Genomics Guide
Databricks provides tools and frameworks that bridge computer science and bioinformatics, designed for end-to-end analysis of genomics data at terabyte to petabyte scales.
Beta
The Databricks genomics applications require Databricks Runtime HLS, which is in Beta. Sign up for access.
In this guide:
Secondary Analysis
Databricks Runtime HLS contains pre-packaged pipelines to align reads and detect and annotate variants in individual samples, parallelized using Apache Spark.
ETL
Once variants have been detected in individuals, append data into Delta Lake tables. Then perform aggregate analyses to calculate cohort level quality control and summary statistics.
Tertiary Analysis
Once quality control is complete, perform population-scale statistical analyses of genetic variants.
Machine Learning
Databricks provides an environment for building, training, and deploying machine learning and deep learning models at scale. To learn more and find example notebooks, see Machine Learning, Deep Learning, and MLflow guides.
This section provides examples of machine learning algorithms applied to genomics data.