limitations.

Figure 3: Example of report section with a brief test description

Secondly, the factual observation will be reported using standard descriptors, that, if needed, can be shared also outside of the laboratory structure performing the test.

Figure 4: Example of report section with primary observations

Thirdly, an analysis of the observations using the biomedical knowledge available at the moment of analysis is provided so as to support and guide the treatment of the patient. This can, or not, depending on the situation, be followed by treatment recommendations. However, this might be the task of another person than the analyst (Fig.5).

If different sections such as primary and secondary findings are presented, each of them will have their own analysis (Fig.6).

Figure 5: Example of report section with part of the key observations’ interpretation

Figure 6: Example of report section with secondary findings and more

At the end, reference to the used analytical methodologies, statistics on the population level, databases used and their versions, as well as other more advanced data mining or artificial intelligence methods applied has to be provided. Also a section on the sample quality control statistics can be included either in the report itself, or in case more details are used it is recommended to provide it as a seprate QC report.

Figure 7: Example of report section on quality control of WES data

Figure 8: Example of report section on applied data sources for the annotations

These general guidelines will have to be adapted to the different types of genetic variants that are reported. Indeed, for small variants, quite precise knowledge is generally available. Structural variants, these being more difficult to compare to previously seen, will require a different approach to the annotation itself, and also the limit of present knowlege might be reached.

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Introduction

Genetic analysis is rapidly becoming an essential tool in fields like healthcare, research, and forensics. However, traditional processes are often  slow, labor-intensive, and prone to error. Automation is transforming this landscape that by streamlining workflows, enhancing  accuracy and saving valuable time. This article explores how automation technology is being applied to genetic identity and analysis systems.

What is Genetic Testing Automation?

Genetic test automation harnesses advanced technologies to optimize and streamline genetic testing workflows. Solutions like omnomicsQ and omnomicsV exemplify this innovation by automating critical steps – from sample tracking to data analysis. These systems integrate seamlessly into laboratory settings, minimising  manual intervention and improving operational efficiency. By taking over repetitive error-prone tasks, automation enables more reliable genetic analyses, enabling laboratories to process larger volumes of data with greater consistency.

Additionally, automated genetic testing  improves access to complex  insights by streamlining the interpretation process. These systems can efficiently analyze  vast datasets to identify patterns or anomalies that might otherwise go unnoticed. This capability is particularly valuable in modern genetic analysis, where large-scale data handling has become the standard. Beyond accelerating workflows, automation also strengthens reproducibility which is essential for maintaining scientific accuracy.

Importance of Automation in Genetic Analysis

Automation is reshaping genetic analysis by ensuring processes are more consistent and efficient. One of its key contributions is improving quality control through automated validation and monitoring systems. These systems follow strict standards, such as ISO 13485 compliance, to reduce the risk of errors during testing. By automating critical validation steps, you can ensure consistent and reliable outcomes in genetic analysis, even when handling large-scale or complex datasets. This level of precision is important for maintaining trust in genetic testing results and meeting industry expectations for quality assurance.

Another significant impact of automation is its role in advancing personalized medicine. The rising demand for individualized treatment plans relies heavily on precise genomic analysis. Automation enables faster and more accurate processing of genetic data, ensuring actionable insights can be derived more efficiently. This capability directly supports the development of customized therapies tailored to a patient’s unique genetic profile, bridging the gap between genetic research and clinical application.

Key Applications of Genetic Testing Automation

1. Accelerating Disease Diagnosis and Prediction

Genetic test automation has revolutionized how clinicians diagnose and manage diseases by making testing faster, more accurate, and more scalable. By rapidly processing large volumes of genetic data, automation enhances both disease diagnosis and risk prediction. In rare disease detection, many disorders result from mutations in a single gene, and automated pipelines can quickly analyze whole-exome or whole-genome data to identify pathogenic variants, significantly shortening the “diagnostic odyssey” that often spans years.

Automation also extends beyond diagnosis, enabling tailored treatment and even predicting disease risk before symptoms appear. Automated analysis of polygenic risk scores (PRS) combines information from thousands of genetic markers to estimate an individual’s predisposition to conditions such as cardiovascular disease, type 2 diabetes, or Alzheimer’s, facilitating earlier interventions. Beyond treatment, automated interpretation of genetic data can guide lifestyle optimization, highlighting predispositions related to diet, exercise response, or circadian rhythms and supporting personalized lifestyle recommendations. At the population level, automation enables preventive screening programs by stratifying individuals based on genetic risk, helping healthcare systems allocate resources more effectively and deliver proactive, data-driven care.

2. Advancing Personalized medicine

Genetic testing is redefining personalized medicine by enabling faster, more accurate, and highly scalable analysis of complex genomic data. A major application is in tumor profiling, the process of analyzing a cancer’s genetic makeup to identify mutations that drive its growth. Automation enhances this by detecting somatic mutations, copy number variations, and gene fusions with high precision, helping physicians choose therapies designed to target those specific changes. For example, identifying BRCA1/2 mutations can inform the use of PARP inhibitors in breast and ovarian cancers, while detecting EGFR mutations supports the use of specialized therapies in lung cancer.

Another critical area is pharmacogenomics, the study of how genes affect an individual’s response to medications. Automated analysis of genetic variants in drug-metabolizing enzymes, such as those in the CYP450 family, helps physicians tailor drug type and dosage to each patient, reducing trial-and-error prescribing and minimizing adverse drug reactions. By combining tumor profiling with pharmacogenomic insights, automated genetic testing not only accelerates the discovery of clinically relevant variants but also integrates treatment guidance directly into patient care. This dual impact enhances precision, efficiency, and personalization across the healthcare system, making genetic insights more practical and accessible for everyday clinical decision-making.

3. Reproductive & Prenatal Testing

In reproductive medicine, speed and accuracy are critical, and genetic testing provides powerful tools that directly influence clinical decisions. In preimplantation genetic testing (PGT), embryos can be screened for chromosomal abnormalities or specific genetic disorders before IVF implantation, lowering the risk of passing on inherited conditions. Similarly, non-invasive prenatal testing (NIPT) analyzes cell-free fetal DNA in maternal blood to detect chromosomal abnormalities such as Down, Edwards, or Patau syndromes with high sensitivity and minimal risk. Beyond pregnancy, both carrier screening and newborn screening benefit from large-scale genetic assays: prospective parents can be tested for recessive conditions, while newborns can be screened for hundreds of disorders. Early detection of these conditions allows for timely interventions that significantly improve long-term outcomes. Together, these approaches enhance family planning, prenatal care, and early-life health management.

