Zhou et al. BMC Genomics 2011, 12:124 http://www.biomedcentral.com/1471-2164/12/124 RNA extraction and microarray analysis was as described above, except that Affymetrix Mouse Genome 430 2.0 GeneChips® (Affymetrix, Santa Clara, CA) were used. The Mouse Genome 430A chip contains over 22,600 probe sets representing transcripts and variants from over 14,000 well-characterized mouse genes. The newer Mouse Genome 430 2.0 Array contains all of the probe sets present on the earlier 430A chip plus additional probe sets for a total of approximately 45,000 probe sets that analyze the expression of over 39,000 transcripts and variants from over 34,000 well characterized mouse genes. The differences in feature size and probe set con- tent make direct comparisons inappropriate, due to scanning and scaling issues, but because the probe sets on the 430A are present on the 430 2.0 array, those can be compared at the level of gene lists. The data from independent arrays (each with RNA from a single embryo) for each of the treatments were extracted using the Affymetrix Microarray Suite 5.0 (MAS5) algorithm. Data for both experiments have been deposited in GEO/NCBI and have been assigned series accession number GSE9545 and sample numbers GSM241642 through GSM241660. To minimize false positive results, only genes detected ("present” by the MAS5 algorithm) on at least half of all individual arrays in at least one experimental condition were retained for further analysis. This avoids data that primarily represent “noise” [72,73]. To detect differentially expressed genes, control samples were compared to ALC-NTC samples, or ALC- NTO samples, or their combination, using a Welch’s t-test on the log-transformed signals. To see genes that were similarly affected in both experiments, we inter- sected the gene lists. To avoid missing genes that met a stringent significance threshold in one experiment but were just beyond that threshold in the second, we chose p ≤ 0.05 as the threshold for each experiment. Given that the two experiments were independent, the prob- ability that a gene overlaps by chance and differs in the same up/down direction in both experiments is (0.05)* (0.05)/2 = 0.00125. False discovery rate (FDR) was calcu- lated based on the number of genes expected to be sig- nificant and in the same direction in both experiments under the hypothesis/the number of such genes actually found. Hierarchical clustering with average linkage function was used to construct a dendrogram based upon all genes that were present on at least half of the arrays in an experimental group. Gene Set Enrichment Analysis (GSEA) [74,75] was carried out to identify groups of related genes that were differentially expressed. GSEA analyses were conducted for 4 different comparisons: control vs. ALC, control vs. ALC/NTC, control vs. ALC/NTO, and ALC/NTC vs. Page 14 of 17 ALC/NTO. The top ranked genes in a significant gene set, in the region up to the maximum score, were con- sidered significant. To reduce multiple testing issues, the GSEA in this study was conducted using two gene set databases designed to test the hypotheses that groups of genes related to Early Development or Stem Cells were differentially affected by alcohol. (a) Early Developmental Biology Gene Sets (Additional file 1): 415 GO categories that were defined by 29 key words were selected (identified gene sets, Additional file 2). (b) Stem Cell Related Gene Sets: 191 GO categories related to stem cells, neurogenesis, osteogenesis, extra- cellular matrix, developmental signal transduction path- way, cell cycle, growth factor, TGFb/BMP signaling, Wnt signaling, and notch signaling were developed by Superarray Bioscience http://www.superarray.com. The gene set information is listed in Additional file 3 (shown with consent of Superarray Bioscience, Frederick, MD). Table 8 Primers for qRT-PCR Primer Bhlhb5-f Bhlhb5-r Ctgf-f Ctgf-r Dll 1-f Dll 1-r Efemp1-f Efemp1-r Gapdh-f Gapdh-r Igf1-f Igf1-r Igfbp2-f Igfbp2-r Ntf3-f Ntf3-r Mylc2a-f Mylc2a-r Neurog1-f Neurog1-r Neurog2-f Neurog2-r Sox5 f Sox5 Tieg1-f Tieg1-r Edil3-f Edil3-r Sequence CCTATTCAACAGCGTCTCGTCC GCTTCTCACTTTCCTCTAGCTTTGG AGATTGGAGTGTGCACTGCCAAAG TCCAGGCAAGTGCATTGGTATTTG ATAGCGACTGAGGTGTAAGATGGAAGC CTTCGCCTGAACCTGGTTCTCAG TCTACCTACGACAAACAAGCCCTGTG AGAGCTTGTGCGGAAGGTTCCTATAC TCCTGGTATGACAATGAATACGGC TCTTGCTCAGTGTCCTTGCTGG ACTGACATGCCCAAGACTCAGAAGTC TGCCTCCGTTACCTCCTCCTGTTC CACAGCAGGTTGCAGACAGTGATG CAGCTCCTTCATGCCTGACTTGAG TGGTTACTTCTGCCACGATCTTACAGG CTCCTTTGATCCATGCTGTTGCC GGAAGAGTTCAAGCAGCTTCTC ACTTGTAGTCAATGTTGCCGGC TCCCTCGGCTTCAGAAGACTTCAC AGGCCTAGTGGTATGGGATGAAACAG GCGTAGGATGTTCGTCAAATCTG TCCGCGCTGGAGGACATC AATATGAGTGGAGATTCTGACGGAAGC GGCATTCATTGGACGCTTTATGTG CAGTCCCAGCATTTTGTTTAACGC GCAGCATCGGAGAAAGATTTGAAG GCTCTCAGGCTGTTCAGAACCTTTG GGCTTTCCTTGGTTCCCAAGTAAAC RefSeq ID NM_021560 NM_010217 NM_007865 NM_146015 NM_008084 NM_184052 NM_008342 NM_008742 NM_023402 NM_010896 BC055743 AI528773 NM_013692 AF031524 Primers are named according to the gene with-f for forward primer and -r for reverse primer.