Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 Many databases have been created using a variety of gene-expression data. The main types of data considered by such databases include: Expressed Sequence Tags (ESTs) (e.g., UniGene [1]), microarray (e.g., BioGPS, earlier SymAtlas [2]), Serial Analysis of Gene Expression (SAGE) (e.g., GermSAGE [3]) and manually curated information from different small scale experiments (e.g., Human Protein Reference Database or HPRD [4]). The majority of these databases can extract and display the expression-related data for genes in multiple species, tis- sues and conditions. However, our preliminary observa- tions showed that the transcription patterns of genes revealed by different databases frequently contradict each other. Further, the reproducibility of data from microarray is questionable in spite of the fact that this is the most common expression profiling technique used [5-9]. Hence, it is preferable to derive a ‘consensus’ expression status for genes by comparing the data-sets from com- parable conditions and tissues/cell-types, rather than relying on one study only. It is possible to derive a sim- ple expression status (expressed or not expressed) for conditions or tissues, for which multiple data-sets are available. However, such simple expression profiles can- not be retrieved by the existing databases, even when the expression data for the corresponding conditions/ locations is available. This is because of the following reasons: a) Some of the published microarray data do not seem to be covered by the existing repositories/databases. For example, the raw data is not deposited in a significant number of cases [10]. The smaller gene-lists, which are sometimes the only gene-sets reported for specific condi- tions/cell-types, are particularly ignored by most databases, with exceptions such as Oncomine [11], which focuses on human cancer data alone. The microarray data corre- sponding to some studies such as the effects of androgen deprivation [12], FSH treatment [13], and testicular carci- noma [14] were not found in most of the resources. b) While most existing databases permit querying with gene symbols (e.g., PRM1) or complete names (e.g., pro- tamine 1), very few databases allow the user to success- fully use the names of cell types, tissues, or specific physiological conditions, as query terms. More impor- tantly, whenever such queries are allowed, the corresponding results do not seem to be reliable (Acharya et al, unpublished). c) Many databases focus on the relative expressions of genes across two conditions and/or the level of expres- sion of genes. This type of information is difficult to compare across data-sets. Hence, most existing data- bases simply provide access to the original data, or represent it graphically, rather than making a final Page 2 of 10 statement about the expression/transcription status of genes under various conditions. Somehow, the value of a simple expression statement in a binary form (i.e., expressed or not expressed) is apparently ignored by most existing databases. In view of such limitations in the available online resources, we have initiated a novel approach to create tissue-specific databases by making maximum use of the available information and facilitate the prediction of gene expression patterns under various conditions. The current article reports creation of the first of its kind tissue-specific database for predicting gene expres- sion patterns. Our objective was to collect maximum gene expression data-sets corresponding to a specific tissue, and enable identification of genes with consistent expression status across multiple studies corresponding to specific conditions/cell-types. The consensus expres- sion status derived for genes using such an approach was expected to largely agree with manually curated data (MCD), particularly in cases where the information is supported by multiple small scale studies. The perfor- mance of the new database was compared with several existing resources. Testis, where the cell differentiation is unique among adult tissues, was taken as the first tissue for database creation. Methods The strategy can be summarized as follows: a) Collect maximum amount of genome-wide expression data, including smaller gene-lists reported in the manuscripts, corresponding to the mammalian testis tissue. b) Create a database using these data. The database would have multiple gene-sets corresponding to the same or similar physiological and/or experimental conditions. c) Mea- sure the extent of agreement or contradictions for each gene’s expression status, across comparable gene-sets in the database. d) Use this degree of consistency to iden- tify the ‘consensus’ expression status for each gene, for maximum possible conditions/locations, and derive a ‘reliability-score’ for each consensus expression status. e) Create interfaces that would allow users (of the data- base) to easily obtain lists of genes for various testicular conditions/cell-types, and also differentiate the genes with higher reliability, from those with low reliability scores, for a specific expression pattern. Collection and curation of data Lists of genes reported to be transcribed or dormant fol- lowing genome-wide expression studies in testis or spe- cific testicular cells (spermatogonia, spermatocytes, spermatids, spermatozoa, Sertoli cells, Leydig cells and myoid cells) under different conditions were collected. Two approaches were used to collect such gene-sets,