Tissue- and Disease-specific Analysis of Genome-scale Data

Abstract

Biologists using modern experimental methods are generating a massive number of genome-scale datasets. In particular, the rate of large-scale data creation in most organisms is quickly outpacing biologists' ability to perform detailed follow-up experiments. Thus a substantial gap exists between the massive data being generated and the comparatively small number of experimental validations being performed (i.e. biological knowledge). In this manuscript, we present four solutions that broadly address this growing disparity, focusing on disease- and tissue-specific genomic analysis. These solutions are unified by their approaches to this problem: by combining and integrating available public genome-wide measurements to enable biological discoveries that would otherwise be impossible. First, we demonstrate a method to systematically transfer experimental knowledge between organisms inferred from high-throughput experimental data. By leveraging functional genomic data, we can improve the coverage and accuracy of function predictions across diverse organisms and machine learning methods. Second, we present an interactive web server that addresses the needs of biologists to visualize their experimental results in the context of multi-species functional predictions and relationships. Third, we describe a method that, for the first time, leverages large data compendia to build genome-scale tissue-specific functional maps in human by integrating thousands of genome-scale datasets. Our method can extract both functional and tissue/cell-type signals even when genomic data are not resolved for the tissue and very little are known about the expression of genes in the tissue. Finally, we detail a method for biologists to analyze their genome-scale datasets in the context of the massive public data compendium. Biologists are generating and trying to make sense of massive high-throughput datasets, and their biological questions can be more precisely addressed within the biological context of their experiment. By incorporating their experimental results in the search and integration of gene expression compendia, we demonstrate improved predictive performance in identifying additional functionally related genes.