Authors: 
B. Borgeson, C. St-Jean-Leblanc, E.M. Marcotte
Category: 
Poster
Conference: 
Abstract: 

Huge portions of the genomes of even the most well-studied organisms remain poorly understood. Accordingly, computational methods have attempted to predict functions and phenotypes associated with genes. Previously, our lab has constructed so-called functional networks--networks designed to predict the function of genes based on their similarity to other genes on a number of biological measures--and used these networks to show that even complex phenotypes such as longevity are relatively predictable. Here we take a new approach with different data, combining widely varied large publicly available gene-phenotype association datasets for human, mouse, worm, yeast, and more into a single inference procedure termed phenologs to predict phenotypes across species. In-silico validation results for predicting long-lived mutants indicates that this method shares similar promise in predicting even complex phenotypes. Experimental validation results for other predicted phenotypes have been highly encouraging, and lifespan experiments are planned.

Keywords (Optional): 
bioinformatics
networks
lifespan
SENS Research Themes: 
Ending Aging: