The permutation and exhaustive search schemes of the previous GGI methods are computationally too intensive to be employed in large genome-wide scale data set for high-order interactions.
However, at the time, no wide scale disease data set with standardized phenotypic annotations was available; therefore, the study was restricted to a small number of manually curated disease descriptions.
Google's technology infrastructure, is optimized for large scale data sets and not rapid iteration.
Statistical methods have been developed to infer the fine-scale structure of recombination rate variation from genome-wide scale data [4].
With large genome-wide scale data, this approach makes it feasible to discover higher-order interactions.
Towards overcoming these limitations we have developed "ePlant" (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana.
Our EST results comprise the first extensive high-throughput, genome-wide data set for a dinoflagellate.
The eFP Browser software is easily adaptable to microarray or other large-scale data sets from any organism and thus should prove useful to a wide community for visualizing and interpreting these data sets for hypothesis generation.
This is especially true when the algorithms are operating on large-scale data sets.
However, our use of large-scale data sets (i.e., genomic sequence information, microarray-based expression data, and functional annotation) enabled us to address these relationships in a genome-wide fashion.
This analysis was done using species-wide data set.
