Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 mammalian testis, it has a few limitations and there is a scope for further improvement in different aspects. For example, incorporation of level of expression along with the basic expression status might be possible in many cases. The method of calculating ‘reliability-scores’ for the expression patterns can be improvised by consider- ing the details such as sample size and validation of the microarray data, reported along with gene-sets. Factors such as unavailability of complete data in many cases, diversity in analytical methods used, and lack of experi- mental details in many of the published gene expression studies have been major hurdles for the compilation of parameters mentioned above. Nevertheless, we are already making attempts to make the possible improve- ments. We are also trying to include data from other types of mass scale studies. In the current database, we have used the non-microarray data in some cases only, particularly when a list of genes was reported in the manuscript or in the supplementary notes. The data in the repositories could not be included due to complica- tions in the process of converting the unique identifiers (e.g., SAGE tags) to standard gene names or ids. We shall complete these tasks in a revised version of the database. Moreover, efforts are on to include data from more mammalian species for the testis tissue, further improve query features of this database and even develop a few other tissue-specific databases. Most of the existing data permit only predictions, rather than actually establishing a final expression status for different genes. This can be explained as follows: a) There is a larger amount of data available for the expression of genes at the RNA level, compared to pro- tein level, and transcription doesn’t guarantee continued translation into proteins. Thus, the mRNA data can only be used to suggest or predict the expression of genes into final proteins. b) The expression status of some genes can vary across samples, even within a study. The genes which behave the same way across samples and studies are more likely to have a stronger association with the physiological condition of the tissue/cell type of interest. This means, the data can only be used to pre- dict the expression possibilities. And, it will be useful to ‘predict’ expression patterns of genes, using a reliability- score such as the one reported here. Conclusions We demonstrate that manual collection of mass scale gene expression data will allow derivation of a ‘reliability score’ for binary expression status of genes. The simpli- city of the new approach permits consideration of expression data obtained using ESTs, SAGE, Massively Parallel Serial Sequencing (MPSS), proteomics and other techniques as well. This way, maximum amount of existing data can be used for better prediction of gene Page 8 of 10 expression patterns across a variety of reports. We also report the mammalian testis specific gene expression database, which performed better than most other gene expression databases in various aspects. MGEx-Tdb is the first database that attempts to make maximum use of the available data to provide a quantitative indicator for the expression-probability of genes under multiple conditions and locations, in the context of at least one mammalian tissue. We are further improving the scor- ing method and enhancing the volume of relevant gene expression data. MGEx-Tdb can be used for easy retrie- val of information about expression of genes in several conditions of testis or its cell types in 3 most well-stu- died mammalian species. This in turn can be very useful for identifying potential biomarkers and studying mole- cular details of mammalian testis physiology. Additional material Additional file 1: Notes S1. Details of gene-set collection and fixed vocabulary usage in the database creation. Additional file 2: Table S1. Illustration of scoring derived from multiple data sets for specific ESLCs (’transcribed’ or ‘dormant’ in normal human adult testis). Additional file 3: Figure S1. Schematic representation of creation and functioning of MGEx-Tdb. Additional file 4: Table S2. References and URLs for various databases/ repositories used/referred in the study. Additional file 5: Table S3. Summary of manually curated data (MCD, from reports on individual gene studies) for the genes selected for final comparison of databases. Additional file 6: Table S4. Scoring method for assessing the extent of agreement between the manually curated data (MCD, from reports on individual gene studies) vs. the information from databases. Additional file 7: Notes S2. Procedure for comparison of MCD with the information from the databases. Additional file 8: Table S5. Procedure for relative assessment of amount and details of information, and agreement with reports from individual gene studies. Additional file 9: Notes S3. Procedure for comparison of results from various resources for gene expression under different physiological conditions and developmental stages in testis. Additional file 10: Notes S4. An overview of MGEx-Tdb use, with the help of screen-shots. Acknowledgements Grant sponsor (to KKA): Department of Biotechnology [Ref. no. BT/PR-5433/ BID/07/117/2004] and Department of Information Technology [Grant numbers: DIT(R&D)/BIO/15(9)/2006 and DIT/R&D/BIO/15(5)/2008], Government of India. Author details 1Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bangalore - 560100, Karnataka state, India. 2Shodhaka Life Sciences Pvt. Ltd., IBAB, Biotech Park, Bangalore - 560100, India. Authors’ contributions KKA conceptualized the project, assigned tasks to other team members, obtained funding, trained the team, contributed to validation of biocuration