4. Expanding Frontiers in Genomic Research 

In research, genetic testing technologies have become indispensable for exploring the complexity of genomes at scale. In the laboratory, high-throughput sequencing platforms, robotic liquid handlers, and automated sample preparation systems reduce human error and accelerate experiments that once took months to complete. On the computational side, automated bioinformatics pipelines manage raw sequencing data, align genomes, identify variants, and integrate multiple data layers—including transcriptomics, epigenomics, and proteomics—without constant manual oversight. This streamlines discovery while improving reproducibility, a long-standing challenge in genomics. Functional genomics also benefits, with streamlined workflows for CRISPR-based screening, single-cell sequencing, and gene expression profiling, helping researchers uncover gene functions and disease mechanisms. In population genomics, large-scale analysis of data from thousands or millions of individuals supports genome-wide association studies (GWAS), variant frequency mapping, and ancestry research, revealing insights into human evolution, population structure, and complex disease risks. By integrating robotics, machine learning, and cloud computing, genomic research is shifting into a new era of speed, scale, and precision, fueling breakthroughs across biology, medicine, and public health.

Core Technologies Driving Gene Test Automation

1. Role of Automated Systems in Monitoring Genomic Samples

Automated systems play a critical role in maintaining the precision and consistency of genomic sample monitoring. These platforms continuously flag samples that fail to meet pre-defined quality thresholds, ensuring that only high-quality data moves forward in the testing process.

• By reducing human errors, such as mislabeling or sample mix-ups, these systems standardize workflows and significantly enhance reliability in high-throughput environments.
• Real-time quality checks ensure that issues like degraded samples or temperature fluctuations are identified early, preventing downstream inaccuracies in genetic analysis.
• Automation also provides enhanced traceability through digital records and barcoding, creating a transparent chain of custody for each sample. This traceability is crucial for audits and compliance with regulatory standards, including ISO 13485 and IVDR.

Incorporating such systems not only boosts efficiency but also ensures compliance and consistency, laying a robust foundation for accurate genomic testing.

2. Integration of High-Throughput Sequencing with Automation

High-throughput sequencing (HTS), when paired with automation, has revolutionized genetic analysis by enabling the rapid sequencing of vast amounts of DNA. Automation enhances this process by streamlining tasks like sample preparation, sequencing, and data analysis.

• Scalability and Efficiency: Automated platforms can handle hundreds or thousands of samples simultaneously, making them indispensable for population-scale studies and clinical diagnostics.
• Reduced Variability: Robotic systems manage repetitive tasks, such as pipetting and library preparation, with precision, ensuring consistency across experiments. This standardization minimizes human variability and strengthens reproducibility.
• Data Analysis: Automated bioinformatics pipelines align sequences, identify variants, and generate insights far more efficiently than manual methods. This reduces processing times while maintaining high data accuracy.

By combining HTS with automated systems, laboratories can meet growing demands for genetic testing with faster turnaround times and improved reliability. These advantages are critical for advancing research and clinical applications, especially in environments requiring high throughput and precision.

3. AI and Machine Learning in Genetic Data Interpretation

Artificial intelligence (AI) and machine learning (ML) are transforming genetic data interpretation by analyzing vast datasets with unparalleled speed and accuracy.

• Variant Classification: AI enhances the identification of genetic variants by analyzing patterns across extensive datasets, reducing false positives and negatives. This is especially critical in clinical applications, where precision is vital for patient outcomes.
• Data Integration: AI bridges diverse data sources, harmonizing inputs from sequencing platforms and annotation databases such as ClinVar, CIViC etc. This unified approach enables researchers and clinicians to derive more comprehensive insights into genetic data.

These technologies, when integrated into workflows, enhance efficiency, data accuracy, and research outcomes. Tools like omnomicsQ monitor quality in real time, while validation systems such as omnomicsV maintain high sensitivity and specificity. Streamlined processes—from sample preparation to data analysis—shorten turnaround times, boost lab productivity, and support faster clinical decision-making. Automation also safeguards data and ensures compliance with GDPR, HIPAA, IVDR, and ISO 13485 through encryption, access controls, audit trails, and detailed documentation. Together, these capabilities make genetic testing more precise, efficient, and trustworthy, benefiting both patient care and research.

Quality Assurance and Standardization in Genetic Testing

1. Participation in External Quality Assessment (EQA) Programs

External Quality Assessment (EQA) programs play a critical role in maintaining high standards in automated genetic testing. These programs, such as the European Molecular Genetics Quality Network (EMQN) and Genomics Quality Assessment (GenQA), are designed to promote cross-laboratory standardization and improve the accuracy of results. By participating in EQA programs, laboratories can compare their performance against standardized benchmarks, identify discrepancies, and make necessary adjustments to ensure consistent quality across different testing platforms.

EQA programs also support collaboration and knowledge sharing among laboratories. By requiring participants to regularly submit genetic testing results for evaluation, these programs create opportunities to align methodologies and address inconsistencies. This process fosters a culture of continuous improvement and helps labs stay updated with advancements in genetic testing technologies.

2. Benchmarking and Inter-Laboratory Comparisons for Performance Monitoring

Benchmarking and inter-laboratory comparisons are integral to performance monitoring in genetic testing. Participation in EQA programs, such as EMQN and GenQA, provides a framework for evaluating laboratory processes and aligning them with international standards.

• Performance Evaluation: By benchmarking against peers and adhering to guidelines like those from ACMG and CAP, labs can identify gaps and optimize workflows.
• Collaborative Improvement: Inter-laboratory comparisons through EQA foster knowledge sharing and help labs refine methodologies to ensure consistent and accurate results.

Challenges in Implementing Gene Test Automation

1. Technical and Infrastructure Barriers

Integrating automated systems into genetic testing laboratories poses significant technical and infrastructural challenges. Establishing a fully automated environment requires substantial investments in advanced robotics, high-capacity servers, and specialized software capable of managing large and complex genomic datasets. Without these foundational elements, automation efforts can encounter workflow bottlenecks that limit efficiency and scalability.

Skill Gaps: Effective use of advanced tools such as omnomicsV and omnomicsQ demands specialized training to ensure laboratory staff can operate new systems confidently and resolve technical issues as they arise. To overcome these challenges, laboratories should prioritize infrastructure modernization and structured training programs to ensure smooth integration of automation into existing workflows.

2. Data Security and Privacy Concerns in Genomics

As automation increases the volume and speed of genetic testing, it also increases the importance of robust data security and privacy measures. Genetic information is uniquely identifiable and highly sensitive, requiring stringent protection to prevent unauthorized access or misuse. Compliance with frameworks such as GDPR and HIPAA is essential, as these regulations emphasize data minimization, consent-based use, encryption, and controlled access.

