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DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin
Dong-Hyuk Heo1, Inyoung Kim1, Heejae Seo1
1Theragen Bio Co., Ltd., Seongnam, Gyeonggi-do, 13488, Republic of Korea.
Scientific Reports
|January 31, 2024
Summary
A new AI model, DEEPOMICS FFPE, accurately distinguishes true genetic variants from artifacts in formalin-fixed, paraffin-embedded (FFPE) tissues. This tool improves next-generation sequencing analysis by preserving low-frequency variants crucial for diagnostics and research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Formalin-fixed, paraffin-embedded (FFPE) tissues are vital for pathology but introduce artifactual mutations.
- Existing tools struggle to differentiate true variants from FFPE artifacts, especially at low allele frequencies.
- Accurate variant identification is critical for companion diagnostics and next-generation sequencing (NGS) data analysis.
Purpose of the Study:
- To develop DEEPOMICS FFPE, an artificial intelligence (AI) model for classifying true variants from FFPE artifacts.
- To improve the accuracy of variant detection in FFPE samples for NGS analysis.
- To provide a tool that can identify subclonal variants and aid in personalized medicine applications.
Main Methods:
- Utilized paired whole exome sequencing data from fresh frozen and FFPE tumor samples for training and validation.
- Developed a deep neural network model incorporating features from MuTect2 caller outputs.
- Employed SHapley Additive exPlanations (SHAP) for feature identification and optimization.
- Compared DEEPOMICS FFPE performance against existing tools like MuTect filter, FFPolish, and SOBDetector.
Main Results:
- DEEPOMICS FFPE achieved an F1-score of 88.3, removing 99.6% of artifacts while retaining 87.1% of true variants.
- Demonstrated high specificity (0.995) for low-allele-fraction variants, enabling subclonal variant identification.
- Outperformed existing FFPE artifact filtering tools in accuracy and variant retention.
Conclusions:
- DEEPOMICS FFPE effectively identifies sequencing artifacts in FFPE samples while preserving true variants, including low-frequency ones.
- The tool shows significant potential for designing capture panels for circulating tumor DNA assays and identifying neoepitopes for personalized vaccines.
- DEEPOMICS FFPE is freely available online, facilitating research in personalized oncology.
