MedGenome launches VarMiner

VarMiner is a novel machine learning-based analysis software to detect genetic variants for rare diseases and inherited cancers

MedGenome Labs has developed and launched VarMiner, an AI-enabled powerful variant interpretation software suite. This proprietary software will help clinicians, molecular geneticists, and Genome analysts to interpret and report actionable variants. VarMiner is powered by unique proprietary tools and databases to provide deeper insights into genetics with extreme accuracy and efficiency.


Dr Ravi Gupta, Vice President of Bioinformatics, MedGenome Labs, said, “Our solution streamlines the complex clinical report generation process and thus enables the diagnostics labs in India and globally to scale up the diagnostic reporting. With our validated solution on a large number of clinical samples, we believe it will further improve the diagnosis rate of rare Mendelian disorders, which has been a challenge in this field.”

 VarMiner supports various NGS Dx workflows

  • Germline Analysis – Covers all rare diseases, inherited cancers, Mitochondrial genome analysis, PGx and HLA analysis
  • Carrier/TRIO Analysis – Combined Analysis of familial samples to detect De-novo and common inherited variants and reporting
  • Somatic Analysis – Comprehensive analysis of cancer genomes with support for Liquid Biopsy, Hematology and Solid tumour cases

Commenting on the launch, Dr Vedam Ramprasad, CEO, MedGenome Labs, said, “We truly believe that VarMiner can help Molecular diagnostics labs in India and globally analyse and automate their NGS reports. While this is an initial version with high specificity, we are working on the next version that increases the algorithm’s sensitivity too.”

VarMiner is an efficient tool for detecting genomic variants in all rare diseases, and inherited cancers, as well as for conducting mitochondrial genome analysis, PGx and HLA analysis. It offers out-of-box clinically validated analysis workflows for germline, somatic and pre-natal NGS tests. Some of the key features that enable the core analysis are the ML-ranking of causal variants, symptoms and phenotype-based variant mapper, automated ACMG Classification, sample-variant quality metrics and advanced annotations.


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