Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 Table 3 Relative assessment of amount and details of information, and agreement with MCD Information availability and volume of supporting data Score (maximum = 33) % score Agreement with reports on individual gene studies Score (maximum = 11) % agreement Database HPRD* MGEX-Tdb UniGene BioGPS 13 27 14 11 Notes: See additional file 8 for detailed methods; *HPRD is specific for humans. Hence the score depicted for this database is not an average across the species. 8 5 3 5 Page 6 of 10 73 45 27 45 Database1 MGEx-Tdb BioGPS3 RefExA4 TissueDistributionDBs4 UniGene4 HPRD 12753 403 92 16124 18421 4249 5215 14 3 4 4 2 Notes: See additional file 9 for detailed methods; NA: Not Applicable, i.e., data restricted to humans only in these databases; 1The URLs are listed in the additional file 4. 2Querying with testis AND cancer retrieved 116 genes in BioGPS; “Germ cell cancer” in testis retrieved 9655 genes (10042 hits) in TissueDistributionDBs and “Germ cell tumor” in testis retrieved 14874 genes (18130 hits) in UniGene and 2 genes in HPRD. 3Includes results from human, mouse & rat species (search can not be restricted to specific species). 4Number of genes in the results corresponding to different queries: probe names in case of RefExA & cluster IDs in case of UniGene & TissueDistributionDBs. 5Querying with alternative equivalent terms of postnatal (neonate) retrieved 10467 genes (11519 hits) in UniGene. Discussion Researchers have been trying to compile and compare microarray data from different studies. ArrayExpress, TranscriptomeBrowser [21], Genevestigator [22] and COXPRESdb [23] are some examples. Variations in experimental conditions across studies have hindered such efforts. Recommendations for systematic reporting of experimental conditions [24,25] and new methods for cross-platform comparisons of microarray results [9,26] have improved our ability to make use of the available microarray data. However, many microarray data-sets are not comparable due to various reasons. These rea- sons include non-compliance with MIAME [27] and complications in statistical methods of data processing, particularly when the studies have used different micro- array platforms [7]. There are also a large number of reports where raw data is not available and only selected genes with specific expression pattern are listed. Such gene-lists lack the necessary basic information for com- paring expression level-related information. The method reported here allows comparison of the gene-sets irrespective of associated information such as intensity values, statistics, platform and probes. Obviously, this simplification would mean loss of other important information such as relative up/down regula- tion and levels of expression. Many ‘meta-analysis’ approaches have considered such details of expression data [28,29], but they are applicable to raw data only. It is also difficult to find consensus by the traditional methods. For example, if a study (A) finds gene xyz to be expressed very high in condition 1 (C1) compared to condition 2 (C2), study B finds xyz to be expressed only marginally higher in C1 than in C2, and study C finds it to be expressed almost equal in both C1 and C2 - they all disagree with each other as far as the ‘relative levels of expression’ is considered. The novelty of our approach is that, by considering only the ‘transcribed’ or ‘dormant’ status, the data could be compiled across dif- ferent microarray platforms, and we can state that gene xyz is ‘present’ in C1. Thus, this method of comparing expression status in a binary form allows use of most of the available microarray data, including simple gene- lists. In fact, obtaining a list of genes expressed or not expressed in specific conditions, and deriving consensus across studies, can provide an extremely important alter- native to biomarker identification. Generally, the genes that are up regulated or down regulated and those Table 4 Results in response to queries for expression status of genes under different testicular conditions Normal testis (human) Azoospermia (human) No. of genes retrieved in response to different queries Asthenozoospermia Testicular Adjudin cancer2 (human) treatment (rat) (human) 39 82 42 33 10 0 0 0 0 0 16617 3 3 2 194 0 10982 0 0 0 Developmental stage- postnatal (mouse) 15209 (day 14) 2 0 15