Regassa et al. BMC Genomics 2011, 12:57 http://www.biomedcentral.com/1471-2164/12/57 ethanol mixture were pipetted into the preconditioned purification column. RNA was bound to the column by centrifuging for 2 min at 100 × g, immediately followed by a centrifugation at 16,000 × g for 30 sec to remove flow through. One hundred μL wash buffer 1 was pipetted into the purification column and centrifuged for 1 minute at 8,000 × g. Contaminating DNA was removed by DNase treatment. One hundred μL of wash buffer 2 (W2) was pipetted into the purification column and centrifuged for 1 min at 8,000 × g. Another 100 μL W2 was pipetted into the purification column and cen- trifuged for 2 min at 16,000 × g. The purification col- umn was checked for any residual wash buffer by re-centrifuging at 16,000 × g for 1 min and transferred to a new 0.5 mL micro centrifuge tube provided in the kit. Eleven μL of elution buffer was pipetted directly onto the membrane of the purification column and finally, the isolated total RNA was preserved in the elu- tion buffer and used for down stream applications. RNA quality and yield of each sample were determined using Bioanalyzer 2100 and RNA 6000 Pico LabChip assay (Agilent Technologies Inc, Palo Alto, CA) in com- bination with Quant-iT™ RiboGreen Reagent according to supplied protocols (Invitrogen, Carlsbad, CA). Initial total RNA concentration across all samples were adjusted to the lowly concentrated sample (12 ng) and this amount of total RNA were used for the two round cDNA synthesis and subsequent in vitro-transcription according to the two-cycle eukaryotic target labeling assay (Affyme- trix expression analysis technical manual: Eukaryotic sample and array processing http://www.affymetrix.com/ support/technical/manual/. MEGA script in vitro tran- scription kit containing T7-Oligo (dT) primer and other components (MEGAscript® high yield transcription kit, Applied Biosystems, Ambions) and random primers (Invitrogen, Karlsruhe, Germany) were used for the first and second cycle of cDNA synthesis. Fifteen μg of frag- mented and biotin-labeled complementary RNA (cRNA) of each group were hybridized with Affymetrix bovine Genome 430 v2.0 GeneChip® arrays for 16 hrs at 45°C. Post-hybridization staining and washing were performed according to manufacturer’s protocols using the Fluidics Station 450 instrument. Image capturing, quantification and data analysis Array slides were scanned with a GeneChip™ 3000 laser confocal slide scanner (Affymetrix) and the images were quantified using Gene Chip Operating Software (GCOS, Affymetrix) version 1.2. Probe level data were imported into the R software environment http://www.r-project.org. Data normalization and background correction were per- formed using guanine-cytosine Robust Multichip Average (gcRMA) function as described in [54]. gcRMA adjusts for background intensities in Affymetrix array data which Page 4 of 20 include optical noise and non-specific binding (NSB). The main function gcRMA converts background adjusted probe intensities to expression measures using the same normalization and summarization methods as RMA (Robust Multiarray Average). It uses probe sequence infor- mation to estimate probe affinity to non-specific binding (NSB). The sequence information is summarized in a more complex way than the simple GC content. Instead, the base types (A,T,G or C) at each position (1-25) along the probe determine the affinity of each probe. The para- meters of the position specific base contributions to the probe affinity are estimated in an NSB experiment in which only NSB but no gene-specific binding is expected. The presence or absence of genes were detected using microarray suit 5 (MAS 5, Affymetrix) as described in [42,55]. To minimize false positive signals, genes called absent were avoided and these called present in at least two of the three replicates were used for further analy- sis. Differential gene expression was analyzed using lin- ear models for microarray (LIMMA) as described in [56]. LIMMA is a package for differential expression analysis of data arising from microarray experiments. The package is designed to analyze complex experi- ments involving comparisons between many RNA tar- gets simultaneously while remaining reasonably easy to use for simple experiments. The central idea is to fit a linear model to the expression data for each gene. The expression data can be log-ratios, or sometimes log- intensities, from two colour microarrays or log-intensity values from one channel technologies such as Affyme- trix. The MIAME guidelines with details of sample pre- paration, experimental design, array processing and hybridization, measurements and normalisation controls are given in additional file 1. Differentially expressed genes in experiments 1A and B were classified according to their gene ontology (GO) using GO consortium [57] and lists of genes over expressed in OO + CCs and CCs + OO relative to those expressed in OO - CCs and CCs - OO respectively, were uploaded into Ingenuity Pathways Analysis (IPA), (Ingenuity Systems, http://www.ingenuity.com) to iden- tify relationships between the genes of interest and to uncover common processes and pathways in the positive phenotypes. IPA is a web-based software application that enables the modelling and analysis of biological sys- tems using microarray data. Validation of the microarray data using semi quantitative and quantitative real time RT-PCR Array data was validated using real time quantitative reverse transcription PCR (qRT-PCR). For this, addi- tional pools of biological replicates representing inde- pendent oocytes and CCs samples (n = 150) were used for total RNA isolation and cDNA synthesis. In order to