We're very pleased to welcome the 1000 Genomes Project data to Amazon S3.
The original human genome project was a huge undertaking. It aimed to identify every letter of our genetic code, 3 billion DNA bases in total, to help guide our understanding of human biology. The project ran for over a decade, cost billions of dollars and became the corner stone of modern genomics. The techniques and tools developed for the human genome were also put into practice in sequencing other species, from the mouse to the gorilla, from the hedgehog to the platypus. By comparing the genetic code between species, researchers can identify biologically interesting genetic regions for all species, including us.
A few years ago there was a quantum leap in the technology for sequencing DNA, which drastically reduced the time and cost of identifying genetic code. This offered the promise of being able to compare full genomes from individuals, rather than entire species, leading to a much more detailed genetic map of where we, as individuals, have genetic similarities and differences. This will ultimately give us better insight into human health and disease.
The 1000 Genomes Project, initiated in 2008, is an international public-private consortium that aims to build the most detailed map of human genetic variation available, ultimately with data from the genomes of over 2,661 people from 26 populations around the world. The project began with three pilot studies that assessed strategies for producing a catalog of genetic variants that are present at one percent or greater in the populations studied. We were happy to host the initial pilot data on Amazon S3 in 2010, and today we're making the latest dataset available to all, including results from sequencing the DNA of approximately 1,700 people.
The data is vast (the current set weighs in at over 200Tb), so hosting the data on S3 which is closely located to the computational resources of EC2 means that anyone with an AWS account can start using it in their research, from anywhere with internet access, at any scale, whilst only paying for the compute power they need, as and when they use it. This enables researchers from laboratories of all sizes to start exploring and working with the data straight away. The Cloud BioLinux AMIs are ready to roll with the necessary tools and packages, and are a great place to get going.
Making the data available via a bucket in S3 also means that customers can crunch the information using Hadoop via Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.