Studying Very Large Genomic Datasets

 Note addressing very large datasets, likely AI applications.  

Scientists Develop Algorithms to Study Genomic Data

UCLA Samueli School of Engineering, June 28, 2022

A multi-institutional team of researchers led by the University of California, Los Angeles' Sriram Sankararaman has developed algorithms that can analyze genomic data up to 1,800 times faster than previous methods, enabling the potential analysis of information from 1 million people in a single day. The researchers designed the SCalable pOPulation structure inferencE (SCOPE) technique to accelerate and scale the inference of genetic variability within a population to reveal patterns that direct conclusions or avoid false associations in research. SCOPE reduces the volume of computational resources needed to process large datasets, and lowers the cost of running calculations. In one experiment, the researchers used just 250 gigabytes (GB) of memory for computation, rather than the approximately 2,000 GB required by a previous research tool.