Title DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data /
Authors Khazeeva, Gelana ; Šablauskas, Karolis ; van der Sanden, Bart ; Steyaert, Wouter ; Kwint, Michael ; Rots, Dmitrijs ; Hinne, Max ; van Gerven, Marcel ; Yntema, Helger ; Vissers, Lisenka ; Gilissen, Christian
DOI 10.1093/nar/gkac511
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Is Part of Nucleic acids research.. Oxford : Oxford University Press. 2022, vol. 50, iss. 17, art. no. e97, p. [1-10].. ISSN 0305-1048. eISSN 1362-4962
Abstract [eng] 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.
Published Oxford : Oxford University Press
Type Journal article
Language English
Publication date 2022
CC license CC license description