The partnership strengthens the product and service offering of Khalid Scientific which is already supplying instruments and reagents to molecular genetics laboratories in the region. Euformatics and Khalid Scientific share a common vision of improving accessibility to end-to-end genomic solutions, enabling laboratories to generate, analyze, and interpret sequencing data efficiently and at high-quality standards. Through Khalid’s extensive local presence and expertise in life sciences and diagnostics, Euformatics Genomics Hub will be made available to a broader network of clinical genetics and oncology-focused laboratories.

“Partnering with Khalid Scientific represents an important step in strengthening our presence in key Middle Eastern markets,” said Tommi Kaasalainen, CEO of Euformatics. “Khalid’s strong local footprint and its long history of supplying high-quality solutions to the clinical organisations make them an ideal partner for us. Together, we can offer hospitals and laboratories a seamless path from wet lab to clinical insight.”

Ali Qudah, CEO of Khalid Scientific, comments:

Ali Qudah, CEO of Khalid Scientific, comments: “Our customers are increasingly looking for complete genomic solutions that connect reagents and instruments with high-quality downstream analysis tools. By using Euformatics’ interpretation and reporting tools, we can provide laboratories with a powerful and streamlined workflow to support clinical genomics and oncology applications while ensuring that every patient is diagnosed using high-quality data. We are excited to bring this combined offering to our markets.”

By combining Euformatics’ expertise in automated NGS data interpretation with Khalid Scientific distribution network, the partnership is expected to accelerate the adoption of integrated genomic workflows in the Middle East, supporting improved diagnostics and patient outcomes.

About Khalid Scientific:

Khalid Scientific Co. was established in 1979 with the passionate desire to adequately provide medical, laboratory and pharmaceutical solutions together with services of the highest quality. Together with our co-founders — leading healthcare providers, medical and laboratory equipment/consumables manufacturers, and clients — KSC provides our valuable customers with the ultimate innovative solutions and a wide range of medical products safely and cost-effectively.

To learn more, visit https://www.khalidscientific.com/ 

About Euformatics:

Euformatics is a Finnish software company that specialises in high-standard bioinformatics tools for genomic data interpretation. Since 2010, Euformatics has been helping medical doctors and molecular genetics laboratories provide better precision medicine for cancer, common or rare disease diagnostics. At present, our core solution is the Genomics Hub which includes a variant interpretation tool; for clinical analysis and reporting of patient NGS data, and quality control tool; for NGS data quality management.

To learn more, visit www.euformatics.com

Press Relations:

Tommi Kaasalainen, CEO

Euformatics

tommi.kaasalainen@euformatics.com

The post PRESS RELEASE | Euformatics partners with Khalid Scientific to power clinical genomics data analysis in Qatar appeared first on Euformatics.

We are introducing omnomicsNGS version 2.14 with two new Vigilance features, Identity and Biological Sex, and harmonise them with the existing Trio concordance check into a single, unified checkpoint, catching sample mix-ups, pedigree mismatches, and sex discordance before variant interpretation begins.

Moreover, this release includes important improvements to ACMG classification (PVS1 and gene-specific BA1 threshold), upgrade to annotation resources (VEP 115 and databases used for CNV classification), as well as multiple usability improvements across the platform.

Identity vigilance:  additional safeguard against sample mix-ups

Confidence in genomic analysis starts with confidence in the sample being analysed. A sample mix-up or inconsistency in patient information will have an impact on the reliability of downstream variant interpretation. With version 2.14, omnomicsNGS introduces an Identity vigilance section that holds 3 vigilance cards with complementary checks that help users to identify potential sample-related inconsistencies early in the analysis workflow.

1.   Sample identity vigilance card

The new Sample identity card compares an independent identity sample with the supposed corresponding NGS test sample to assess whether both are originating from the same individual. We apply a statistical method of the same type as that presented by Sejoon Lee et al. (doi: 10.1093/nar/gkx193 )

The identity sample input is a vcf file from the same patient, providing that additional source of information for identity verification. Support for orthogonal (non-NGS) methods can be provided through prior conversion of the output to a vcf format. The Sample identity card presents a clear overview of the compared samples, the percentage of concordance, and the resulting identity status, helping users to quickly identify successful matches, potential mismatches, or cases where sufficient data is unavailable (Fig. 1).

