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Open Access

DeepFilter: A Deep Learning Based Variant Filter for VarDict

School of Software, Shandong University, Jinan 250100, China
Shenzhen Research Institute of Shandong University, Shenzhen 518057, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Institute for Computer Science, Johannes Gutenberg University, Mainz 55128, Germany
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With the development of sequencing technologies, somatic mutation analysis has become an important component in cancer research and treatment. VarDict is a commonly used somatic variant caller for this task. Although the heuristic-based VarDict algorithm exhibits high sensitivity and versatility, it may detect higher amounts of false positive variants than callers, limiting its clinical practicality. To address this problem, we propose DeepFilter, a deep-learning based filter for VarDict, which can filter out the false positive variants detected by VarDict effectively. Our approach trains two models for insertion-deletion mutations (InDels) and single nucleotide variants (SNVs), respectively. Experiments show that DeepFilter can filter at least 98.5% of false positive variants and retain 93.5% of true positive variants for InDels and SNVs in the commonly used tumor-normal paired mode. Source code and pre-trained models are available at


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Tsinghua Science and Technology
Pages 665-672
Cite this article:
Zhang H, Yin Z, Wei Y, et al. DeepFilter: A Deep Learning Based Variant Filter for VarDict. Tsinghua Science and Technology, 2023, 28(4): 665-672.








Web of Science






Received: 26 February 2022
Revised: 27 April 2022
Accepted: 22 August 2022
Published: 06 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (