DeepVariant: Highly Accurate Genomes With Deep Neural Networks
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds, sample preparations, and sequencing instruments. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Cory McLean is a Senior Software Engineer on the genomics team at Google Brain. His research interests include the development and application of machine learning methods to genomics and the biology of human health. Recent projects include efforts to use deep learning image classification methods to improve the detection of genetic variation from high-throughput sequencing data and to interpret cellular morphology and function. Before joining Google, Cory was a research scientist at 23andMe where he studied Parkinson's disease and population genetics. He received his B.S. and M.Eng. in computer science from MIT and his M.S. and Ph.D. in computer science from Stanford University.