Sample identity checks  provide an additional layer of protection against sample mix-ups and helps users to maintain confidence that the sample being analysed corresponds to the intended patient.

The Sample Identity card is displayed under the Identity Vigilance section of the sample page. The card provides an at-a-glance overview of the identity comparison and its outcome. It displays the samples included in the comparison, the concordance percentage, and the resulting identity status.

2. Biological sex vigilance card

The previously available derived sex of the sample (based on the ratio of read depth on chromosome X compared to that of autosomes) has been improved. In version 2.14 the Biological sex card was brought in as one of the 3 cards of the Identity vigilance section on the sample page, alongside Sample identity and Trio consistency.

On the Sex vigilance card the provided patient biological sex is compared to the sex derived from sequencing data and the concordance status is shown on a clear and intuitive card. The result is presented as either concordant, discordant, or insufficient data, depending on the available information and whether the values agree (Fig.1).

The biological sex selected in the patient information is also carried through to the PDF report, ensuring that the recorded information is consistently presented throughout the analysis workflow.

3. Trio consistency vigilance card

The previously available Trio consistency check of the index sample connected to two parental samples has been improved. In version 2.14 the Trio consistency card was brought in as one of the 3 cards of the Identity vigilance section on the sample page, alongside Sex vigilance and Sample identity. It holds the same information as previously in the same clear way as the other vigilance cards.

Figure 1.  An overview of the Identity Vigilance section showing Sex Vigilance, Sample Identity and Trio Consistency cards

 Conclusion

With omnomicsNGS version 2.14, we introduce Identity vigilance to provide an additional safeguard designed to catch sample mix-ups, pedigree mismatches, and sex discordance before variant interpretation begins.

This release also brings improvements to ACMG classification, annotation resources, and overall usability.

Together, these updates support a more reliable and consistent workflow, helping laboratories analyse and interpret genomic data with greater confidence.

For questions about omnomicsNGS v2.14, please contact us at support@euformatics.com.

The post New release: omnomicsNGS version 2.14.0 brings Patient Identity Vigilance as a new feature appeared first on Euformatics.

New approach rescued clinically important genomic findings that automatic filtering would have discarded, pointing to a high-scoring therapy option in an additional roughly 10% of cases.

Espoo, Finland; 27 July 2026 – Euformatics (Finland), Institut Curie (France), Oncompass Medicine and Genomate Health (Hungary) have successfully completed their 2.5-year Eurostars project, Precision Oncology Platform with Genomic Noise Cancellation, marking the milestone with a closing consortium meeting held in Budapest.

The project brought together leading expertise in bioinformatics, clinical oncology, precision medicine decision support, and real-world oncology data to address a key challenge in genomic medicine: how to reduce genomic noise caused by the large number of biologically non-relevant alterations and alterations generated by inherent errors of sequencing of thousands of bases without losing clinically important variants.

In routine molecular diagnostics, automated filtering steps during the bioinformatic analysis are essential to reduce the noise of false signals and help clinicians focus on variants that are likely to be clinically relevant. However, in some cases, clinically important variants with low or ambiguous quality signals are filtered out using flat quality thresholds before they can be assessed in the clinical context. 

Today, the process largely depends on the bioinformatician’s experience and subjective judgment to detect clinically relevant genomic alterations, which should be manually double-checked to avoid automated filtering. This poses a risk to cancer patients. 

Similar to noise canceling, we need a more advanced technology that not only reduces background noise but also enhances the strength of the important genomic signal when signal quality is poor, and the signal can be lost in the noise, to automatically flag these for human manual check. 

In this project, the consortium successfully implemented computational precision-oncology reasoning technology to automatically flag such “not-to-be-missed” variants, ensuring that all clinically relevant molecular information is identified, supporting a more sensitive and clinically informed interpretation workflow to find the right personalized therapy for all cancer patients. Genomate’s core technology was connected to and combined with Euformatics’s bioinformatics platform to provide an end-to-end solution for the somatic diagnostic process.