Tools like omnomicsNGS, which are designed in alignment with these standards, strengthen data protection by incorporating encryption, secure data storage, and detailed audit trails. These measures not only safeguard patient privacy but also build trust in automated genomic workflows and ensure ongoing regulatory compliance.

3. Ethical Considerations in Automated Genetic Testing

Automation introduces critical ethical considerations in genetic testing, including informed consent, data use, and equitable access.

• Informed Consent: As automated systems handle vast amounts of genetic data, individuals must fully understand how their information is used, stored, and shared. Transparent processes are essential to ensure consent remains meaningful and not merely procedural.
• Data Protection: Safeguards must prevent genetic discrimination or misuse of sensitive data, ensuring ethical handling in compliance with GDPR and HIPAA.
• Equitable Access: Automation can reduce costs and increase efficiency, but low-resource settings often lack access to these advancements. Addressing this disparity requires deliberate strategies to make genetic testing technologies accessible globally.

Conclusion

Genetic test automation is redefining genetic analysis by combining efficiency with precision. It bridges technology and biology to accelerate discoveries, improve patient outcomes, and set new standards in diagnostics. While challenges remain, its transformative potential outweighs the challenges. Embracing automation in genetic testing is not just an upgrade—it’s a strategic step toward shaping the future of personalized medicine and genomic sciences.

Euformatics, a leader in NGS data validation and analysis, offers cutting-edge solutions to enhance genetic analysis through automation. With tools like omnomicsQ, omnomicsV, and omnomicsNGS, Euformatics ensures accuracy, compliance, and efficiency in genetic testing workflows. To help laboratories plan and optimize their costs, the Genomics Hub price configurator provides a transparent and customizable pricing tool. Explore it here.

Take the next step in advancing your genetic testing processes—Book a Demo today and see how Euformatics can transform your lab’s efficiency and precision.

FAQ

What is genetic testing automation?

Genetic testing automation refers to the use of advanced software, robotics, and integrated bioinformatics tools to streamline processes such as DNA extraction, sequencing, data analysis, and reporting. It minimizes manual handling, improves accuracy, and speeds up genetic testing workflows.

2. How does automation improve accuracy?

Automated systems reduce human error by standardizing procedures and applying consistent quality checks. They also use built-in validation and monitoring tools—like omnomicsQ and omnomicsV—to ensure results meet high sensitivity and specificity standards.

4. How is automation used in personalized medicine?

Automation allows clinicians to analyze large volumes of genetic data quickly, identifying variants linked to disease risk or drug response. This enables personalized treatment plans, targeted therapies, and safer medication choices.

What Are the Benefits of Automating Genetic Testing Workflows?

Key benefits include faster turnaround times, improved accuracy, higher reproducibility, and stronger regulatory compliance. It also enables large-scale testing, enhances patient care, and supports data-driven healthcare decisions.

References

• Mansfield, David C., Alastair F. Brown, Daryll K. Green, Andrew D. Carothers, Stewart W. Morris, H. John Evans, and Alan F. Wright. “Automation of genetic linkage analysis using fluorescent microsatellite markers.” Genomics 24, no. 2 (1994): 225-233.
• Pérez-Losada, Marcos, Miguel Arenas, Juan Carlos Galán, Mª Alma Bracho, Julia Hillung, Neris García-González, and Fernando González-Candelas. “High-throughput sequencing (HTS) for the analysis of viral populations.” Infection, Genetics and Evolution 80 (2020): 104208.
• Fountzilas, E, Pearce, T, Baysal, MA, Chakraborty, A, Tsimberidou, AM. “Convergence of evolving artificial intelligence and machine learning techniques in precision oncology”. npj digital medicine 8, 75 (2025)

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Growth in clinical genomics

Euformatics develops and delivers software for clinical genomics, helping hospitals and diagnostic laboratories transform raw sequencing data into accurate, accredited, and actionable results for patient care. Our solutions support laboratories around the world in:

• Annotating and interpreting genetic variants
• Running highly automated next-generation sequencing (NGS) bioinformatics workflows
• Ensuring reliable and accurate patient reporting through integrated quality control and NGS test validation

Between 2021 and 2024, the company experienced strong organic growth as genomic testing became increasingly integrated into mainstream healthcare. Clinical genomics transitioned from a specialized research activity to a core diagnostic tool — especially in oncology, rare diseases, and inherited disorders.

A rare combination: growth and profitability

The Deloitte Technology Fast 50 ranking is based purely on revenue growth, not profitability. This makes Euformatics’ performance particularly noteworthy — one of the few companies on the list to have achieved significant growth while operating profitably every year from 2021 to 2024.

“Achieving a place on the Deloitte Fast 50 list is a great recognition for our team and our customers,” said Tommi Kaasalainen, CEO of Euformatics.

“What makes me most proud is that we have managed to grow rapidly without sacrificing profitability or sustainability. In the current tech landscape, that’s rare — but for us, it reflects the trust that clinical laboratories place in our software and the long-term value we’re creating for healthcare. This recognition belongs equally to our dedicated team and to the customers who rely on us every day to make genomics work in real-world diagnostics.”

Growth driven by innovation and collaboration

Euformatics’ growth has been powered by a combination of product innovation, trusted customer relationships, and global collaboration in advancing clinical genomics standards.

Among our valued customers is Cerba Healthcare, one of Europe’s leading networks of medical laboratories. Cerba uses Euformatics software as part of its clinical diagnostics operations — supporting accurate, standardized variant interpretation in daily patient testing. Working with a group that processes millions of samples each year provides real-world validation of our products’ scalability, robustness, and regulatory readiness.

Our commitment to improving data quality in genomics also extends beyond our clinical customers. Euformatics has worked closely for the past decade with international quality assessment organizations such as EMQN (European Molecular Genetics Quality Network) and GenQA to assess the performance of clinical NGS laboratories globally. Through these partnerships, we contribute software tools, domain expertise, and automation solutions that support external quality assessment (EQA) schemes — helping laboratories worldwide measure and improve their performance in bioinformatics workflows.

Looking ahead

Being included in the Deloitte Technology Fast 50 list is both a milestone and a motivation. As precision medicine continues to expand globally, Euformatics remains focused on helping laboratories and healthcare systems scale genomics safely, efficiently, and affordably.

“Our mission has always been to make genomic data interpretation accurate, standardized, and accessible,” added Kaasalainen. “We’ll continue to invest in products and partnerships that make that vision a reality — sustainably.”

