Urzúa et al. BMC Genomics 2010, 11(Suppl 5):S1 http://www.biomedcentral.com/1471-2164/11/S5/S1 Frederick (Frederick, MD) with a BioRobotics Microgrid arrayer (Genomic Solutions, MI). Hybridization condi- tions and washes have been described elsewhere [11]. Samples were co-hybridized against a whole-newborn mouse total RNA as common reference sample using a replicated dye-swap design. A total of 24 microarray hybridizations were performed. TIFF images were cap- tured with a GenePix 4000B fluorescent scanner (Mole- cular Devices, CA) and then saved for further analysis. Statistical and bioinformatic analysis Scanned microarray images were extracted as GPR files using the GenePix 5.0 software and uploaded to the NCI’s Microarray Database (“mAdb”; http://nciarray.nci. nih.gov). Data files containing updated gene annotation were subjected to local (“print-tip”) loess normalization, scale adjustment, and filtering/imputation of missing values with the “DNMAD” and “PreProcessor” tools at the GEPAS server (http://www.gepas.org). Reproductive and spontaneous ovarian tumor records were extracted from the MPD at http://www.jax.org/phenome[8] and from the MTB at http://tumor.informatics.jax.org/ mtbwi/index.do[9] databases, respectively. Tumor data corresponds to the “highest reported tumor frequency” in all literature records collected in MTB for each strain. Continuous tumor and reproductive data for the 4 mouse strains (see Table 1) were correlated to gene expression log2 ratios using linear regression analysis under multiple-test control (FDR indep) with the tool Pomelo II using 200,000 permutations (http://pomelo2. bioinfo.cnio.es/). The ANOVA test among the 4 strains and a t-test between SWR and the 3 remaining strains were also conducted with Pomelo II using 200,000 per- mutations. Gene functionality was primarily assessed with Gene Ontology (GO) terms using hypergeometric tests conducted with WebGestalt (http://bioinfo.vander- bilt.edu/webgestalt). Literature mining with HUGO approved gene symbols, associated aliases and keywords, was carried out in PubMed queried through GeneCards (http://www.genecards.org) and with SciMiner (http:// jdrf.neurology.med.umich.edu/SciMiner/). Quantitative PCR confirmation of microarray results Primer pairs design, cDNA preparation, thermocycling conditions and equipment has been previously described [11]. Quantification of mRNAs was based on CT values, which is defined as the PCR cycle at which an increase in reporter fluorescence above baseline signal can be detected. Normalization was done with the 18S rRNA as reference transcript assayed under identical conditions respective to the gene of interest in both the test and the reference RNA samples. The ΔΔCT-Sample value (ΔΔCT-Sample = ΔΔCT-Sample - ΔCT-Reference) was trans- formed by taking the result of the expression: If 2(-ΔΔCT) Page 9 of 11 - 1 > 0 then the result = 2(-ΔΔCT) - 1 or else the result = -1 / 2(-ΔΔCT) This calculation converted the linear range for down regulation from 1®0 to 0®(-∞), and up regu- lation from 1®(+∞) to 0®(+∞) in the log2 scale. Additional file 1: Contains: Table 1: Detailed microarray ratios and Q-PCR results of selected transcripts. Table 2: Functional analysis of genes correlated to all phenotypes. Table 3: Gene Ontology profile of genes differentially expressed between SWR and the remaining three strains. Figure 1: Summary of statistical tests among ovarian transcriptional profiles of four mouse strains. Additional file 2: Contains the complete, loess normalized dataset for 14,586 transcripts. Additional file 3: Contains results for all 145 clones correlated both to OTF and NL in mouse ovaries. Additional file 4: Functional profile of transcripts correlated both to OTF and NL in the mouse ovary Acknowledgements UU was a fellow at the ORFD Program, OIA, National Cancer Institute (NCI) at National Institutes of Health (NIH). This work was funded in part with Federal Funds from the NCI, NIH under contract N01-C0-12400. The bioinformatic section of this paper was supported by the grant I205/09-2 from Vicerrectoria de Investigacion y Desarrollo (VID), Universidad de Chile. The pilot Microarray facility at ICBM, Facultad de Medicina, Universidad de Chile has been supported by the grant Mecesup UCH0115. Travel funding to attend the 2009 X-Meeting was granted by VID, Universidad de Chile. This article has been published as part of BMC Genomics Volume 11 Supplement 5, 2010: Proceedings of the 5th International Conference of the Brazilian Association for Bioinformatics and Computational Biology. The full contents of the supplement are available online at http://www. biomedcentral.com/1471-2164/11?issue=S5. Author details 1Laboratorio de Genómica Aplicada, ICBM, Universidad de Chile, Independencia 1027, Santiago, Chile. 2Laboratory of Molecular Technology, SAIC-NCI Frederick, 915 Tollhouse Avenue, Suite 211, Frederick, MD 21701. 3Laboratory Animal Sciences Program, SAIC-NCI Frederick, Frederick, MD 21702. 4Center for Comparative Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030. Authors’ contributions UU designed the study, performed microarray experiments, data analysis, interpretation and wrote the manuscript; GAO handled clone collection and manufactured microarrays; GMZ carried out animal care and tissue harvest; JMC performed Q-PCR assays; JJS and DJM participated in study design and coordination, and critically revised the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Published: 22 December 2010 References 1. Riman T, Nilsson S, Persson IR: Review of epidemiological evidence for reproductive and hormonal factors in relation to the risk of epithelial ovarian malignancies. Acta Obstet Gynecol Scand 2004, 83(9):783-95. 2. Purdie DM, Bain CJ, Siskind V, Webb PM, Green AC: Ovulation and risk of epithelial ovarian cancer. Int J Cancer 2003, 104:228-32. 3. Choi JH, Wong AS, Huang HF, Leung PC: Gonadotropins and ovarian cancer. Endocr Rev 2007, 28(4):440-61. 4. Garson K, Shaw TJ, Clark KV, Yao DS, Vanderhyden BC: Models of ovarian cancer-are we there yet? Mol Cell Endocrinol 2005, 239(1-2):15-26. 5. Silver LM: Mouse genetics. Concepts and applications. Oxford University Press; 1995.