“Reducing genomic noise is not only a technical challenge; it is a clinical challenge,” said Christophe Roos, Chief Scientific Officer of Euformatics. “The project demonstrated that bioinformatics workflows can be made more sensitive and more clinically aware when variant interpretation and treatment relevance are brought closer together.”

Using securely managed, confidential data from Institut Curie, Euformatics and Genomate Health built a prototype workflow that helps reduce genomic noise while preserving potentially actionable findings. In the project dataset, rescued variants enabled a high-scoring treatment option in 9% of evaluated patient cases, suggesting that the approach may reveal new therapy options in a clinically meaningful subset of patients.

While many decision-support systems and AI approaches are benchmarked against similarity to expert recommendations, the technology developed by the consortium aims to go one step further: to estimate whether an individual patient is likely to benefit from a targeted therapy. This outcome-oriented approach – tested on molecular tumor board data from Institut Curie – was presented and discussed in multiple genomics conferences during the project.

“For Genomate Health, this project was an important step toward connecting high-quality genomic interpretation with patient-level treatment benefit prediction,” said Dr. Barbara Vodicska, Head of Translational Science at Genomate Health. “The ability to rescue potentially relevant variants and then evaluate their therapeutic relevance through an outcome-oriented scoring system is exactly the type of innovation needed to make precision oncology more actionable, evidence-based, and scalable.”

Taken together, the results of the Eurostars project support the potential of integrating bioinformatics quality assessment, genomic noise reduction, and outcome-predictive treatment ranking into a more robust precision oncology workflow. The consortium believes that this approach warrants prospective clinical studies and may, in the future, help streamline access and reimbursement decisions for molecularly guided therapies by providing stronger evidence on expected patient benefit.

“The strength of this project lies in bringing together expertise from across the precision oncology ecosystem,” said Dr. Edith Borcoman, project lead and medical oncologist at Institut Curie’s Drug Development and Innovation Department, and leader of the Institut Curie Molecular Tumor Board. “By combining advanced bioinformatics, computational reasoning, and clinical validation, the consortium has developed an innovative approach to reducing genomic noise while preserving clinically meaningful findings. Genomate warns us when genomic findings deserve a second look, providing an additional layer of quality control that has the potential to improve the consistency and quality of molecular interpretation and make precision oncology more evidence-based and ultimately more beneficial for patients.”

The project also advanced the partners’ regulatory readiness, with IVDR¹ compliance as a central consortium objective. IVDR-related audits were conducted at both participating companies during the project period, and both are now progressing toward IVDR certification for their respective IP.

The successful completion of the Eurostars project marks an important milestone for the consortium and lays the groundwork for future clinical validation, prospective studies, and continued collaboration between the partners.

“No patients are left behind, and we turn every stone to find the right therapy for cancer patients for the first time and every time. Integrating Genomate’s automated reasoning into the beginning of the diagnostic process gives another layer of security for our patients,” added Dr. Istvan Petak, Founder and Chief Scientific Officer of Genomate Health and Founder of Oncompass Medicine. 

‍

About the Eurostars Project

The Eurostars-funded project Precision Oncology Platform with Genomic Noise Cancellation was launched to develop an end-to-end oncology solution capable of improving genomic variant interpretation by reducing noise while preserving clinically relevant signals. The project was carried out by Euformatics, Oncompass Medicine, Genomate Health, and Institut Curie over 2.5 years.

