Title: Nanopore Variant Calling using Deep Neural Networks

Kishwar Shafin, Graduate Student in the Computational Genomics Lab

Genomic sequencing of an individual genome produces millions of sequence reads. Once aligned to a reference genome, the reads can be used to identify genetic variations. Variant calling is essential in clinical genomics because genetic variants can be associated with genetic diseases. Next-generation short read sequencing technology is widely used for variant calling, but the short-reads are unable to solve complex regions of the genome. The third-generation long-read sequencing technology produces sequences that can span larger area in the genome which provides a better resolution in the complex areas of the genome. Although the third-generation long read sequencing technology like the Oxford Nanopore has a clear advantage, due to the error rate of the output sequences, the existing variant callers perform poorly. The Computational Genomics Lab (CGL) under the UCSC Genomics Institute is developing deep neural network based modules to enable analysis with noisy long reads. In this presentation, we will discuss various aspects of using deep neural networks to perform variant calling with noisy long read sequences.