Introducing GlowGR: An industrial-scale, ultra-fast and sensitive method for genetic association studies
Today, we announce that we are making a new whole genome regression method available to the open source bioinformatics community as part of Project Glow. Large cohorts of individuals with paired clinical and genome sequence data enable unprecedented insight into human disease biology. Population studies such as the UK Biobank, Genomics England, or Genome Asia...
Accelerating Somatic Variant Calling with the Databricks TNSeq Pipeline
Genetic analyses are a critical tool in revolutionizing how we treat cancer. By understanding the mutations present in tumor cells, researchers can gain clues that lead to drug targets and eventually new therapies. At the same time, genetic characterizations of individual tumors enables physicians to tailor treatments to individual patients and improve outcomes while reducing...
Introducing Glow: An Open-Source Toolkit for Large-Scale Genomic Analysis
The key to solving some of today’s most challenging medical problems lies in the analysis of genomics data. Understanding the impact of the minor changes in an individual’s genome on their overall health is fundamentally a data driven challenge that requires integration across hundreds of thousands of individuals. By analyzing genomes across large cohorts, researchers...
Parallelizing SAIGE Across Hundreds of Cores
As population genetics datasets grow exponentially, it is becoming impractical to work with genetic data without leveraging Apache Spark™. There are many ways to use Spark to derive novel insights into the role of genetic variation on disease processes. For example, Regeneron works directly on Spark SQL DataFrames, and the open-source Hail package can be...
Engineering population scale Genome-Wide Association Studies with Apache Spark™, Delta Lake, and MLflow
The advent of genome-wide association studies (GWAS) in the late 2000s enabled scientists to begin to understand the causes of complex diseases such as diabetes and Crohn’s disease at their most fundamental level. However, academic bioinformatics tools to perform GWAS have not kept pace with the growth of genomic data, which has been doubling globally...
Scaling Genomic Workflows with Spark SQL BGEN and VCF Readers
In the past decade, the amount of available genomic data has exploded as the price of genome sequencing has dropped. Researchers are now able to scan for associations between genetic variation and diseases across cohorts of hundreds of thousands of individuals from projects such as the UK Biobank. These analyses will lead to a deeper...
Building the Fastest DNASeq Pipeline at Scale
In June, we announced the Unified Analytics Platform for Genomics with a simple goal: accelerate discovery with a collaborative platform for interactive genomic data processing, analytics and AI at massive scale. In this post, we’ll go into more detail about one component of the platform: a scalable DNASeq pipeline that is concordant with GATK4 at...
Persistent Clusters: Simplifying Cluster Management for Analytics
Today we are excited to announce persistent clusters for analytics in Databricks. With persistent clusters, users no longer need to go through the laborious process of providing all the cluster and Apache Spark configuration every time they need a cluster. Admins can provide all the configurations to setup multiple clusters once and then allow users...
