UC Case No. 2019-351
SUMMARY:
UCLA researchers from the Department of Computer Science have developed a method to analyze large genomic data sets to quickly identify bacteria community imbalances.
BACKGROUND:
Bacterial diseases such as dysbiosis are a widespread and common issue in both medicine and agriculture. Diagnosing the bacterial strain that is causing the disease is a difficult problem to solve as there are a wide variety of bacterial strains that require differing treatments. Current systems can only rapidly identify a small set of bacteria or require expensive, extremely time-consuming processes to identify a bacterial strain. There is a need for a method that can identify a large set of bacteria quickly without decreasing accuracy in identification.
INNOVATION:
UCLA researchers have developed a method that reduces the amount of time for the characterization of bacterial biomes. Fast Expectation-Maximization Microbial Source Tracking (FEAST) can be leveraged for use in medical diagnoses and characterization of agricultural products. It can analyze and identify bacteria accurately up to 300 times faster than conventional identification methods. FEAST can also identify diagnosable differences between biomes from health and sick patients.
POTENTIAL APPLICATIONS:
• Diagnosis tool for various bacteria-based diseases
• Ecological tool used for studying microorganisms
• Tracking foodborne illness
ADVANTAGES:
• Faster identification of bacterial communities
• Identification of against uncharacterized sources
• Can be used with significantly larger data sets
DEVELOPMENT-TO-DATE:
The algorithm has been developed and is ready for use.
RELATED MATERIALS:
• Shenhav, L., Thompson, M., Joseph, T. A., Briscoe, L., Furman, O., Bogumil, D., Mizrahi, I., Pe’Er, I., and Halperin, E. (2019) FEAST: fast expectation-maximization for microbial source tracking. Nature Methods 16, 627–632.