The search page allows the user to search through the collection of strains, experiments, and genetic features (read: genes) in the database. The search is not case-sensitive. Each of the search results is linked to a report.
The experiment report shows the results of analyzing an experiment; note that any samples can be used in an analysis, so this can be used for both real and virtual experiments. Currently, the result of an analysis is a list of genes affected-- those that passed criteria for being up- or down-regulated, for instance. Each of these result lists is linked to a view showing the genes, and their (rescaled) expression levels, which can be sorted on any sample, or on the maximum difference between all samples. The experiment report initially displays a form on the right, providing a list of categories or other results to compare to the current experiment, explained further below. The report also lists some information about the sample used as the mutant type.
The strain report is fairly self-explanatory; it shows the genetic background of samples in the database, as well as a list of any other samples of the same strain, and the experiments they are in. This provides the means to find other experiments (either real or virtual) that are testing the same gene.
The genetic feature report provides a summary of information in the database about a given gene. In addition to basic bookkeeping information, this includes a summary of experimental results in which this gene was affected: experiments in which the gene was up- or down- regulated, and links to the pertinent results.
The set intersection tool can be used for comparing two sets of results, or for comparing a set of results against other categories, such as the MIPS categories, or chromosomes, for instance. The resulting report presents first a table showing all overlaps, with a count of genes in common, and a -log10 p-value based on the hypergeometric distribution. A threshold is computed, based on the total number of comparisons, and intersections above that threshold are highlighted and displayed in a summary table below.
The experiment comparison tool presents a new approach in the database to finding relationships in the data. Rather than clustering genes, based on their levels of expression in different samples, it is possible to cluster the samples by comparing their global gene expression. Using this approach, one can quickly find what knockouts are similar or different.