BMC Genomics 2009, 10:490 http://www.biomedcentral.com/1471-2164/10/490 dicted endothelial transcriptome gene list [15,16] was compared with the genes in this data set to find those endothelial up regulated or specific. These genes that had a False Discovery Rate (FDR) qvalue of < = 0.01 were classed as endothelial. Cold response genes: persistently up-regulated genes Cold response genes were found mining the microarray data from Gracey et al. 2004 [26]. Differentially expressed cDNAs for the heart and muscle were taken directly from the data without any processing. These were the first list of cold-induced genes. Then a second list was generated on the hypothesis that angiogenesis is switched on at initial cooling and switched off after some time. Therefore, two further strat- egies were employed. Cold response genes: ratio subtraction Gracey et al. 2004 [26] harvested RNA and ran microar- rays for several time points at each drop in temperature (see their figure 1). As angiogenesis is hypothesised to be switched on at initial cooling, subtracting gene expression ratio on the 1st day of cooling vs. the expression ratio on the last day will show which genes showed highest expres- sion on initial cooling. This can be expressed as a simple equation: A = Average gene expression fluorescence at 30°C, the control temperature B = Fluorescence intensity at day 1 of cooled temperature e.g. 17°C at day 1 C = Ratio of fluorescence at day 1 vs. the control i.e. B/A D = Fluorescence intensity at last day of cooled tempera- ture e.g. 17°C at day 4 E = Ratio of fluorescence at last day vs. the control i.e. D/A R = ratio subtraction = C - E The higher value of R means gene expression was higher on the first day as compared to the last day. Cold-response genes: microarray methods Pre-processed and normalised gene expression data were obtained from the study [26]. Analysis was carried out using Limma from BioConductor R package [68], a gen- eral linear model approach that uses an empirical Baye- sian smoothing [115] method to gain power when a large number of predictors (i.e. probes) are present with a small number of cases (i.e. arrays). Contrasts were employed within Limma to detect probes for which the change in expression between the first day of cooling and the con- trol was significantly different to the change in expression between the last time points and the control. The Ben- jamini and Hochberg method [116] was used to correct for multiple testing. Amongst significant probes, different patterns were observed. However, angiogenesis-related genes are predicted to be up-regulated in the initial stages of cooling, before the animal has become acclimatised to a particular temperature; therefore, genes that were up- regulated at the 1st time point but showed no differential regulation at the last day of cooling were selected from this list. An adjusted p-value < = 0.05 was used as a cut-off. Comparison with Homologene Homologene orthologous group data was downloaded from the Homologene ftp site at the NCBI [117]. Then a Perl program compared the gene symbol assignments for human and zebrafish between the two data sources. addi- tional file 6 contains the results. Programs for running ortholog searches See http://www.cbrg.ox.ac.uk/~jherbert/ for programs enabling high-throughput ortholog assignment for incomplete nucleotide sequences. Please contact the first author for guidance as some customisation maybe needed depending on species. Abbreviations Carp: Cyprinus carpio; RBH: Reciprocal Best Hit; SRBH: Stepped Reciprocal Best Hit; CSRBH: Conditional Stepped Reciprocal Best Hit; BLAST: Basic Local Alignment Search Tool; BLAT: BLAST Like Alignment Tool; cDNA: comple- mentary DNA; DDD: Digital Differential Display; EST: Expressed Sequence Tag; VEGF: Vascular Endothelial Growth Factor; EC: Endothelial Cell; GEO: Gene Expres- sion Omnibus; SAGE: Serial Analysis of Gene Expression; SW: Smith-Waterman; ECM: extracellular matrix; CGAP: Cancer Genome Anatomy Project; NCBI: National Center for Biotechnology Information; EBI: European Bioinfor- matics Institute Authors' contributions JH performed the bioinformatic analyses and co-wrote the paper. FB performed the re-analysis of the microarray data. HV aided in interpretation of the data and helped write the paper. SE and RB conceived the overall project, helped with the interpretation of the data and writing of the paper. All authors read and approved the final manu- script. Page 13 of 17 (page number not for citation purposes)