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Copy number variants (CNVs) are the subject of extensive research. They are common features of the human genome that play an important role in evolution, contribute to population diversity, development of certain diseases, and influence host–microbiome interactions. CNV analysis has found application in the molecular diagnosis of many diseases and in non-invasive prenatal care; still, its full potential lies ahead in time. CNVs are expected to have a significant impact on screening, diagnosis, prognosis, and monitoring of several disorders, including cancer and cardiovascular disease (Carey-Smith et al. 2024, DOI: 10.3390/ijms25136815 , Caputo et al. 2025, DOI: 10.3390/jcdd12070258).

While it is clear that CNVs play a significant role in the modification of the function of the genome, CNV calling has been challenging for clinical diagnostic laboratories. This was also shown in two pilot external quality assessment tests run by EMQN and GenQA together with Euformatics in 2022 and 2024, and presented first time at ESHG in 2023 (Gutowska-Ding et al. 2023, DOI: 10.1038/s41431-023-01482-x). Challenges of different kinds were identified.

On a high level, a great diversity of CNV reporting was seen for one and the same sample (Figure 1 below). Indeed, setting up the CNV caller pipeline is not mastered everywhere, leading for example to some unexpected results, such as omitting and rejecting shorter CNVs at specific thresholds. In the oppisite direction, some used pipelines also reported CNVs insertions or deletions shorter than 10 base pairs. Such variants, by convention, belong to the category of Indels (in general when shorter than 50bp) and can be called by simpler pipelines used for SNVs and Indels.

On a detailed level, break-end discovery, as a step in CNV detection, for the same sample, varied significantly with up to 1500bp for exon-based panels, while WGS-based (whole genome sequencing) breakend calling, quite expectedly, could get down to more precise position across different callers. Breakends can also for biological reasons show high variability with many unique breakpoints but also recurrent hotspots with clustering of breakends around “unstable” genomic loci is. Persistency of breakend detection, beyond technical issues of detection, is affected by genomic architecture such as chromatin structure, sequence homology, repeat content, replication timing, fragility, and of course selective pressures.

Getting better with CNV detection

In a nutshell, good detection requires a homogenous sequence signal, in other words regular, maximally uniform read depth and per base
quality.

The difference between panel sequencing, including WES on one hand, and WGS on the other is related to two challenges. Uniformity of the
coverage is essential for CNV detection. Segmented sequence information, where intron sequence is mostly lacking, makes it, if not
impossible, then at least more difficult to recognise the position of the breakends. Moreover, in segmented, mostly exon-sequencing, the
detection signal for a capture is not even over the exons, diminishing read depth near the segment ends. In theory, therefore, calling CNVs
on typical WES data can, at best, get down to a per exon breakend approximation.

For WGS data, an uneven signal landscape, also including introns, just looks like, well, capture and sequencing errors. Consequently also here
breakend identification is affected by genome architecture. Noise or real signal, that will be the question for any pipeline. For practical reasons
also price of WGS sequencing and data management of complete genome data can be an issue for clinical diagnostic laboratories and WES still maintains some advantages.

Figure 1: Sizes of copy number loss variant calls. Each dot represents a CNV loss call. Each row corresponds to a submission (only part of the
data is shown), the horizontal position of the dot corresponds to the size of the called deletion. The color of the dot represents the filtering status, with
rejected calls in orange. (Gutowska-Ding et al. 2023, doi: 10.1038/s41431-023-01482-x)

Clinical diagnostics

To improve the clinical diagnostic power of WES, it is possible to gain some extra information also from the intronic segments by adding fragmented capture through capture spiking. This has been done for example by Twist Biosciences through the creation of a CNV backbone to be combined with a standard WES capture. The value of regular spike-in grows for longer gaps not otherwise amenable to typical panel sequencing1. Also, the high uniformity and quality of the Element Biosciences sequencers’ read-out provide better precision to the breakend detection process.

This has been tested by Euformatics in a systematic validation of its full range CNV calling pipeline (exon to whole chromosome aneuploidy) combined with the Twist exome CNV backbone capture on a set of 1000 Genomes Project samples available from Coriell2 (CNVPANEL01, 43 samples). The raw sequencing data came from libraries constructed using the Twist Exome 2.0 Plus Comprehensive Exome Spike-in capture panel. The Twist spike-ins targeted polymorphic SNPs distributed in the intergenic and intronic regions. Combined with the exon targeting, this backbone of spike-ins allowed a genome-wide detection of CNVs and loss of heterozygosity (LOH) in addition to small variants with a sensitivity (recall) of 100% over 42 samples.

One sample had a uniparental disomy, which requires an adjustment in the calling process, and it is not, sensu structo, a CNV. Precision was not estimated, since the CVPANEL01 does not provide a full truth set of all the CNVs present in the samples.

Conclusion

WES together with extra spike-in provide good, even if fragmented capture coverage over the genome. A high quality sequence read-out is then sufficient for clinical diagnostic laboratories to provide exon-level CNV calling. Intra-exonic CNVs are already covered by WES without spike-in. Once CNV breakends are detected, such as with the Euformatics validated variant calling pipelines, variant analytics and reporting tool omnomicsNGS will provide the necessary support to add any essential variant, compound heterozygosity, gene, phenotype, similarity to previously identified structural variants, and other annotations to support clinical reporting.

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Euformatics and Analityk Genetyka have collaborated successfully on the Polish market to sign several hospitals as Genomics Hub customers during 2023 and 2024.  A number of genetic laboratories at public hospitals have been or are being onboarded to analyze clinical NGS data from locally collected samples. For more than a decade, Analityk Genetyka has been providing comprehensive support to users of Illumina NGS systems in Poland, which has resulted in the majority of sequencing data being generated on these platforms. Given the installed base of NGS instruments in Poland there is a significant upside potential to further expand the collaboration.

1. Background and Context

Euformatics develops and markets software tools for clinical genomic data analysis by hospitals and clinical laboratories. The tools are used for diagnosing rare diseases and cancer patients. The company was founded in 2010 and has operated profitably for most of its history, not having to rely on external investors to fund its operations.

Analityk Genetyka (AG) was founded in 2018, based on former OpenExome (2012-2018) company resources. AG from the very beginning is bonded with Analityk company creating joined structure called the Analityk Group. Analityk Genetyka provides complex portfolio including laboratory equipment, automatization, accessories, consumables, reagents and analysis software. Our mission is to deliver a comprehensive, NGS-based end-to-end solution to our customers. Many years of experience and productive cooperation both with our foreign partners and Polish customers shape the main pillar of our business.

The reason Euformatics and Analityk Genetyka decided to partner was clear – both companies were targeting the same end users in the clinical genomics space. Having an end-to-end offering from reagents and instruments to the tools needed to analyze genomic data and produce professional reports was important for Analityk Genetyka to serve their customers throughout the lab workflow. Euformatics, on the other hand, needed a local partner who had existing relationships and trust established with the local key opinion leaders and major medical institutions. The clinical genomics market in Poland is undergoing a significant transformation, driven by advancements in technology, increasing awareness of precision medicine, and supportive governmental and academic initiatives. Stakeholders must navigate issues such as funding, workforce development, and ethical concerns while capitalizing on advancements in technology and a supportive policy environment. By addressing these barriers, Poland can emerge as a leader in genomics-driven healthcare within Central and Eastern Europe, setting an example for neighboring markets.

2. The Partnership Strategy

The channel partnership started quite typically with product training and joint local event participation. Getting a new service known on a local market takes time and effort. Trust and perception of reliability and responsiveness by a service provider does not happen overnight. Clinical genomics in particular requires a high level of confidence in the analytical performance of the solutions because the end user is analyzing molecular level events. Those cannot be easily validated with other means so the performance of the kit, instrument and bioinformatics pipeline need to be on a high level. Often this means that validation is done separately for each customer to ensure that their patients are diagnosed using high quality data. This is what Euformatics and Analityk Genetyka set out to do with the labs that onboarded to Genomics Hub.

One such lab where we worked closely together with the geneticist team of the customer was Pomeranian Medical University in Szczecin (PUM).

Dr. Wojciech Kluźniak from PUM Szczecin comments on the collaboration: ”Our joint work with Euformatics and Analityk Genetyka has been very successful and allows us to effectively carry out NGS analyses. Euformatics provides advanced bioinformatics tools that enable precise and efficient analysis of sequencing data. Meanwhile, Analityk Genetyka supplies high-quality equipment and reagents necessary for laboratory research. The combination of solutions from both companies offers solid support for our projects and translates into the high quality of the results obtained. We assess the cooperation as professional, effective, and worth continuing. We especially appreciate the excellent support provided by the staff, whose professionalism and commitment greatly facilitate our work.”

3. Joint Market Engagement

Euformatics and Analityk Genetyka have jointly organized and participated in both physical and online events for the Polish clinical NGS market. Analityk Genetyka sponsored an event by the International School of Molecular Biology in 2022 in Warsaw at Lazarski University that brought together the key opinion leaders in the Polish market. Euformatics participated in demonstrating its clinical NGS data analysis tools for the participants.

We have also organized joint webinars for the Polish customers to introduce and onboard local laboratories to the Genomics Hub product family. The discontinuation of a software tool previously used in local laboratories marked a pivotal moment, as it prompted increased interest from labs seeking a suitable replacement. This resulted in multiple new users for Euformatics Genomics Hub in Poland.

4. Challenges and How They Were Overcome

The Polish labs that are starting out their NGS journey face the same challenges as labs elsewhere. Located in the EU, they need to be compliant with IVDR requirements and work with solutions that are either CE-IVD or IVDR certified. Assay selection, validation and verification is another area that requires effort. Analityk Genetyka can support their customers with the wet lab side activities while the application scientists at Euformatics onboard customers with NGS test validation in the beginning of the collaboration. These are important steps to ensure that patients are diagnosed based on high quality sequence data.

All new Genomics Hub customers from Poland already had their own quality management systems in place. The NGS operations needed to be added to those and full workflows documented properly. This has been a joint effort between the labs, Analityk Genetyka and Euformatics.

Dr. Agnieszka Sulewska from Olsztyn Children’s Hospital comments on the onboarding support: ”The teams at Analityk Genetyka and Euformatics have been instrumental in getting new tests introduced to our lab. They have supported us with the test validation and identification of problematic areas in the patient genome. These are important to detect as they could carry variants that do not get detected but are of clinical significance.”

Dr. Christophe Roos, Chief Scientific Officer at Euformatics continues: ”There is a tremendous and general drive to raise the ambition level of genetic diagnostics at Polish hospitals and laboratories. This is reflected in the fact that we have been working with very motivated people interested in taking 2768some steps deeper in the understanding of the complexity of NGS-based sequencing. This attention to details bodes well in a world where some products are presented under their ideal conditions, creating an illusion of simplicity. Knowing the difficulties and potential pitfalls removes risks from the patient care.”

5. Conclusions

Onboarding six new NGS sites in Poland for the Euformatics Genomics Hub has been a nice collaborative effort with Analityk Genetyka. AG has supplied consumables and instruments while Euformatics has taken the lead in setting up the bioinformatics pipelines for the customers and training local geneticists on how to automate diagnosis. The best evidence of successful onboarding has been the growing number of samples that are being analyzed with the Genomics Hub  Each of the laboratories started with just a few samples per month, but now they use Genomics Hub routinely.

This is a great start to a collaboration we expect to continue and grow for years to come. The leadership of both companies is committed to maintaining a high level of service to Polish hospitals and clinical labs.

Tommi Kaasalainen, CEO of Euformatics comments: ”Analityk Genetyka is an example of a great local distributor for us. They know the clinical genetics space in Poland very well and have obtained the trust of the customers especially in the NGS area through their Illumina partnership. From our perspective, we could not have hoped for a better partner in Poland and we are looking forward to working with them to transition Poland more deeply into precision medicine.”

Dr. Juliusz Unrug, Head of Marketing and Market Access at Analityk Genetyka concludes: ”The collaboration with Euformatics represents a model example of implementing advanced solutions in the genetic testing market, providing full support to users in Poland. Through access to reliable data and comprehensive analyses, we are able to ensure consistent and effective support for all users of these solutions.”

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Introduction

Analyzing somatic variants in next-generation sequencing (NGS) data can be complex. Between data preprocessing, variant calling, and filtering, the process often requires specialized expertise and computational resources. This complexity can slow down research and clinical decision-making. New tools and methods are emerging to simplify this workflow, making somatic analysis more accessible and efficient. This article explores practical approaches to streamline somatic variant analysis in NGS data.

Understanding Somatic Variant Analysis in NGS

Biological and Clinical Relevance

Somatic variant analysis focuses on detecting mutations that occur in non-germline (somatic) cells, those not inherited or passed on to offspring. These mutations can play a pivotal role in cancer initiation, progression, and treatment response. As such, somatic variant detection is a cornerstone of oncology research, personalized medicine, and precision diagnostics.

Accurate identification and interpretation of somatic variants provide critical insights into tumor evolution and heterogeneity. Clinicians and researchers rely on these insights to guide targeted therapies, predict patient response, and monitor disease progression over time.

