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Background <p>Multiple different evidence types as well as gene-specific variant classification guidelines need to be considered during the classification of variants, making the process complex. Therefore, tools that support variant classification by experts are urgently needed.</p> Methods <p>We present HerediVar a web application and HerediClassify a variant classification algorithm. The performance of HerediClassify was validated and compared to other variant classification tools. HerediClassify implements 19/28 variant classification criteria by the American College of Medical Genetics and gene-specific recommendations for <i>ATM</i>, <i>BRCA1</i>, <i>BRCA2</i>, <i>CDH1</i>, <i>PALB2</i>, <i>PTEN</i>, and <i>TP53</i>.</p> Results <p>HerediVar offers modular annotation services and allows for collaboration in the classification of variants. On the validation dataset, HerediClassify shows an average F1-Score of 93% across all criteria. HerediClassify outperforms other automated variant classification tools like vaRHC and Cancer&#xa0;SIGVAR.</p> Conclusion <p>In HerediVar and HerediClassify we present a powerful solution to support variant classification in HBOC. Through their modular design, HerediVar and HerediClassify are easily extendable to other use cases and human genetic diagnostics as a whole.</p>

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HerediVar and HerediClassify: tools for streamlining genetic variant classification in hereditary breast and ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Anna-Lena Katzke,
  • Marvin Doebel,
  • Jan Hauke,
  • Gunnar Schmidt,
  • Marc Sturm

摘要

Background

Multiple different evidence types as well as gene-specific variant classification guidelines need to be considered during the classification of variants, making the process complex. Therefore, tools that support variant classification by experts are urgently needed.

Methods

We present HerediVar a web application and HerediClassify a variant classification algorithm. The performance of HerediClassify was validated and compared to other variant classification tools. HerediClassify implements 19/28 variant classification criteria by the American College of Medical Genetics and gene-specific recommendations for ATM, BRCA1, BRCA2, CDH1, PALB2, PTEN, and TP53.

Results

HerediVar offers modular annotation services and allows for collaboration in the classification of variants. On the validation dataset, HerediClassify shows an average F1-Score of 93% across all criteria. HerediClassify outperforms other automated variant classification tools like vaRHC and Cancer SIGVAR.

Conclusion

In HerediVar and HerediClassify we present a powerful solution to support variant classification in HBOC. Through their modular design, HerediVar and HerediClassify are easily extendable to other use cases and human genetic diagnostics as a whole.