Integrated Omic Analysis
The correlation between metabolite levels and gene expression
Developing Tools for Integrated Omic Analysis
The Sara Cooper Lab has been fortunate to generate and have access to large genomic and metabolomic data sets. Our own data in combination with the vast resources available from public projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) provide a wealth of data we can mine to improve our understanding of human cancers. We are working to develop methods to integrate these data in order to accomplish two main tasks: 1) Visualize these data together in order to understand how gene expression, protein levels and metabolite levels change together and 2) Validate one data type by using another, e.g., we can identify genes whose expression patterns change and then show that metabolite levels fluctuate as predicted. This allows us to both improve confidence in smaller data sets and also demonstrates the functional consequences of the changes detected at the level of transcription.