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.

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As genetic testing is being proven useful and used in ever more areas of medicine where it can instruct disease prevention, medical care, and disease monitoring, there is a concomitant longer term accumulation of experience taking place in the background. Both fundamental research and clinical work can benefit from the value of this growing knowledge when it is structured and exposed to users as well as algorithms so that they can make proper use of it. In the specific context of genetic variant interpretation, it has been evidenced that a combination of a knowledge base with analytical tools can quickly grow the expertise of medical organisation, whether in clinical research, practice, or diagnostics.

Changing the perspective of variant analysis to a cohort-based from a patient-based one opens possibilities to find associations between patient cases or conditions on one hand and gene/genomic variant observations on the other. GWAS (genome-wide association studies) is an old and still valid data analysis approach frequently used in research and based on SNP array technologies. NGS (next generation sequencing) has made cohort analysis more powerful in the sense that the coverage is complete over the target regions, which can also be more focused. Contrary to GWAS, NGS also brings the data to the diagnostic laboratories, which are thus empowered to look at the accumulating information in view of improving their detection and reporting capabilities.

Euformatics has developed a knowledge base structure for genomic variants, a classification of samples into cohorts, and a workbench for accessing this information from a cohort-based perspective in addition to the standard per-sample perspective used in diagnostics. The cohort analytics has been designed to greatly reduce the effort involved in finding variants characteristic for a cohort. The analytics workbench – see image below – provides access to the same ca 150 variant annotation filters available also in the clinical diagnostic mode of the omnomicsNGS variant interpretation module, allowing different kinds of exploratory modes simultaneously targeting multiple samples.

Overall, the Genomics Hub has been designed with modules to create an end-to-end solution that can start from the sequencer raw data output and end with a tailored clinical report. Automated comprehensive quality control is included to allow the monitoring of both the bioinformatic pipeline and the wet lab steps. The cohort analytics helps users to evaluate variants over groups of samples and identify intricate connections between variants and associated clinical syndromes. Be confident, and keep pace as the genomic world evolves with the Genomics Hub. Reach out to us to schedule a demo.

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CNV analysis is recognised as a challenging area within clinical genomics. Discerning CNVs from biological or technical noise, particularly with short-read sequencing, which is still the most popular sequencing technology, is not obvious. Particularly CNVs nested within repetitive genomic regions or those of considerable size pose problems (10.1093/bioinformatics/bts535). Bioinformatic analysis requires specialised computational tools and algorithms (10.1038/s41431-020-0675-z). Also interpretation of CNVs poses problems: the biological effect of variety in the extension or limits of CNV boundaries is not clear, particularly when considering effects depending on multiple genes and intraregional co-evolution of gene function.

To help standardise the classification part of the analyses, a joint consensus recommendation was published in 2020 by the American College of Medical Genetics and Genomics and the Clinical Genome Resource (10.1038/s41436-019-0686-8). It improves the quality of CNV interpretation in clinical settings by addressing critical aspects of CNV evaluation. The article includes assessment methodologies, extensive variant classification criteria, and effective communication of findings to clinicians and patients. Following these recommendations, Euformatics developed a CNV workbench, which is now available in the Genomics Hub.

The CNV workbench has been designed to greatly reduce the effort involved in CNV classification by comprehensively calculating the ACMG/ClinGen classification score and allowing to visualise the result in compact (Figure 1) as well as fully documented mode. The recommendation system automatically evaluates CNV from several angles, such as size, genomic content, literature evidence, and inheritance patterns. In practice, larger CNVs stand out as they are more likely to have a phenotypic impact. The gene content of the CNVs is also considered, as those that encompass known disease-associated genes are of particular interest. A detailed evaluation of genomic content using published research and public databases is done automatically alongside the evaluation of inheritance patterns and family history.

Figure 1. Genomics Hub’s CNV workbench.

Overall, the Genomics Hub has been designed with modules to create an end-to-end solution that can start from the sequencer raw data output and end with a tailored clinical report. Automated comprehensive quality control is included to allow the monitoring of both the bioinformatic pipeline and the wet lab steps. The CNV workbench helps users to effortlessly evaluate CNVs and identify intricate connections between CNVs and associated clinical syndromes. Be confident, and keep pace as the genomic world evolves with the Genomics Hub.

Reach out to us to schedule a demo:

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GnomAD 3.1.2

When mining through genomic variants, it can often be a challenge to filter out or narrow the focus onto the most severe mutations. On the assumption that causative or pathogenic variants are expected to be absent or appear very infrequently in the general population, evaluating the rarest variations is critical to the effectiveness of variant filtering on the path towards diagnosis.

Revered for their efforts in compiling resources of allele frequencies in specified populations, the GnomAD Consortium’s latest version 3.1.2 builds upon previous iterations to provide the most up-to-date collection of allele frequencies. Some stand-out features of this version include frequencies for the Middle East and Amish populations, as well as coverage for the human genome build GRCh38.

OmnomicsNGS further transforms the population data so that it is available for the equivalent variants on GRCh37, as well as calculating and providing the homozygous as well as heterozygous ‘carrier’ frequency of each variant. Now, the tools are in your hands with access to ever growing filterable metrics & data integrations to get most out of your data.

Traceability

In order to create a paper-trail of actions and methods taken in the working process, a new feature has been added to the omnomicsNGS that allows for the creation of checklists. Users can define a workflow for their own process, and tick off actions which are then timestamped. In doing so, users can be reminded where they left off should they leave their analysis for a period of time, or simply remind themselves of tasks in the analyses.

Routine Maintenance

In addition to introducing GnomAD 3.1.2, omnomicsNGS also received a scheduled upgrade of its other databases, including the Online Mendelian Inheritance in Man (OMIM), The CIViC database, and the Clinical Genomic Database (CGD).

Users may see an increase in the number of annotations from the CIViC database. Formerly, we used to only import annotations that had been reviewed and approved by the community. Now, we further show those annotations under evaluation by the community, so that users can assess themselves. 

All these database updates revealed that the time of data import to omnomicsNGS is important in the analysis process. Now the database versions and their date of accession are displayed and can be added to the user reports. 

In a nutshell, omnomicsNGS version 2.7 has intensified the flexibility putting the power in your hands to streamline any clinical domain your lab is expanding into.

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WGS filtering and analysis

Users of omnomicsNGS will already know that they have had the capability to handle WGS samples for a while but knowing where to start with a file that can contain millions of variants is often a challenge.

omnomicsNGS now features the ability to perform hard-filtering on samples before proceeding to analysis, allowing users to quickly filter out unwanted variants from the sample early in the process. Filtering can be done through setting a population frequency cut-off, uploading a BAM file containing a specified region, such as exomes, user-created gene panels or through phenotype associations using HPO terms.

The addition of this feature increases the speed that WGS samples can be analysed in the system, as it removes the need for frequent reannotations and allows users to only analyse the variants that are of most importance for the case at hand.

Multiple analyses per sample, saved in time

With the hard-filtering changes comes a new way of analysing samples in omnomicsNGS. Users can now create multiple analyses for the same sample and come back to them later or share with a colleague who also has access to the system. This gives you a much more flexible way of analysing samples in the software and offers a clearer way of navigating through the tool to get from VCF to clinical report.

It also means that the annotations associated with a specific analysis of a sample are stored as a snapshot in time and can be compared with later analyses using updated databases and annotations. For users who may need to re-analyse samples periodically as part of their quality management, this feature makes the comparison of samples and annotations possible.

Did you spot the Easter Egg?

The release also fixes several minor bugs, as well as an Easter Egg which some users may have noticed… In the list of integrated databases, Ensemble was listed instead of Ensembl. This has now been corrected, but if we have managed to miss any other small spelling errors, please do let us know! You can always drop us a message at contact@euformatics.com

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