Deletion genotype calling on the basis of sequence visualisation and image classification. (2018)

• Record Type: Journal Article
• Authors: Wang, Jing; Gao, Jingyang; Ling, Cheng
• Is Part Of: International journal of data mining and bioinformatics. Volume 20:Number 2(2018)
• Page Start: 109
• Page End: 122
• Publication Date: 2018
• Subjects: deletion -- genotype calling -- convolutional neural network -- visualisation -- image classification; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Widely known genotype calling methods, such as CNVnator, Pindel, and LUMPY, are restricted in terms of detectable length ranges and sequence coverage. Focusing on deletions larger than 50 bp, we propose a new approach with two main steps: (1) visualising images of deletions and (2) conducting deletion genotypes classification. Given the coordinates of candidates, this method first generates breakpoint images by fetching reads from BAM files. Convolutional neural networks then perform genotype recognition. We test our approach on both low and high coverage simulated noisy data and compare the results to those of CNVnator, Pindel, and LUMPY. The results indicate our approach surpasses other tools with higher accuracy, wider detectable deletion length range, and better performance on both low and high coverage data. To summarise, our approach not only provides an intuitive image view of deletion regions, but also achieves better results for genotype calling compared to existing tools.