About Euformatics

Euformatics is a Finnish software company specializing in high-standard bioinformatics tools for genomic data interpretation and quality management. Since 2010, Euformatics has supported clinical laboratories and healthcare providers with solutions for NGS data quality control, variant interpretation, and reporting. To learn more, visit: euformatics.com

About Institut Curie

Institut Curie, France’s leading cancer center, combines an internationally-renowned research center with a cutting-edge hospital, treating all types of cancer, including the rarest ones. Founded in 1909 by Marie Curie, Institut Curie employs 4,000 researchers, physicians, and health professionals across three sites (Paris, Saint-Cloud, and Orsay), all of whom contribute to its three missions of treatment, teaching, and research. As a public-interest foundation authorized to receive donations and bequests, Institut Curie relies on donor support to accelerate scientific discovery and improve treatments and quality of life for patients. To learn more, visit: curie.fr

About Genomate Health

Genomate Health develops precision oncology decision-support solutions designed to help identify the most relevant targeted therapy options for individual cancer patients. Its proprietary technology, Digital Drug Assignment or Genomate®, integrates molecular profiles, tumor context, drug evidence, and clinical relevance into an outcome-oriented treatment ranking approach. To learn more, visit: genomate.health

About Oncompass Medicine

Oncompass Medicine is a Hungarian pioneer in precision oncology, integrating molecular diagnostics, advanced bioinformatics, and oncology decision-support tools to guide personalized cancer therapy. The company has been at the forefront of applying molecular diagnostics in solid tumors as companion diagnostics in 2003, next-generation sequencing in 2008, and mathematical modeling to identify effective targeted treatments for patients as early as 2014, earning international recognition for its innovations in personalized medicine. To learn more, visit: oncompass.hu

‍

Press Contacts

Euformatics

Tommi Kaasalainen – tommi.kaasalainen@euformatics.com

Genomate Health / Oncompass Medicine

‍Alina Luchian – alina.luchian@genomate.health 

Institut Curie

Catherine Goupillon-Senghor – +33 06 13 91 63 63 – catherine.goupillon@curie.fr
Elsa Champion – +33 07 64 43 09 28 – elsa.champion@curie.fr

‍

^1. The In Vitro Diagnostic Regulation (IVDR, Regulation (EU) 2017/746) is the European regulatory framework governing in vitro diagnostic medical devices, ensuring their safety, quality, and clinical performance before and after market access.

The post PRESS RELEASE | Eurostars Consortium Completes Precision Oncology Project That Could Reveal Overlooked Treatment Options for Cancer Patients appeared first on Euformatics.

Introduction

Gene panel sequencing has emerged as a cornerstone of modern genomics, offering targeted analysis of specific gene sets with high precision and efficiency. It plays a pivotal role in disease diagnosis, personalized treatment planning, and biomedical research, enabling clinicians and researchers to gain actionable insights from genetic data. As applications continue to expand across healthcare and research settings, understanding the purpose, benefits, and practical uses of gene panel sequencing is essential for professionals in the field. This article explores what gene panel sequencing entails, why it is used, and how it is applied across clinical and scientific research. 

What is Gene Panel Sequencing?

Gene panel sequencing is a Next-Generation Sequencing (NGS) approach that targets a predefined group of genes associated with specific diseases or biological pathways.  Instead of sequencing the entire genome or exome, this approach focuses only on genes of clinical or research-relevance, making it a cost-effective and efficient alternative for diagnostic laboratories and l research institutions.

Laboratories can choose between custom or pre-designed gene panels depending on their validation, variant interpretation, or research needs.

• Custom panels allow researchers and clinicians to tailor gene selection for specific diseases or phenotypes.
• Pre-designed panels streamline standardization and validation, ensuring compliance with ISO 13485 standards, which are a prerequisite for IVDR certification.

Purpose and Advantages of Gene Panel Sequencing

Gene panel sequencing plays a critical role in clinical and research settings by enabling the targeted analysis of specific disease-associated genes. Unlike whole genome sequencing, which generates vast amounts of genetic data, gene panel sequencing focuses only on relevant genes, reducing unnecessary data and simplifying downstream analysis. This targeted approach enhances diagnostic precision and streamlines treatment decision-making. 

One of the key advantages of gene panel sequencing is its faster turnaround time for clinical reporting. Since the sequencing process is limited to a predefined set of genes, laboratories can process results more efficiently, making it ideal for high-throughput diagnostic workflows. Faster results enable clinicians to make timely decisions, improving patient management and personalized treatment planning.

