Databases1 MGEx-Tdb Bgee MGI (GXD) MRG 4DXpress Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 Table 5 Results for expression status of genes at developmental stages in mouse testis from different databases. Postnatal period Specific postnatal Stage (0-6 day/TS27) 210162 15899 59543 ~8500 ND 21195 15501 ~8500 ND Notes: See additional file 9 for detailed methods; NA: Not Applicable, i.e., no query feature available for early post-natal stages of development; ND: No Data in postnatal development stage [available till TS 26 only]; TS: Theiler stage (A term used to denote the stage of development of a mouse; Theiler, 1989); Bgee: a dataBase for Gene Expression Evolution; MGI (GXD): Mouse Genome Informatics (Gene Expression Database); MRG: Mammalian Reproductive Genetics; 4DXpress: EXpression database in 4D (four dimensions). 1The references and URLs are listed in the additional file 4. 2MGEx-Tdb results correspond to genes transcribed/dormant on specific days between day 0-20 and many genes cancel due to contradictory results. However, one can obtain better results for specific stage using this database, as indicated in the third column. 3The results include some repeats. whose function/ontology is well established are consid- ered as possible markers. With the new approach one can compare the list of genes with high reliability score for the ‘expressed status’ under a normal condition with a similar set of genes absent in an abnormal condition. The union list derived after such a comparison would have a unique value as a set of potential biomarkers. Similarly, genes that are more likely to be dormant in normal conditions, but expressed in abnormal tissues would also be important. We are currently trying to use this approach to identify genes that have a strong corre- lation with azoospermia. Nevertheless, the current database can also be used as a single source for identifying most of the mass scale gene expression data as it directs the user to the original data in all cases. Those interested in using the original microarray data can do so, and perform their own com- parison and analysis. Approaches similar to the one used in this study have been used for other purposes earlier too: Smith et al. [30] applied such a method for meta-analysis of breast cancer microarray data and Harsha et al. [31] for identi- fying potential pancreatic-cancer biomarkers. Very recently, Culhane et al. [32] also reported a very similar approach to create a gene expression database, Gene- SigDB, which considers gene lists from tables or figures embedded in publications or included as supplementary material on the journal’s or the author’s website. But, GeneSigDB does not use raw data, cover testis-related conditions or derive a consensus across data-sets (from different studies). New methods such as Gene Set Page 7 of 10 Enrichment Analysis (GSEA) [33], Parametric Analysis of Gene set Enrichment (PAGE) [34] and Generally Applicable Gene set Enrichment for pathway analysis (GAGE) [35] process data across multiple data-sets in such a way that, the specific details of data-processing within each study are not required to bring out mean- ingful information from the microarray experiments. However, the objective of GSEA and PAGE was to gain insights into biological mechanisms by clustering genes across studies, while our focus was in deriving the con- sensus information along with a reliability score. Compilation of gene-sets corresponding to comparable conditions and locations, and deriving a reliable ESLC for each gene, can be useful in various ways. One can cluster genes based on their expression pattern in differ- ent ESLCs. Such clustering can help to identify genes having strong association with specific conditions and/ or locations. For example, genes with consistent expres- sion in normal testis but absent in infertility conditions might be of significance for researchers. The higher the reliability-score of a gene, the higher will be its chances of being a biomarker and/or a candidate for research in diagnostics, prognostics and therapeutics. Moreover, tis- sue-specific databases, such as MGEx-Tdb, also have the potential to assist in exploring the variation or conserva- tion of expression of genes across different species in multiple tissues. The need for systematically compiling gene-expression data in one place is obvious from previous efforts. In fact, TisGeD [36], a new database, has been reported during the last stages of the writing of this manuscript. This database is a compilation of data for most tissues and species, mainly from existing databases. But it seems to have failed to make the best use of all available information, at least for the testis tissue. On the con- trary, an effort like the current one may not be always practical. The biocuration process consumed a signifi- cant amount of time (about 3 years) and is eventually limited to only one tissue. However, it would provide more reliable information. There is perhaps a compro- mised approach possible. While about 222 gene-sets in the database were retrieved from literature, 156 of them had less than 500 genes per set. By avoiding such smal- ler gene-sets, one might save time - albeit with some loss of information. Even though this study has compared MGEx-Tdb with a few well-established databases, the purpose is of course not to undermine the value of these pre-existing resources. Such databases have their own specific advan- tages and, in many cases, a wider variety of applications. The objective of comparing the different databases was to validate the novel approach. While MGEx-Tdb can facilitate unique applications in the gene expression studies in the context of