Genomic Deletion holds the largest proportion of the structural variation (SV). There are many methods for detection of SVs using next-generation data, such as Pindel, SVseq2, BreakDancer, DELLY and so on. However, each method has advantages on only some kind of SVs. For deletions, existing tools usually produce variations with accurate results under 0.5. In this paper, we present CNNdel, a tool based on shallow convolutional neural network to detect genomic deletions with real data from the 1000 Genomes Project. The experimental results show that the accuracy and sensitivity is both improved compared with other existing methods.
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