Standardization and reproducibility are also enhanced through gene panel sequencing, as the technique ensures consistent validation across laboratories. To ensure standardized variant classification and reporting, laboratories conducting gene panel sequencing have to comply with guidelines established by the American College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP), and the College of American Pathologists (CAP). These frameworks define best practices for interpreting genetic variants, ensuring consistency and reliability in clinical reporting. Additionally, adherence to international regulatory standards such as ISO 13485 and In Vitro Diagnostic Regulation (IVDR) is required for laboratories developing and using diagnostic tests. Tools like omnomicsV support the validation of sequencing data, while omnomicsNGS facilitates variant interpretation, ensuring compliance with these standards.

Maintaining high-quality assurance in gene panel sequencing is important for clinical reliability. Automated systems such as omnomicsQ continuously monitor genomic samples, flagging those that do not meet pre-defined quality thresholds. Participation in external quality assessment (EQA) programs, such as EMQN and GenQA, further improves cross-laboratory standardization, ensuring that results remain reproducible and clinically valid.

By integrating gene panel sequencing into clinical workflows, healthcare providers gain a powerful tool for diagnosing hereditary conditions, guiding cancer treatment, optimizing pharmacogenomic therapies, and ensuring compliance with strict industry standards.

Clinical Applications of Gene Panel Sequencing

One important application is the diagnosis of hereditary diseases, where gene panel sequencing is used to detect pathogenic variants associated with inherited disorders. Identifying these genetic variants allows for early detection of disease, enabling healthcare providers to take preventive actions or begin treatment before symptoms appear.

This approach also supports informed family planning. Individuals who carry disease-related mutations can evaluate potential reproductive risks and consider options such as genetic counseling or preimplantation genetic testing.

In oncology and cancer genomics, gene panel sequencing is vital for detecting somatic and germline mutations. This data enables personalized treatment by helping clinicians select targeted therapies or immunotherapies tailored to a tumor’s specific genetic profile.

For example, mutations in the BRCA1 and BRCA2 genes are widely used to assess an individual’s risk of developing breast and ovarian cancers. Similarly, pathogenic variants in MLH1, MSH2, MSH6, and PMS2 are associated with Lynch syndrome, a hereditary condition that significantly increases the risk of colorectal and other cancers. The growing clinical relevance of gene panel testing is reflected in the development of comprehensive diagnostic tools, such as the Invitae Common Hereditary Cancers Panel, which analyzes multiple genes linked to hereditary cancer susceptibility.

Another important application of gene panel sequencing is pharmacogenomics, which aims to optimize drug therapy based on a patient’s genetic profile. By identifying genetic variations that affect drug metabolism and response, clinicians can better predict how patients will react to specific medications. This enables the selection of the most appropriate drug and dosage while minimizing the risk of adverse drug reactions.

For instance, variations in genes encoding enzymes of the cytochrome P450 system can significantly influence the metabolism of commonly prescribed medications, including anticoagulants and antidepressants. Incorporating this genetic information into clinical decision-making supports personalized treatment strategies and improves both the safety and effectiveness of drug therapy.

Research Applications of Gene Panel Sequencing

Understanding gene function is essential for elucidating the molecular mechanisms underlying disease. Gene panel sequencing enables the investigation of genetic pathways and interactions, helping researchers identify the roles of specific genes in both normal biological processes and disease states. 

This approach allows researchers to analyze targeted sets of genes associated with particular biological pathways, facilitating discoveries related to inherited disorders, cancer development, and rare genetic diseases. By examining how genetic variants influence protein function and cellular processes, researchers can gain valuable insights that may contribute to the development of new therapeutic strategies. Accurate variant classification and data interpretation require advanced bioinformatics tools capable of processing and analyzing the large volumes of genomic data generated by sequencing technologies.

 Platforms like omnomicsNGS support this process by integrating multi-source annotations, ensuring that variant classifications remain consistent and up to date. These platforms streamline the interpretation of genetic data by incorporating information from databases such as ClinVar and CIViC, reducing the manual workload while improving accuracy.  Automated re-evaluation of stored variants ensures that previously analyzed data reflects the latest scientific findings, which is important for maintaining reliable research outcomes.