To ensure clinical utility, the interpretation of somatic variants must be reproducible and grounded in evidence-based standards. This is achieved by adhering to well-established guidelines from professional organizations, including:

• American College of Medical Genetics and Genomics (ACMG): Provides widely adopted guidelines for the interpretation of sequence variants, especially in germline (inherited) contexts
• Association for Molecular Pathology (AMP): Partners with ACMG and CAP to co-develop and endorse best practices for molecular testing, including somatic variant interpretation in oncology
• American Society of Clinical Oncology (ASCO): Publishes consensus recommendations on incorporating genomic testing results into cancer diagnosis and treatment
• College of American Pathologists (CAP):  Establishes laboratory accreditation standards to ensure high-quality and reproducible variant analyses.
  

The adoption of automated validation tools like omnomicsV supports laboratories in confirming variant calls and ensuring that genomic findings are both accurate and actionable. Furthermore, real-time quality control platforms such as omnomicsQ help prevent the downstream analysis of low-quality samples, reducing the risk of erroneous conclusions.

Participation in external quality assessment (EQA) programs, such as those run by EMQN and GenQA, also plays a key role. These programs facilitate cross-laboratory benchmarking, enabling labs to identify discrepancies and continually improve their performance.

Regulatory Compliance and Quality Assurance in Somatic Variant Analysis

Beyond technical accuracy, manufacturers developing variant interpretation software and other in vitro diagnostic (IVD) solutions must adhere to rigorous regulatory and quality assurance frameworks. These frameworks ensure that products are safe, effective, and suitable for clinical use—ultimately influencing the reliability and consistency of downstream laboratory analyses.

A foundational regulatory benchmark is ISO 13485:2016, which defines requirements for quality management systems (QMS) specific to medical devices and IVD products. For manufacturers, ISO 13485 compliance ensures:

• Documented design and development processes
• Risk management integrated throughout the product lifecycle
• Robust traceability and process control
• Continuous improvement and corrective action tracking
  

Compliance with ISO 13485 is particularly crucial for gaining CE marking under the European Union’s In Vitro Diagnostic Regulation (IVDR). IVDR introduces stricter requirements for clinical evidence, performance evaluation, and post-market surveillance. Manufacturers must provide detailed technical documentation and validation data to demonstrate that their products meet these stringent criteria.

Impact on Laboratories

While the primary responsibility for regulatory compliance lies with the manufacturer, these standards have a direct impact on laboratories using the products:

• Reliability: Laboratories depend on ISO 13485 certified tools to ensure analytical validity and reproducibility in somatic variant analysis
• Traceability: Standardized outputs and well-documented software design facilitate traceable, auditable workflows
• Regulatory Use: For laboratories operating under IVDR as part of clinical diagnostic services, use of certified IVD products ensures regulatory alignment
• Risk Management: Manufacturer-led risk controls are foundational for laboratories to conduct accurate risk-benefit assessments when interpreting genetic variants

By aligning with ISO 13485:2016 and IVDR, manufacturers enable laboratories to deliver high-confidence, clinically actionable results, while also ensuring their own compliance with global regulatory expectations.

Methods to Simplify Somatic Variant Analysis

1. Efficient Data Preprocessing

Effective data preprocessing is fundamental to accurate and streamlined somatic variant analysis. Automating quality control and standardising workflows help minimize error rates, reduce computational overhead, and enhance the reliability of downstream results (Roy et al., 2018).

1.1 Automated Sample Quality Control

Low-quality samples introduce significant noise, increasing the risk of false positives or missed variants. Tools like omnomicsQ offer real-time monitoring of sequencing quality and automatically flag samples that fall below predefined thresholds. Early identification and removal of problematic samples prevent wasted resources and ensure that only high-quality data proceeds to variant calling and interpretation.

1.2 Real-Time Alerts and Workflow Integration

The integration of real-time QC systems like omnomicsQ within sequencing workflows enables immediate corrective actions, such as reprocessing or resequencing, before data issues propagate through the pipeline. This proactive approach not only increases laboratory efficiency but also improves data integrity and reduces turnaround time.

1.3 Cross-Laboratory Standardization and Benchmarking
Variability in sequencing platforms and analysis tools can lead to inconsistent results. To mitigate this, laboratories are encouraged to participate in External Quality Assessment (EQA) programs, such as those provided by EMQN and GenQA, which enable cross-lab benchmarking and performance evaluation. By adopting automated, real-time quality control, engaging in benchmarking initiatives, and standardizing preprocessing protocols, laboratories can significantly streamline somatic variant analysis. These measures not only improve reproducibility and reduce errors but also make high-throughput sequencing more accessible and scalable across institutions.

2. Somatic Variant Calling Methods

Somatic variant calling is essential for improving the efficiency, accuracy, and reproducibility of NGS data analysis. Historically, this process has required deep bioinformatics expertise, but advances in automation, optimized workflows, and regulatory-aligned tools have made it far more accessible to clinical and research laboratories.

2.1 Automated and Preconfigured Pipelines

Modern variant-calling tools provide the core tools, users need to configure parameters and build or adopt their own automated workflows to run them end-to-end. These tools include default parameters as a baseline, users need to review the documentation and tune settings for their specific data and experimental design.

Following tools are widely adopted in cancer genomics and support tumor-normal and tumor-only analyses:

• GATK Mutect2 (Broad Institute)
• Strelka2 (Illumina)
• VarDict and VarScan2
  

By automating parameter selection and filtering, these tools reduce user-introduced variability and enhance reproducibility across sequencing runs. While variant calling is handled by computational algorithms, platforms like omnomicsV by Euformatics add a crucial layer of validation supporting structured, repeatable verification of detected variants across different runs and laboratories.

2.2 Low-Noise and High-Confidence Variant Detection

Detecting true somatic mutations particularly in heterogeneous or low-purity tumor samples requires careful handling of background noise and sequencing artifacts. Advanced filtering techniques are essential to minimize false positives and improve confidence in mutation calls.

These techniques typically include:

• Base quality score recalibration
• Duplicate read removal
• Background noise modeling
• Artifact filtering

Automated filtering pipelines often integrated within secondary analysis tools consistently apply these steps without the need for manual curation, helping labs deliver high-confidence results in less time.

2.3 Cloud-Based Scalability and Ease of Use

Variant calling is computationally intensive, but cloud-based platforms provide scalable infrastructure and user-friendly interfaces that streamline complex analysis. Platforms such as:

• DNAnexus,
• Seven Bridges Genomics,
• Terra, and
• Illumina BaseSpace

 enable researchers to run full somatic workflows including variant calling, annotation, and visualization—without managing local hardware or installing complex software.These platforms are especially valuable for clinical labs, as they support automated execution of validated pipelines, saving both time and IT resource

3. Regulatory-Compliant and Secure Workflows

For laboratories working with clinical samples, compliance with international regulations is non-negotiable. Tools and workflows must adhere to standards such as:

• IVDR (In Vitro Diagnostic Regulation): Ensures the safety and clinical performance of diagnostic workflows, including NGS-based tests.
• ISO 13485:2016: Establishes quality management requirements for medical devices and diagnostics.
• GDPR (EU) and HIPAA (US): Mandate strict protection of patient data and genomic information

Solutions like omnomicsNGS are designed to align with these frameworks, supporting regulatory-compliant validation, traceability, and data security. This integration allows laboratories to optimize their variant analysis workflows while maintaining the highest standards of quality and privacy.

