ArticleNucleic acids research2022
DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 27 citations in OpenAlex.
- Whole-exome sequencing in individuals with obsessive-compulsive disorder and chronic tic disorders identifies 36 large-effect risk genes.Nature neuroscience · 2026Article
- Review
- TriosCompass: a snakemake workflow for integrated detection of SNVs, indels, STRs, and structural de novo variants in parent-child trios.Bioinformatics (Oxford, England) · 2026Article
- Improved discovery of de novo mutations using TrioDNM and VRFS.GigaScience · 2026Article
- Systematic evaluation of de novo mutation calling tools using whole genome sequencing data.Briefings in bioinformatics · 2025Article
- Rare coding mutations identify 36 large-effect risk genes in obsessive-compulsive disorder and chronic tic disorders.medRxiv : the preprint server for health sciences · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- Efficient identification of de novo mutations in family trios: a consensus-based informatic approach.Life science alliance · 2025Article
- Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses.Nature medicine · 2025Article
- Chromosome 20p11.2 deletions cause congenital hyperinsulinism via the loss of FOXA2 or its regulatory elements.European journal of human genetics : EJHG · 2024Article
- Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.Cancer discovery · 2024Review
- Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain.bioRxiv : the preprint server for biology · 2024Article
- Automated Identification of GermlinebioRxiv : the preprint server for biology · 2024Article
- Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data.Scientific reports · 2023Article
- Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation.Nature communications · 2023Article
- Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data.Nucleic acids research · 2023Article
- Comprehensive de novo mutation discovery with HiFi long-read sequencing.Genome medicine · 2023Article
- De novo mutations in children born after medical assisted reproduction.Human reproduction (Oxford, England) · 2022Observational
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.