Acharya et al. BMC Genomics 2010, 11:467 http://www.biomedcentral.com/1471-2164/11/467 from studies on mouse, rat and human species (details of the process in additional file 1): a) Literature search was performed to collect rele- vant articles. A search strategy was carefully designed to select appropriate query terms, search fields and combinations. The initial PubMed results were filtered by screening the title of the articles. Abstracts of the short-listed citations were read to identify articles of probable relevance (articles that might report expression of multiple genes in mam- malian testis or its cell types). These articles were then searched for the list of genes reported to be expressed, up-regulated, down-regulated, etc. Origi- nal complete lists reported in the supplementary notes (such as the journal web-sites or the author’s own page) were traced. When this was not possible, the genes reported to be expressed were extracted from the main text of the published article. b) Microarray repositories such as Gene Expression Omnibus (GEO) [15], ArrayExpress [16], Oncomine, Stanford Microarray Database (SMD) [17] and Cen- ter for Information Biology gene EXpression data- base (CIBEX) [18] were screened for data-sets related to mammalian testis. The search and collec- tion of data-sets were performed manually. The data-sets were uploaded into the new database along with associated details such as the statistical meth- ods and platforms used in the experiments, using a specially designed excel-based format. Newly created programs (please see below) extracted the contents of such files, including genes and their expression status, into designated tables of the database. Author/depositor-calls on the expression status were used. No additional analyses were performed. Each gene-set was collected along with the necessary cognate information, such as, 1. the expression status (expressed or dormant), 2. species, 3. tissue-area or cell type, and 4. specific physiological or experimental condition. This set of basic parameters is henceforth referred to as ‘Expression Status under specific Location and Condi- tion (ESLC)’. The ‘conditions’ include normal physiolo- gical state, diseases, developmental stages and treatment with hormones and/or other chemicals. This type of information and the corresponding gene-set were sys- tematically formatted and entered into the database. No attempts were made to perform fresh statistical analysis. Instead, the ‘expressed’ or ‘not expressed’ call by the author(s) was relied upon. In any study with multiple hybridizations, the expression status indicated in the majority of cases was used as the final expression status. Page 3 of 10 For example, any gene reported to be ‘expressed’ in the majority of the hybridizations/samples was identified as ‘expressed’. In several cases, the gene-sets reported by the authors had to be split into multiple gene-lists (see additional file 1 for an example). Thus, several gene-sets were collected, each ranging in size from three genes to several thousand genes. Each gene-set was characterized by the transcription, or lack of it, in specific location and condition. A scoring system was used to suggest the degree of reliability of the ESLC For every ESLC, a score was derived for each gene and added up across comparable gene-sets to indicate the reliability of that ESLC. This ‘reliability score’ reflected the consensus expression status across multiple gene- sets. Each gene was assigned a score of two for a specific and definite expression status (transcribed or dormant). When the evidence for an ESLC of a gene increased, across the gene-sets, the score increased. For example, a gene reported to be ‘not detected’ in normal rat testis by two studies, would be shown as ‘dormant’ in normal rat testis with a reliability-score of four. Thus, a lower score would indicate either lack of supporting evidences or pre- sence of contradicting reports for the specific expression status under consideration (details in additional file 2). Database creation (see additional file 3) MySQL Relational Data-Base Management System (RDBMS) was employed for storing data. A table was dedicated to store the basic gene-related information including the gene name, locus and transcript details. Another table was used to store gene identifiers such as gene name, gene description, official gene symbol and the National Center for Biotechnology Information (NCBI) gene identifier etc. There are also tables corre- sponding to each main expression condition: each table was created to store the genes with the derived expres- sion status along with details pertaining to their ESLC’s. Perl based CGI script has been used to create an interface for entry of gene-lists. Programs were written for automatic downloading of basic information for every new gene entered into the database. Another pro- gram used the available identifier for each gene in the uploaded gene-set, and retrieved its respective NCBI gene identifier and the gene symbol. After the basic information compilation, the genes were put in queue for further information downloading from online resources, followed by uploading into the new database. Specially designed Perl programs such as LWP modules (http://search.cpan.org/~gaas/libwww-perl-5.836/lib/ LWP.pm) were used to connect to NCBI and, with the aid of NCBI E-utilities (http://eutils.ncbi.nlm.nih.gov/ entrez/query/static/eutils_help.html), the required