Validation of genetic variants from NGS data using deep convolutional neural networks

Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. A rigorous evaluation shows that the resulting model is robust and performs on par with trained researchers following published standard operating procedure.

Automated summary

Accurate somatic variant calling from next-generation sequencing data is crucial for personalised cancer therapy. A machine learning approach using a Convolutional Neural Network can improve the validation of genetic variants, incorporating contextual data from sequencing tracks. This model performs on par with trained researchers and enhances reproducibility and scalability.