Background: Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a diseasebased focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations.

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BackgroundOnce specific genes are identified through high throughput genomics technologies there is a need to sort the final gene list to a manageable size for validation studies. The triaging and sorting of genes often relies on the use of supplemental information related to gene structure, metabolic pathways, and chromosomal location. Yet in disease states where the genes may not have identifiable structural elements, poorly defined metabolic pathways, or limited chromosomal data, flexible systems for obtaining additional data are necessary. In these situations having a tool for searching the biomedical literature using the list of identified genes while simultaneously defining additional search terms would be useful.ResultsWe have built a tool, BEAR GeneInfo, that allows flexible searches based on the investigators knowledge of the biological process, thus allowing for data mining that is specific to the scientist's strengths and interests. This tool allows a user to upload a series of GenBank accession numbers, Unigene Ids, Locuslink Ids, or gene names. BEAR GeneInfo takes these IDs and identifies the associated gene names, and uses the lists of gene names to query PubMed. The investigator can add additional modifying search terms to the query. The subsequent output provides a list of publications, along with the associated reference hyperlinks, for reviewing the identified articles for relevance and interest. An example of the use of this tool in the study of human prostate cancer cells treated with Selenium is presented.ConclusionsThis tool can be used to further define a list of genes that have been identified through genomic or genetic studies. Through the use of targeted searches with additional search terms the investigator can limit the list to genes that match their specific research interests or needs. The tool is freely available on the web at [1], and the authors will provide scripts and database components if requested mdatta@mcw.edu

Background: Our increasing use of genetic and genomic strategies to understand human prostate cancer means that we need access to simplified and integrated information present in the associated biomedical literature. In particular, microarray gene expression studies and associated genetic mapping studies in prostate cancer would benefit from a generalized understanding of the prior work associated with this disease. This would allow us to focus subsequent laboratory studies to genomic regions already related to prostate cancer by other scientific methods. We have developed a database of prostate cancer related chromosomal information from the existing biomedical literature. The input material was based on a broad literature search with subsequent hand annotation of information relevant to prostate cancer.