Gene panel sequencing also plays an important role in large-scale population studies by providing scalable sequencing strategies for epidemiological and genomic research. By applying gene panels to large cohorts of individuals, researchers can investigate how genetic variation contributes to disease risk across diverse populations. This approach is particularly valuable in population-based genetic screening programs, where identifying disease-associated variants can inform public health strategies and preventive interventions.

Large-scale sequencing efforts enable researchers to assess genetic diversity, detect mutations associated with inherited conditions, and improve risk prediction models using population-level genetic data. These studies help reveal patterns of genetic variation within and between populations, contributing to a better understanding of disease susceptibility and genetic risk factors.

Overall, gene panel sequencing supports large-scale population research by enabling targeted investigation of disease-related genes, improving variant interpretation, and advancing our understanding of the genetic basis of disease across populations.

Technical Aspects of Gene Panel Sequencing

Understanding the technical aspects of gene panel sequencing is important for ensuring accurate, reliable, and scalable genomic analysis. The efficiency and quality of sequencing depend on well-structured panel design, robust variant interpretation, seamless IT integration, and adherence to external quality assessment (EQA) programs.

Effective panel design starts with selecting target genes based on clinical relevance. The goal is to ensure high coverage and specificity, capturing genes associated with the disease or condition being investigated.  This involves balancing panel size with sequencing depth and avoiding unnecessary genes that could weaken analytical sensitivity. Additionally, designing panels with flexibility allows laboratories to update them as new genetic insights emerge.

Interpreting variants presents major computational challenges. Genomic data require secondary analysis pipelines capable of handling large datasets efficiently. Tools such as omnomicsNGS streamline variant interpretation by integrating multiple annotation sources and filtering strategies. This automation minimizes manual effort while ensuring compliance with guidelines from organizations like ACMG and CAP. Also, quality control tools like  omnomicsQ provide real-time monitoring of sequencing runs, flagging samples that do not meet predefined quality thresholds. This proactive approach prevents downstream errors and ensures only high-quality data proceed to analysis. The  validation tools like omnomicsV help laboratories verify assay performance, ensuring that variant detection meets required sensitivity and specificity thresholds. Additionally, seamless integration with laboratory IT systems is critical to maintaining workflow efficiency and regulatory compliance. Laboratories have to ensure that sequencing data flows smoothly between sequencing platforms, LIMS (Laboratory Information Management Systems), and healthcare IT infrastructures. Compliance with data protection regulations such as GDPR, HIPAA, and IVDR is non-negotiable. While GDPR and HIPAA focus on safeguarding patient data, IVDR ensures that diagnostic products meet safety and performance standards. Laboratories using ISO 13485-compliant tools meet these regulatory requirements while maintaining high operational standards.

Limitations and Ethical Considerations in Gene Panel Sequencing

Despite its advantages, gene panel sequencing has several important limitations that must be considered in both research and clinical settings. Because panels target a predefined set of genes, they may fail to detect pathogenic variants in genes not included, including novel or unexpected disease-associated genes. Certain types of genetic alterations—such as large structural variants, copy number variations, repeat expansions, and deep intronic or regulatory mutations—may also go undetected depending on the panel design and sequencing technology. Interpreting results can be challenging, as many variants are classified as variants of uncertain significance (VUS), requiring additional functional studies, family segregation analyses, or reference to population databases for accurate classification. Population bias is another concern, since panels are often developed based on data from specific ethnic groups, potentially reducing accuracy and leading to misclassification in underrepresented populations. Technical factors, including low-quality DNA, uneven sequencing coverage, and potential sequencing errors, can further compromise result reliability. Moreover, gene panels must be regularly updated to incorporate newly discovered disease-associated genes; otherwise, older panels may provide incomplete or outdated information.

