Convergent Functional Genomics and the Web

The Convergent Functional Genomics approach leads to a powerful Bayesian-based prioritization of ours and existing data in the field, and identifies known as well as novel genes, and thus provides novel leads and validation way beyond the perusal of animal and postmortem literature published already. One way to conceptualize it is by similarity to the way the Google PageRank algorithm organizes the masses of amorphous data on the web- the more the links to a page, the more it comes up to the top of your list. Similarly, the more the number of independent lines of evidence converging on a gene, the higher it is on our priority lists. The pyramids of prioritization described in some of our publications should make this point visually clear. (Some of the postdoctoral fellows in the lab do remark on similarities with ancient Egypt also in terms of the painstaking labor involved). A significant amount of new experimental data as well as laborious critical manual curation of existing literature, organized in constantly updated internal databases in our laboratory, goes into our approach-similar to what Tim Berners-Lee calls the “Semantic Web”.