OncoGen.AI: an integrated platform for automated genomic analysis and reporting in precision oncology
- 1Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- 2Rajiv Gandhi Cancer Institute & Research Center
Abstract
The translation of complex cancer genomics into clinically actionable insights is a critical bottleneck in precision oncology. To automate the entire workflow, we present OncoGen.AI, an end-to-end, containerized platform that automates the process from raw sequencing data to a final clinical report. The platform integrates platform-agnostic pipelines (Illumina/Ion Torrent) for variant calling, copy number variation (CNV), and tumor mutational burden (TMB) analysis. Its core innovation is a clinically relevant Knowledge Graph comprising 20,242,772 triples across 11 node types, integrating data from domain-specific databases such as COSMIC, ClinVar, and others for evidence-based interpretation. The workflow culminates in a structured clinical report automatically generated by Google Gemini on the curated evidence extracted by the Knowledge Graph. When benchmarked against a diverse cohort of 13 clinical tumor exomes and validated across seven publicly available datasets, OncoGen.AI recovered 100% of pathogenic mutations identified by proprietary software (DRAGEN and Torrent Suite). Furthermore, in comparative analyses against other leading tools, OncoGen.AI demonstrated superior sensitivity, uniquely identifying clinically significant mutations in challenging Ion Torrent datasets that were otherwise missed. By providing a transparent “glass box” solution that bridges the gap between raw data and clinical action, OncoGen.AI is a comprehensive tool poised to accelerate reproducible decision-making in precision oncology.
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BibTeX
@article{shome2026oncogenai,
title = {{OncoGen.AI: an integrated platform for automated genomic analysis and reporting in precision oncology}},
author = {Raidhani Shome and Suvendu Kumar and Sanjay Kumar Mohanty and Dhwani Dholakia and Arushi Sharma and Saveena Solanki and Sakshi Sharma and Sonam Chauhan and Shiva Satija and Himanshi Diwan and Manoj Kumar Panigrahi and Juhi Tayal and Anurag Mehta and Gaurav Ahuja},
journal = {npj Precision Oncology},
year = {2026},
month = jun,
doi = {10.1038/s41698-026-01578-9},
url = {https://doi.org/10.1038/s41698-026-01578-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - OncoGen.AI: an integrated platform for automated genomic analysis and reporting in precision oncology
AU - Raidhani Shome
AU - Suvendu Kumar
AU - Sanjay Kumar Mohanty
AU - Dhwani Dholakia
AU - Arushi Sharma
AU - Saveena Solanki
AU - Sakshi Sharma
AU - Sonam Chauhan
AU - Shiva Satija
AU - Himanshi Diwan
AU - Manoj Kumar Panigrahi
AU - Juhi Tayal
AU - Anurag Mehta
AU - Gaurav Ahuja
PY - 2026
DA - 2026/06/01
JO - npj Precision Oncology
DO - 10.1038/s41698-026-01578-9
UR - https://doi.org/10.1038/s41698-026-01578-9
AB - The translation of complex cancer genomics into clinically actionable insights is a critical bottleneck in precision oncology. To automate the entire workflow, we present OncoGen.AI, an end-to-end, containerized platform that automates the process from raw sequencing data to a final clinical report. The platform integrates platform-agnostic pipelines (Illumina/Ion Torrent) for variant calling, copy number variation (CNV), and tumor mutational burden (TMB) analysis. Its core innovation is a clinically relevant Knowledge Graph comprising 20,242,772 triples across 11 node types, integrating data from domain-specific databases such as COSMIC, ClinVar, and others for evidence-based interpretation. The workflow culminates in a structured clinical report automatically generated by Google Gemini on the curated evidence extracted by the Knowledge Graph. When benchmarked against a diverse cohort of 13 clinical tumor exomes and validated across seven publicly available datasets, OncoGen.AI recovered 100% of pathogenic mutations identified by proprietary software (DRAGEN and Torrent Suite). Furthermore, in comparative analyses against other leading tools, OncoGen.AI demonstrated superior sensitivity, uniquely identifying clinically significant mutations in challenging Ion Torrent datasets that were otherwise missed. By providing a transparent “glass box” solution that bridges the gap between raw data and clinical action, OncoGen.AI is a comprehensive tool poised to accelerate reproducible decision-making in precision oncology.
SN - 2397-768X
ER -