Analysis, Exploration and Visualization of High-Throughput Data
Our research is focused on how we can best utilize high-throughput data sources to understand biology at multiple levels. This problem has become increasingly challenging over the past decade as new experimental techniques and resources (e.g. gene expression microarrays, deep sequencing, tandem mass spectrometry, etc.) have grown widely available and more affordable. While these data promise to shed light on cellular mechanisms, gene regulation, protein functions, and ultimately human disease, the rate at which these data are translated into knowledge is currently much slower than the rate of data generation.
In order to help bridge this gap, our focus is on developing novel algorithms and approaches for the analysis, exploration and visualization of this data. In particular, these methods incorporate biologists into the early phases of analysis in order to utilize their existing, expert knowledge.
Center related publications
Functional genomics complements quantitative genetics in identifying disease-gene associations
Guan Y, Ackert-Bicknell CL, Kell B, Troyanskaya OG, Hibbs MA.
PLoS Comput Biol. 2010 Nov 11;6(11):e1000991. PMCID: PMC2978695. [ Full Text ] [ Supplemental Website ]