By 2021, consultant firm Frost & Sullivan expects that artificial intelligence (AI) systems will generate $6.7 billion in revenue from healthcare globally. One area that machine learning is significantly evolving is genomics--the study of the complete set of genes within an organism. While much attention has been paid to the implications for human health, genetic sequencing and analysis could also be ground-breaking for agriculture and animal husbandry. When researchers can sequence and analyze DNA, something that artificial intelligence systems make faster, cheaper and more accurate, they gain perspective on the particular genetic blueprint that orchestrates all activities of that organism. With this insight, they can make decisions about care, what an organism might be susceptible to in the future, what mutations might cause different diseases and how to prepare for the future.

By 2021, consultant firm Frost & Sullivan expects that artificial intelligence (AI) systems will generate $6.7 billion in revenue from healthcare globally. One area that machine learning is significantly evolving is genomics--the study of the complete set of genes within an organism. While much attention has been paid to the implications for human health, genetic sequencing and analysis could also be ground-breaking for agriculture and animal husbandry. When researchers can sequence and analyze DNA, something that artificial intelligence systems make faster, cheaper and more accurate, they gain perspective on the particular genetic blueprint that orchestrates all activities of that organism. With this insight, they can make decisions about care, what an organism might be susceptible to in the future, what mutations might cause different diseases and how to prepare for the future.

CHICAGO (GenomeWeb) – Evidence published in the journal Nature Biotechnology in September demonstrated the efficacy of DeepVariant, Google's deep-learning-based variant caller, compared to older previous methods of calling genomic variants. DeepVariant "replaces the assortment of statistical modeling components with a single deep-learning model," according to the paper, whose authors represented Google and sister company Verily.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a suite of Python/C programs that run on any Unix-like operating system. For convenience the documentation refers to building and running DeepVariant on Google Cloud Platform, but the tools themselves can be built and run on any standard Linux computer, including on-premise machines. Note that DeepVariant currently requires Python 2.7 and does not yet work with Python 3. Pre-built binaries are available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you'll need a CPU (such as Intel Sandy Bridge) that supports them.

The brain's neural network has long inspired artificial-intelligence researchers.Credit: Alfred Pasieka/SPL/Getty Four years ago, scientists from Google showed up on neuroscientist Steve Finkbeiner's doorstep. The researchers were based at Google Accelerated Science, a research division in Mountain View, California, that aims to use Google technologies to speed scientific discovery. They were interested in applying'deep-learning' approaches to the mountains of imaging data generated by Finkbeiner's team at the Gladstone Institute of Neurological Disease in San Francisco, also in California. Deep-learning algorithms take raw features from an extremely large, annotated data set, such as a collection of images or genomes, and use them to create a predictive tool based on patterns buried inside. Once trained, the algorithms can apply that training to analyse other data, sometimes from wildly different sources.

Today, a teaspoon of spit and a hundred bucks is all you need to get a snapshot of your DNA. It's exactly the kind of problem that makes sense to outsource to artificial intelligence. On Monday, Google released a tool called DeepVariant that uses deep learning--the machine learning technique that now dominates AI--to identify all the mutations that an individual inherits from their parents.1 Modeled loosely on the networks of neurons in the human brain, these massive mathematical models have learned how to do things like identify faces posted to your Facebook news feed, transcribe your inane requests to Siri, and even fight internet trolls. And now, engineers at Google Brain and Verily (Alphabet's life sciences spin-off) have taught one to take raw sequencing data and line up the billions of As, Ts, Cs, and Gs that make you you. And oh yeah, it's more accurate than all the existing methods out there.

Google on Monday released DeepVariant, an artificial intelligence tool that uses gene sequencing data to build a more accurate genomic model. Google on Monday released DeepVariant, an artificial intelligence (AI) tool that uses gene sequencing data to build a more accurate genomic model by automatically spotting small insertion and deletion mutations and single-base-pair mutations. The Google Brain team compiled millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle project, feeding the data to a deep-learning system and modifying the parameters of the model until it learned to interpret sequenced data with very high accuracy. "DeepVariant...demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems," says Deep Genomics CEO Brendan Frey. Frey predicts AI will ultimately transcend its ability to help sequence genomic data.

Today, a teaspoon of spit and a hundred bucks is all you need to get a snapshot of your DNA. But getting the full picture--all 3 billion base pairs of your genome--requires a much more laborious process. It's exactly the kind of problem that makes sense to outsource to artificial intelligence. On Monday, Google released a tool called DeepVariant that uses deep learning--the machine learning technique that now dominates AI--to assemble full human genomes. Modeled loosely on the networks of neurons in the human brain, these massive mathematical models have learned how to do things like identify faces posted to your Facebook news feed, transcribe your inane requests to Siri, and even fight internet trolls.

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.