4. Automated Annotation & Filtering Methods

In somatic variant analysis, automated annotation and filtering play a crucial role in transforming raw variant calls into meaningful biological insights while reducing false positives and manual workload.

4.1 Multi-Source Annotation Integration

Effective somatic variant interpretation relies on consolidating clinical and genomic insights from multiple authoritative databases. Automated annotation systems gather relevant data from sources such as:

• ClinVar (clinical variant interpretations)
• CIViC (Clinical Interpretation of Variants in Cancer)
• COSMIC (Catalogue of Somatic Mutations in Cancer)
• gnomAD (population frequency data)
  

Rather than manually cross-referencing these diverse resources, tools like omnomicsNGS integrate multi-source annotations to deliver faster, more comprehensive, and up-to-date variant classifications. This approach reduces discrepancies and ensures variant assessments reflect the latest scientific knowledge

Popular automated annotation tools include:

• ANNOVAR: Provides comprehensive functional annotation, including gene-based, region-based, and filter-based annotations.
  
• Ensembl Variant Effect Predictor (VEP): Annotates variants with information about their impact on genes, transcripts, and regulatory regions. Supports extensive plugin architecture for custom annotations.
  Ensembl VEP
  
• SnpEff: Predicts the effects of variants on genes and proteins, facilitating the identification of potentially damaging mutations.

4.2 Automated Filtering and Validation

Automated filtering pipelines systematically exclude likely false positives or benign variants, improving result confidence while minimizing manual review. Common filtering criteria include:

• Read depth and base quality thresholds
• Strand bias and mapping quality metrics
• Artifact and sequencing noise removal

Variant callers like GATK Mutect2 (FilterMutectCalls) and Strelka2 (Strelka2 GitHub) integrate these filtering steps automatically during or after variant calling.

Complementing filtering, semi-automated tools such as omnomicsNGS provide a structured framework to ensure variant interpretations meet industry and regulatory standards. By systematically verifying results against quality guidelines—including IVDR and ISO 13485 compliance—these tools reduce the need for extensive manual data curation and increase confidence in the reproducibility of reported variants.

5. Guidelines and Frameworks for Somatic Variant Interpretation 

As NGS becomes routine in oncology, the accurate interpretation of somatic variants is critical for guiding clinical decisions and therapeutic strategies. Unlike germline variants, somatic mutations often exhibit complex patterns and low allele frequencies, making their classification and reporting particularly challenging. To address this, several internationally recognized guidelines and interpretation frameworks have been developed to standardize the analysis process and improve consistency across laboratories.

 5.1 AMP/ASCO/CAP Guidelines for Somatic Variant Classification

The joint guidelines published by the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) in 2017 provide a tiered system for interpreting somatic variants in cancer. Variants are categorized based on clinical significance and supporting evidence (Li et al., 2017):

• Tier I: Strong clinical significance (e.g., FDA-approved therapies, diagnostic relevance)
• Tier II: Potential clinical significance (e.g., emerging evidence, investigational drugs)
• Tier III: Unknown clinical significance
• Tier IV: Benign or likely benign

5.2 VICC Meta-Knowledgebase Framework

The Variant Interpretation for Cancer Consortium (VICC) created a harmonized meta-knowledgebase that integrates data from multiple somatic variant resources (such as CIViC, OncoKB, and JAX-CKB). It maps variant interpretations to common evidence levels and classification schemes, enabling researchers and clinicians to compare and consolidate clinical evidence more efficiently. This initiative supports global consistency by reducing discordant interpretations across databases.

5.3 Joint ClinGen/CGC/VICC Guidelines: A Standard Operating Procedure (SOP)

In 2022, a consortium including ClinGen, Cancer Genomics Consortium (CGC), and VICC published a consensus Standard Operating Procedure (SOP) for classifying the oncogenicity (pathogenic potential) of somatic variants in cancer (Horak et al., 2022). Previous frameworks—such as AMP/ASCO/CAP somatic guidelines focused on clinical actionability, but lacked a structured method to evaluate variant oncogenicity. The new SOP addresses this gap. Input from experts in translational biology, oncology, and bioinformatics was synthesized to create a scoring-based system that categorizes evidence strength into four levels: Very Strong, Strong, Moderate, and Supporting. The SOP was validated using 94 somatic variants across 10 cancer-associated genes, demonstrating reproducibility and improved consistency in variant classification.

How the SOP Classifies Oncogenicity?

Each criterion (e.g., hotspot presence, functional assays, population frequency) carries a specific point value. The combined score determines one of five classifications:

• Oncogenic (≥ 10 points)
• Likely Oncogenic (6–9 points)
• Variant of Uncertain Significance (VUS) (0–5 points)
• Likely Benign (−1 to −6 points)
• Benign (≤ −7 points)

Combining the SOP’s oncogenicity output with AMP/ASCO/CAP evidence tiers refines the interpretation pipeline allowing to distinguish biologically relevant variants regardless of clinical actionability.

6. AI & Machine Learning-Driven Methods for Faster Analysis

AI and machine learning are transforming somatic variant analysis in NGS by improving sensitivity, reducing manual workloads, and improving accuracy. These methods allow you to detect low-frequency mutations more reliably and streamline interpretation workflows.

AI-powered variant calling models significantly improve the detection of somatic mutations by increasing both sensitivity and specificity. Traditional methods often struggle with distinguishing true variants from sequencing noise, especially in low-frequency mutations.

AI models trained on large genomic datasets can better differentiate real mutations from artifacts, reducing false positives and false negatives. This results in more confident variant calls, which is critical for applications like cancer genomics and rare disease diagnostics.

Deep learning algorithms further refine accuracy by reducing noise in sequencing data. One of the key challenges in variant calling is the presence of sequencing errors, which can obscure true low-frequency variants. By analyzing complex sequencing patterns, deep learning techniques filter out artifacts while preserving real variants. This is particularly beneficial in tumor samples, where somatic mutations often exist at low allele frequencies and require highly sensitive detection methods. 

