Indel calling from ONT sequencing data of family trios via sparse attention and 3D convolution.

Abstract

Accurate calling of parental-child SNPs and Indels in family trios is very helpful for understanding genetic traits and diseases. Indel calling is even more important than SNP calling, as Indels may have led to substantial changes in protein structures that affect more of the traits of the organism. However, the best Indel calling methods have recall rates below 85%, precision below 92%, and F1 below 88% on $60\times $ ONT Q20 data, much lower than their SNP calling's recall performance of 99.87%, precision of 99.86%, and F1 of 99.86%. Difficulties in Indels calling include how to distinguish sequencing errors from genuine Indels and how to optimize the Mendelian genetic model. This work proposes sparse attention learning for high-performance calling of Indels from family-trios' ONT long-read sequencing data, while still maintaining exceptional performance on SNP calling. Key steps include a sparsely connected attention network to convert fully aligned data cubes into essential features, and a deep learning on these features via ResNet and 3D convolutional blocks to enable accurate detection of family-trio variants. This attention network is in fact a dual attention network to aggregate both channel and spatial information, capable of selecting sub-cubes of critical channels and base locations that are resistant to the confounding effects of sequencing errors. Comparing with the current best-performing trio-variant detection method, our F1 is 5.6%-14.19% higher, recall is 7.07%-18.67% higher, and precision is 3.85%-7.87% higher on ONT Q20 datasets. Case studies of indel-dense regions in chromosome 20, including the centromere and disease-associated genes, demonstrate the significant impact of indel variations on disease pathogenesis, providing novel perspectives for future personalized and targeted therapies.