The completion of the Human Genome Project and development in technologies and techniques like ONCOMINE a joint venture between IOB and Chinnaiyan Lab, Michigan University, is a bioinformatics infrastructure for the cancer biologist where publicly available cancer microarray studies are curated and provided with data mining tools to efficiently query genes and expression profiles of interest as well as meta-analyze groups of studies. The project has three modules in it (i) Query by Gene, (ii) Query by Analysis and (iii) Meta Analysis.

Query by Gene: This module enables the user to query the data by gene, using any one of the gene identifiers like Gene name/Symbol, Swissprot ID, Unigene ID, Locus Link ID, OMIM ID with two flexible search modes.
Quick search - This search mode is for the user not very familiar to search engines and might find difficulties in trying to extract a filtered output from the data set by using intelligent keyword search. Hence, by default, the keyword entry is treated as an 'exact match'. If the dataset does not give an exact match, then an automated internal query is triggered to provide other possible combinations of results.
Advanced search -This search mode is for the user with average exposure to search tools. Here the user can select keywords with a range of options like - start with, end with, exact match or contains the keyword, without the use of wildcard characters.

Query by Analysis: Multiple genes included in a particular study of interest can be retrieved by the user by selecting a specific tissue (e.g. breast, pancreas etc.). This dynamically updates the “Study” option which provides the user with all the studies in the dataset included for this specific tissue. The differential conditions (normal versus diseased) under which the micro-array analysis was performed can be chosen from the option “Class 1 vs Class 2”. The retrieved results can be further filtered using ‘drug target’ or ‘gene ontology’ as criteria. The display parameters like Cell Width, Cell Height, Gene/Frame enable customizing the result sets.

Meta Analysis: The Meta Module is useful for exploring selected multi-study analyses that we have performed. Thus far, 5 such analyses have been performed. First, we identified genes that are significantly over-expressed in multiple cancers vs. normal analyses, a so called universal cancer meta-profile. We found a large set of genes that are highly expressed in many cancer types, suggesting an important role in the process of carcinogenesis in general. Second, we defined a high grade, or undifferentiated, meta profile, demonstrating that high grade cancers share transcriptional features regardless of cancer type. Third, we generated cancer type specific meta-profiles for cancer types in which multiple analyses tested the same hypothesis (e.g. prostate cancer vs. normal prostate). These meta-profiles serve to cross-validate multiple studies.

Result set display options:
The output data obtained from the query search can be plotted using either the bar graph or box plot options to assist further analysis. The user can view this data for a single study or a group, by selecting the appropriate check boxes. The image plots are supplemented by detailed mouse-over descriptions to assist the user in comprehending the plots. The image scale with reference to both width and height can be changed choosing the variable parameters thus simulating zooming features. The user can effortlessly toggle between the expression data and the annotations for a specific gene using convenient hyper links.

Download:
The download features (both types of graphs and data) are available for all.