A Simplified Approach to Somatic Analysis in NGS

Introduction

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

Understanding Somatic Variant Analysis in NGS

Biological and Clinical Relevance

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

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

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

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

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

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

Regulatory Compliance and Quality Assurance in Somatic Variant Analysis

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

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

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

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

Impact on Laboratories

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

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

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

Methods to Simplify Somatic Variant Analysis

1. Efficient Data Preprocessing

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

1.1 Automated Sample Quality Control

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

1.2 Real-Time Alerts and Workflow Integration

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

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

2. Somatic Variant Calling Methods

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

2.1 Automated and Preconfigured Pipelines

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

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

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

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

2.2 Low-Noise and High-Confidence Variant Detection

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

These techniques typically include:

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

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

2.3 Cloud-Based Scalability and Ease of Use

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

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

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

3. Regulatory-Compliant and Secure Workflows

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

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

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

4. Automated Annotation & Filtering Methods

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

4.1 Multi-Source Annotation Integration

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

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

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

Popular automated annotation tools include:

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

4.2 Automated Filtering and Validation

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

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

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

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

5. Guidelines and Frameworks for Somatic Variant Interpretation 

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

 5.1 AMP/ASCO/CAP Guidelines for Somatic Variant Classification

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

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

5.2 VICC Meta-Knowledgebase Framework

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

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

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

How the SOP Classifies Oncogenicity?

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

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

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

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

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

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

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

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

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

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

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

Ready-to-Use Pipelines for Simplified Somatic Analysis

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

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

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

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

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

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

Conclusion

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

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

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

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

FAQ

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

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

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

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

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

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

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

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

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

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

References

• Roy, S., Coldren, C., Karunamurthy, A., Kip, N. S., Klee, E. W., Lincoln, S. E., Leon, A., Pullambhatla, M., Temple-Smolkin, R. L., Voelkerding, K. V., Wang, C., & Carter, A. B. (2018). Standards and guidelines for validating next-generation sequencing bioinformatics pipelines: A joint recommendation of the Association for Molecular Pathology and the College of American Pathologists. The Journal of Molecular Diagnostics, 20(1), 4–27

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

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

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