While panels are more cost-effective and faster than whole-exome or whole-genome sequencing, their narrow focus limits their ability to capture the full spectrum of genetic variation, which may be critical in complex, atypical, or multifactorial cases.Gene panel sequencing raises important ethical, regulatory, and quality considerations. Handling genomic data requires compliance with frameworks such as GDPR in Europe and HIPAA in the United States, which protect patient privacy and enforce data security. Ensuring standardization and reproducibility across laboratories is also critical, as variations in sequencing protocols, bioinformatics pipelines, and data interpretation can affect results. Laboratories address these challenges through validated workflows, automated quality control, and participation in external quality assessment programs. Additionally, gene panel sequencing can reveal incidental findings, highlighting the need for clear policies on informed consent, genetic counseling, and responsible reporting to protect patient autonomy and well-being. Emerging regulations, such as the IVDR in the EU, will further formalize standards for diagnostic test performance once fully implemented.

Conclusion

Gene panel sequencing is both a targeted and efficient approach to genetic analysis. Its ability to focus on specific genes improves diagnostic accuracy while optimizing cost and data management. As its applications expand in both clinical and research fields, considerations around limitations and ethical concerns remain important. Advancements in sequencing technology and bioinformatics will continue refining its utility, ensuring broader accessibility and improved outcomes.

Euformatics provides an end-to-end genomic data analysis platform that ensures accuracy, compliance, and efficiency in gene panel sequencing workflows. With tools like omnomicsQ for real-time quality control, omnomicsV for validation, and omnomicsNGS for variant interpretation, laboratories can streamline sequencing processes while meeting ISO 13485 and IVDR standards. 

To make genomic analysis solutions more accessible, Euformatics offers a transparent pricing configurator where laboratories can customize costs based on their specific needs. Explore the Genomics Hub Price Configurator.

Book a demo today to see how Euformatics can optimize your gene panel sequencing workflows.

FAQ

What is Genetic Panel Testing?

A genetic panel test analyzes multiple genes simultaneously to detect variations associated with specific health conditions, hereditary disorders, or inherited traits. It can aid in diagnosing genetic diseases, assessing individual disease risk, guiding treatment decisions, and supporting informed family planning.

How Much Does a Full Genetic Panel Cost?

The cost of a full genetic panel varies widely, typically ranging from $300 to $5,000, depending on the complexity of the test and the number of genes analyzed. Prices might also differ based on the laboratory, the technology used, and whether insurance coverage is available.

What are incidental findings in gene panel testing?

Incidental findings are genetic variants discovered during testing that are unrelated to the original reason for the test but may still have potential health implications. 

What Are the Advantages Gene Panel Testing? 

Gene panel testing is  cost-effective, faster than whole exome sequencing (WES) and whole genome sequencing (WGS), and focuses on clinically relevant genes, which simplifies data analysis and interpretation. . 

What are the limitations of Gene Panel testing?

Gene panels are analyzing only a predefined set of genes, that may miss disease causing variants in genes that are not included in the panel. Additionally it can miss CNV and other structural variants which might be relevant for the disease.

References

• Kohno, Takashi. “Implementation of “clinical sequencing” in cancer genome medicine in Japan.” Cancer science 109, no. 3 (2018): 507-512.
• Nagahashi, Masayuki, Yoshifumi Shimada, Hiroshi Ichikawa, Hitoshi Kameyama, Kazuaki Takabe, Shujiro Okuda, and Toshifumi Wakai. “Next generation sequencing‐based gene panel tests for the management of solid tumors.” Cancer science 110, no. 1 (2019): 6-15.
• Patel, Keyur P., Roberto Ruiz-Cordero, Wei Chen, Mark J. Routbort, Kristen Floyd, Sergio Rodriguez, John Galbincea et al. “Ultra-Rapid Reporting of GENomic Targets (URGENTseq): clinical next-generation sequencing results within 48 hours of sample collection.” The Journal of Molecular Diagnostics 21, no. 1 (2019): 89-98.

The post Gene Panel Sequencing: Understanding Purpose and Applications appeared first on Euformatics.

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

The post NGS Data Quality Control: Best Practices for Accuracy appeared first on Euformatics.

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.

The post What Are the Fundamentals of a Somatic Variant Interpretation Report? appeared first on Euformatics.

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.

The post What are the fundamentals of an NGS report? appeared first on Euformatics.

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)

The post Enhancing Genetic Analysis with Automation appeared first on Euformatics.