Google has released an AI tool that makes sense of your genome
Almost 15 years after scientists first sequenced the human genome, making sense of the enormous amount of data that encodes human life remains a formidable challenge. But it is also precisely the sort of problem that machine learning excels at.
On Monday, Google released a tool called DeepVariant that uses the latest AI techniques to build a more accurate picture of a person’s genome from sequencing data.
DeepVariant helps turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data.
High-throughput sequencing became widely available in the 2000s and has made genome sequencing more accessible. But the data produced using such systems has offered only a limited, error-prone snapshot of a full genome. It is typically challenging for scientists to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. These mutations may be directly relevant to diseases such as cancer.
A number of tools exist for interpreting these readouts, including GATK, VarDict, and FreeBayes. However, these software programs typically use simpler statistical and machine-learning approaches to identifying mutations by attempting to rule out read errors.
“One of the challenges is in difficult parts of the genome, where each of the [tools] has strengths and weaknesses,” says Brad Chapman, a research scientist at Harvard’s School of Public Health who helped develop DeepVariant. “These difficult regions are increasingly important for clinical sequencing, and it’s important to have multiple methods.”
Chapman worked with researchers from the Google Brain team, a group that focuses on developing and applying AI techniques; Verily, another Alphabet subsidiary that is focused on the life sciences; and Andrew Carroll, vice president of DNAnexus, a cloud platform for the storage of genomic data.
The team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB) project, a public-private effort to promote genomic sequencing tools and techniques. They fed the data to a deep-learning system and painstakingly tweaked the parameters of the model until it learned to interpret sequenced data with a high level of accuracy.
