Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 M E T H O D O L O G Y A R T I C L E Open Access A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database Kshitish K Acharya1,2*, Darshan S Chandrashekar1†, Neelima Chitturi1†, Hardik Shah1, Varun Malhotra1, Sreelakshmi KS1, Deepti H1, Akhilesh Bajpai1, Sravanthi Davuluri1, Pranami Bora1, Leena Rao1 Abstract Background: In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time. Results: The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a ‘consensus’ expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/ experimental conditions and/or cell-types. Conclusions: Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a ‘reliability-score’, which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: http://resource.ibab.ac.in/MGEx-Tdb/ Background Establishing gene expression patterns would facilitate understanding of almost every aspect of cellular and molecular biology. Since mass scale detection is easier in case of mRNAs than proteins, many studies have * Correspondence: kshitish@ibab.ac.in † Contributed equally 1Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bangalore - 560100, Karnataka state, India Full list of author information is available at the end of the article considered gene expression determination at the tran- script level. These reports have resulted in large number of signature sets of genes corresponding to various experimental/physiological conditions and tissues/cell- types of multiple species. The phrase ‘gene-sets’ is used in this article to refer to the data-sets, which usually have a large list of genes with additional expression values, as well as the simpler gene-lists, which are a smaller set of genes, and usually without additional data. © 2010 Acharya et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.