DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data
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Abstract
De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160$ \times$164 resolution images. DeNovoCNN was trained on DNMs of 5616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.
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AI is used in nearly every step of next-generation sequencing processing and interpretation, including mutation identification, artifact filtering, and driver gene identification.
“This includes the use of AI in nearly every step of next-generation sequencing (NGS) processing and interpretation, including mutation identification (122, 123), artifact filtering (124), and driver gene identification (125).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [122]
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- Authors
- Khazeeva, G., Sablauskas, K., van der Sanden, B., Steyaert, W., Kwint, M., Rots, D., Hinne, M., van Gerven, M., Yntema, H., Vissers, L., & Gilissen, C.
- Affiliations
- Radboud University Nijmegen (NL)
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University , Vilnius , Lithuania
- Department of Human Genetics, Donders Centre for Neuroscience, Radboud University Medical Center , Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
- Radboud University Medical Center (NL)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen , The Netherlands
- Corresponding author
- Gilissen, Christian
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-06-17
- Volume
- 50
- Issue
- 17
- Pages
- e97-e97
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC9508836
- OpenAlex
- W4283068733
- Cited by
- 30
- References
- 23
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 9 · 2023: 6 · 2022: 1
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Netherlands Organisation for Scientific Research (917-17-353)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- de novo mutations, convolutional neural networks, trio analysis, exome sequencing, whole genome sequencing, rare diseases
- MeSH terms
- Exome Sequencing, Deep Learning, Sequence Analysis, DNA, High-Throughput Nucleotide Sequencing
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