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Congenica v3.0 - Overcoming Challenges to Accelerate Variant Classification

Congenica v3.0 - Overcoming Challenges to Accelerate Variant Classification

12/9/2020    |    0 min read

Performing genomic analysis with Congenica

Manage Your Own Automated Variant Analysis Database

As more cases undergo genomic testing and the global supply of classified genomic data increases, reducing repetitive work is essential to scaling access to genomic analysis. Most genomics laboratories already report being at or near capacity[1]. To begin addressing this challenge, we have introduced Congenica Express to automatically classify and annotate your known variants.

Reduce your manual workload by up to 91% for 25-70% of cases

You told us you wanted more control over your databases and curated variant lists(CVLs),and greater data transparency to be able to see the contents of your CVLs. With Congenica version 3.0, we’re pleased to announce that this is now possible.

You can now manage your own curated variant data bases in the platform, including a simple upload template to get you started quickly and the ability to export the variants and their associated data.

In Congenica 3.0 you can also add newly classified variants to your curated variant database directly from your case workflow. This allows you to automate the interpretation of these variants in future cases.

The integrated management of curated variants databases combined with Congenica Express creates a foundation for reducing workloads in your lab by ensuring that you can automatically classify known variants and prepare them with comments for reporting in as little as 5-minutes.

Depending on the clinical area of study and data availability, with Congenica Express, you should be able to reduce your manual workload by up to 91% for 25-70% of cases.

Download the Congenica Express datasheet


Predict the Pathogenicity of Rare Genomic Variants with Confidence

For geneticists analyzing complex rare disease cases, understanding the role of rare genetic mutations in any given individual is often a significant project –researching literature on the gene, analyzing the genomic data, and trying to predict effects on proteins.

Over the years, many algorithms(generating in-silico scores)have been developed to accelerate this analysis by calculating predictions for which variants are likely pathogenic.

Congenica previously supported several commonly used in-silico tools, including PolyPhen & SIFT, as well as newer solutions such as SpliceAI, a leading predictor of aberrant splicing effects[2].

In Congenica version 3.0 you now benefit from added REVEL scores within the platform.

The REVEL algorithm is an ensemble predictor, combining results from over 10 distinct in-silico tools and was specifically developed for rare variants identified via NGS.

REVEL has been repeatedly shown to outperform over 20 other commonly used in-silico tools with greater accuracy for discriminating between pathogenic and neutral missense variants ranging in frequency from very rare to common[2, 3, 4].

The ClinGen Variant Curation SOP Committee and the UK’s Association for Clinical Genomic Science (ACGS) recommend REVEL for variant interpretation and classification according to ACMG guidelines(e.g. PP3 & BP4 criteria)[5,6]. For more information about the development of REVEL, see Ioannidis et al2016[7].

Using Congenica you now have access to SpliceAI, Congenica Splice Site Finder, and REVEL, providing the most accurate and comprehensive evidence available for geneticists analyzing rare genomic variants.

Download the Congenica Express datasheet


  1. American Board of Medical Genetics and Genomics, “Medical Genetics and Genomics” (2020). Available at: https://bit.ly/2H2S0gM.
  2. https://www.biorxiv.org/content/10.1101/781088v1
  3. Tian, Y., Pesaran, T., Chamberlin, A. et al. REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Sci Rep 9, 12752 (2019). https://doi.org/10.1038/s41598-019-49224-8
  4. Jinchen Li, Tingting Zhao, Yi Zhang, Kun Zhang, Leisheng Shi, Yun Chen, Xingxing Wang, ZhongshengSun, Performance evaluation of pathogenicity-computation methods for missense variants, Nucleic Acids Research, Volume 46, Issue 15, 6 September 2018, Pages 77937804, https://doi.org/10.1093/nar/gky678
  5. ClinGen Variant Curation SOP Committee. ClinGen General Sequence Variant Curation Process: Standard Operating Procedure Version 1.0. Published online April 2019. https://www.clinicalgenome.org/site/assets/files/3677/clingen_variant-curation_sopv1.pdf
  6. https://www.acgs.uk.com/media/10793/uk_practice_guidelines_for_variant_classification_2018_v10.pdf
  7. Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99(4):877-885. doi:10.1016/j.ajhg.2016.08.016