Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/human-longevity-inc-changing-medicine-through-genomics-research/
Human Longevity, Inc. (HLI) is at the forefront of genomics research and wants to build the world’s largest database of human genomes along with related phenotype and clinical data, all in support of preventive healthcare. In today’s guest post, Yaron Turpaz, Bryan Coon, and Ashley Van Zeeland, talk about how they are using AWS to store the massive amount of data that is being generated as part of this effort to revolutionize medicine.
When Human Longevity, Inc. launched in 2013, our founders recognized the challenges that lie ahead. A genome contains all the information needed to build and maintain an organism; in humans, a copy of the entire genome, which contains more than three billion DNA base pairs, is contained in all cells that have a nucleus. Our goal is to sequence one million genomes and deliver that information—along with integrated health records and disease-risk models—to researchers and physicians. They, in turn, can interpret the data to provide targeted, personalized health plans and identify the optimal treatment for cancer and other serious health risks far earlier than has been possible in the past. The intent is to transform medicine by fostering preventive healthcare and risk prevention in place of the traditional “sick care” model, when people wind up seeing their doctors only after symptoms manifest.
Our work in developing and applying large-scale computing and machine learning to genomics research entails the collection, analysis, and storage of immense amounts of data from DNA-sequencing technology provided by companies like Illumina. Raw data from a single genome consumes about 100 gigabytes; that number increases as we align the genomic information with annotation and phenotype sources and analyze it for health insights.
From the beginning, we knew our choice of compute and storage technology would have a direct impact on the success of the company. Using the cloud was clearly the best option. We’re experts in genomics, and don’t want to spend resources building and maintaining an IT infrastructure. We chose to go all in on AWS for the breadth of the platform, the critical scalability we need, and the expertise AWS has developed in big data. We also saw that the pace of innovation at AWS—and its deliberate strategy of keeping costs as low as possible for customers—would be critical in enabling our vision.
Leveraging the Range of AWS Services
Today, we’re using a broad range of AWS services for all kinds of compute and storage tasks. For example, the HLI Knowledgebase leverages a distributed system infrastructure comprised of Amazon S3 storage and a large number of Amazon EC2 nodes. This helps us achieve resource isolation, scalability, speed of provisioning, and near real-time response time for our petabyte-scale database queries and dynamic cohort builder. The flexibility of AWS services makes it possible for our customized Amazon Machine Images and pre-built, BTRFS-partitioned Amazon EBS volumes to achieve turn-up time in seconds instead of minutes. We use Amazon EMR for executing Spark queries against our data lake at the scale we need. AWS Lambda is a fantastic tool for hooking into Amazon S3 events and communicating with apps, allowing us to simply drop in code with the business logic already taken care of. We use Auto Scaling based on demand, and AWS OpsWorks for managing a Docker pipeline.
We also leverage the cost controls provided by Amazon EC2 Spot and Reserved Instance types. When we first started, we used on-demand instances, but the costs started to grow significantly. With Spot and Reserved Instances, we can allocate compute resources based on specific needs and workflows. The flexibility of AWS services enables us to make extensive use of dockerized containers through the resource-management services provided by Apache Mesos. Hundreds of dynamic Amazon EC2 nodes in both our persistent and spot abstraction layers are dynamically adjusted to scale up or down based on usage demand and the latest AWS pricing information. We achieve substantial savings by sharing this dynamically scaled compute cluster with our Knowledgebase service and the internal genomic and oncology computation pipelines. This flexibility gives us the compute power we need while keeping costs down. We estimate these choices have helped us reduce our compute costs by up to 50 percent from the on-demand model.
We’ve also worked with AWS Professional Services to address a particularly hard data-storage challenge. We have genomics data in hundreds of Amazon S3 buckets, many of them in the petabyte range and containing billions of objects. Within these collections are millions of objects that are unused, or used once or twice and never to be used again. It can be overwhelming to sift through these billions of objects in search of one in particular. It presents an additional challenge when trying to identify what files or file types are candidates for the Amazon S3-Infrequent Access storage class. Professional Services helped us with a solution for indexing Amazon S3 objects that saves us time and money.
Moving Faster at Lower Cost
Our decision to use AWS came at the right time, occurring at the inflection point of two significant technologies: gene sequencing and cloud computing. Not long ago, it took a full year and cost about $100 million to sequence a single genome. Today we can sequence a genome in about three days for a few thousand dollars. This dramatic improvement in speed and lower cost, along with rapidly advancing visualization and analytics tools, allows us to collect and analyze vast amounts of data in close to real time. Users can take that data and test a hypothesis on a disease in a matter of days or hours, compared to months or years. That ultimately benefits patients.
Our business includes HLI Health Nucleus, a genomics-powered clinical research program that uses whole-genome sequence analysis, advanced clinical imaging, machine learning, and curated personal health information to deliver the most complete picture of individual health. We believe this will dramatically enhance the practice of medicine as physicians identify, treat, and prevent diseases, allowing their patients to live longer, healthier lives.