Machine learning models can distinguish true somatic mutations from sequencing artifacts more effectively than traditional rule-based filters. By learning complex patterns in sequencing data, AI-powered tools reduce false positives and enhance sensitivity, particularly for low-frequency variants. For example, GATK Mutect2 incorporates a machine learning-based filter (FilterMutectCalls) that models error profiles and contamination to improve variant call precision. Similarly, platforms like DeepVariant use deep learning to generate highly accurate variant calls from raw sequencing reads

For variant interpretation, automated tools like omnomicsNGS simplify and accelerate the process by integrating multi-source annotations and automating pathogenicity classification. Instead of manually cross-referencing multiple databases, this system aggregates data from sources like ClinVar, CIViC, and other variant repositories, automatically assigning pathogenicity scores based on ACMG and CAP guidelines. This automation reduces the time required for interpretation and ensures consistency in classification.

By utilizing AI and machine learning, you can streamline somatic variant analysis, reduce manual intervention, and improve the reliability of variant detection. These advancements not only accelerate workflows but also improve the accuracy and reproducibility of results in clinical and research settings.

Ready-to-Use Pipelines for Simplified Somatic Analysis

Ready-to-use pipelines simplify somatic variant analysis by integrating multiple processing steps into a single workflow. These pipelines handle quality control, variant calling, annotation, and reporting in a fully automated manner, reducing the need for manual intervention and technical expertise. By streamlining the entire process, they enable laboratories to achieve higher efficiency and consistency in genomic data analysis.

Cloud-based solutions further improve this efficiency by providing scalability and computational power without requiring high-maintenance local infrastructure. These platforms allow you to process large datasets without investing in expensive hardware, while also supporting on-demand resource allocation to optimize performance. By utilizing cloud environments, you can ensure faster processing times and seamless collaboration across distributed teams.

For regulatory compliance, modern pipelines incorporate GDPR, HIPAA, and IVDR-compliant frameworks that ensure secure data handling in both research and clinical settings. While GDPR and HIPAA focus on protecting patient data, IVDR ensures that diagnostic tools meet strict safety and performance standards. Compliance with these regulations is important for laboratories that handle sensitive genomic information, particularly those involved in clinical diagnostics.

GenomicsHUB by Euformatics: A Complete End-to-End Solution for Somatic Variant Analysis

GenomicsHUB is a secure, cloud-based platform that delivers an end-to-end solution for somatic variant analysis. It integrates the full power of omnomicsV, omnomicsQ, and omnomicsNGS, and a unified, scalable environment supporting quality control, secondary analysis, variant interpretation, and regulatory-compliant reporting in a seamless workflow. Designed for clinical diagnostics, research, and commercial sequencing labs, GenomicsHUB enables automated, high-throughput analysis while ensuring regulatory compliance. With its intuitive interface, multi-user collaboration features, and customizable reporting, GenomicsHUB simplifies complex genomic pipelines and supports the delivery of accurate, reproducible, and clinically meaningful results.

By adopting these fully automated, cloud-enabled, and compliance-driven pipelines, you can significantly reduce the complexity of somatic variant analysis while maintaining high accuracy and reliability.

Conclusion

Simplifying somatic variant analysis in NGS improves efficiency and accessibility without compromising accuracy. Advances in computational methods, automation, and AI-driven tools reduce complexity while maintaining analytical robustness. As methodologies continue to evolve, adopting streamlined approaches ensures faster, more scalable, and reproducible results.

Euformatics provides an end-to-end solution for NGS validation, quality control, and variant interpretation, enabling laboratories to adopt a truly simplified approach to somatic analysis. With tools like omnomicsQ for real-time sample quality control, omnomicsV for variant validation, and omnomicsNGS for comprehensive variant interpretation, Euformatics helps laboratories achieve accuracy, compliance, and efficiency.

To make genomic analysis solutions more accessible, Euformatics offers a transparent Genomics Hub Price Configurator, allowing laboratories to customize pricing based on their specific needs.

Ready to optimize your NGS workflows? Book a Demo today and see Euformatics in action.

FAQ

What Are the Common Challenges in Somatic Variant Calling From NGS Data?

Somatic variant calling in NGS data presents challenges such as low variant allele frequency, tumor heterogeneity, sequencing errors, and distinguishing true somatic mutations from germline variants. Accurate calling requires high-quality sequencing, optimized bioinformatics pipelines, and effective filtering strategies. omnomicsQ helps address these challenges by ensuring real-time sample quality monitoring, preventing poor-quality data from entering downstream analysis. omnomicsV assists in variant validation, ensuring that results meet quality and reproducibility standards.

How Can I Choose The Right Somatic Variant Caller For My Specific Needs?

Selecting a somatic variant caller depends on factors like sensitivity, specificity, and computational efficiency. A well-integrated validation framework, such as omnomicsV, helps verify variant accuracy across sequencing runs, ensuring reliability. Laboratories can streamline their workflows using automated pipelines that support seamless variant validation, reducing manual effort while maintaining high confidence in variant calls.

What Quality Control Metrics Should I Consider For Somatic Variant Analysis?

Key quality control metrics include sequencing depth, base quality scores, mapping quality, and variant allele frequency (VAF). omnomicsQ ensures sample integrity by flagging suboptimal genomic data in real-time, preventing low-quality samples from affecting results. Integrating automated quality control solutions helps labs minimize errors, reduce sequencing failures, and optimize NGS workflows.

How Do I Interpret The Output of A Somatic Variant Caller?

The output of somatic variant calling includes variant allele frequency (VAF), read depth, quality scores, and annotations. omnomicsNGS simplifies variant interpretation by automating pathogenicity classification and integrating multi-source annotations. It streamlines the interpretation process, ensuring that clinically relevant variants are identified efficiently, reducing the risk of false positives.

What Are The Emerging Trends in Somatic Variant Analysis Using NGS?

Advancements in somatic variant analysis focus on simplifying workflows while improving accuracy. Automated quality control and validation tools, such as omnomicsQ for sample monitoring, omnomicsV for variant validation, and omnomicsNGS for interpretation, enable labs to optimize efficiency and compliance. AI-driven methods, cloud-based platforms, and machine learning-driven variant filtering further improve sensitivity and specificity, ensuring more reliable results in precision medicine and cancer research.

References

• 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, 20(1), 4–27

• Ainscough, B. J., Barnell, E. K., Ronning, P., et al. (2018). A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nature Genetics, 50(12), 1735–1743

• Li, M. M., Datto, M., Duncavage, E. J., et al. (2017). Standards and guidelines for the interpretation and reporting of sequence variants in cancer. The Journal of Molecular Diagnostics, 19(1), 4–23
  
• Horak, P., Griffith, M., Danos, A. M., et al. (2022). Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of ClinGen, CGC, and VICC. Genetics in Medicine, 24(5), 986–998.

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