Improving the accuracy of genomic analysis
Sequencing genomes enables us to identify variants in a person’s DNA that indicate genetic disorders such as an elevated risk for breast cancer.
Highly accurate genomes with deep neural networks
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. As published in Nature Biotechnology, DeepVariant, an open-source variant caller that uses a deep neural network to call genetic variants from next-generation DNA sequencing data, significantly improves the accuracy in identifying variant locations, reducing the error rate by more than 50%. Learn more
Identifying disease-causing variants in cancer patients
Researchers wanted to understand if incorporating automated deep learning technology would improve the detection of disease-causing variants in patients with cancer. In a cross-sectional study published in JAMA of 2,367 prostate cancer and melanoma patients in the US and Europe, DeepVariant found disease-causing variants in 14% more individuals than prior state-of-the-art methods.
Building large-scale cohorts for genetic discovery research
Large cohorts of sequenced individuals are the foundations for discovery of novel genetic associations with disease. We developed best practices for generating cohorts that substantially improves over previous methods, which has been adopted by the UK Biobank for its large-scale sequencing efforts. Read the article