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KMID : 0620920230550081734
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Experimental & Molecular Medicine
2023 Volume.55 No. 8 p.1734 ~ p.1742
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AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples
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Jeon Hyeon-Seong
Ahn Jun-Hak
Na Byung-Gook
Shin You-Seop
Lee Sa-el
Kim Sun
Yoon Sung-Roh
Baek Dae-Hyun
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Abstract
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The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth.
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KEYWORD
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Cancer, Data processing,
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