The Personal Genome: Everyone Has One
The advent of the “personal genome” is revolutionizing how we derive medical insight from whole-genome DNA sequencing for individuals. Sequencing millions of personal genomes integrated with phenotype (observable (BMI) versus inherited (eye color) characteristics) and personal medical data creates data storage and analytical challenges on an unprecedented scale. Researchers are developing a new generation of algorithms to overcome limitations of approaches that rely on mapping sequences to one consensus human reference genome, which is not a “one-size-fits-all” solution to medical research, as it was built from few individuals - resulting in missing data and large discrepancies when applied to diverse populations. Having reference genomes that are customized for ancestry, phenotype and disease state can provide more specific insights that lead to effective treatments. The ability to store and analyze multiple reference genomes on the cloud will be a fundamental building block in this new research paradigm.
This demonstration takes attendees on a journey from the familiar to the possible by deep-diving into some of the latest tools available in the cloud stratosphere to support data-driven discovery for “personal genomics” by optimizing the use of new and existing analytics tools for researchers. This demonstration provides best practices to get the most out of databases for genomics applications and simple ML to examine cohorts and populations using pan-genomes, a compilation of a representative genome for a given population. We’ll also explore the potential of AI and ML high-throughput data analysis tools to speed up research insights from personal genomes.