High-Dimensional Data Approaches to Understanding Nuclear Hormone Receptor Signaling

Bioinformatics applies unbiased approaches to develop statistically robust insight into health and disease. At the global, or "20,000 ft" view bioinformatic analyses of NR signaling can measure how the NRs are implicated in human health and disease through the impact of genome-wide significant genetic variation, family-wide NR expression patterns or considering where NRs are significantly identified in other high-dimensional data analyses. With a more NR-centric, or "2000 ft" view, bioinformatic approaches can interrogate events downstream of a given NR. Integrative approaches aim to combine multiple NR-centric high-dimensional data both derived in cell models and primary human tissue to reveal how NR-transcriptional networks relate to human health and disease. Bioinformatic approaches to such high-dimensional data are central and require specialist statistical insight and computational skills, coupled with a dexterous understanding of the biological question. A current challenge is determining the optimal mechanism to share such bioinformatic approaches through the biological research community.