Park et al. BMC Genomics 2012, 13:473 http://www.biomedcentral.com/1471-2164/13/473 filter (Additional file 1: Figure S5, Additional file 1: Tables S10, S11, S12, and Additional file 1: Supplementary Methods). The filters had been validated on mouse RNA-Seq data [29] by showing that 80.28% of the identi- fied SNPs were confirmed in two inbred mouse genomes (Additional file 1: Supplementary Methods and Additional file 1: Table S13). Each horse showed a similar number of SNVs, ranging from 72,000 to 77,000 (Additional file 1: Table S11). 82,476 individual-specific SNVs were identified (Additional file 1: Table S12). Only 7,316 out of 182,722 SNPs (4.00%) overlapped with both of the two existing data- bases (Figure 1B). This is because only 1% (10,229 from Broad Institute and 4,287 from Ensembl) of the SNPs from the two databases were located in the exonic region of the current genome annotation (Additional file 1: Tables S11 and S14). These results demonstrate the usefulness of iden- tifying novel SNPs from transcriptome data. Moreover, 116,650 (61.40%) of 189,973 SNVs were located in exons of unigene clusters, and 67,788 (58.11%) of the 116,650 SNVs caused amino acid changes. Some of the transcripts are pos- sibly related to the horses’ running ability. For example, titin (TTN) is related to the passive stiffness of muscle by limiting the range of motion of the sarcomere in tension [30], such that TTN affects the ability of muscle directly [31]. Other examples include obscurin (OBSCN), which is associated with TTN and ANK1 genes [32,33], and the skeletal muscle calcium release channel gene (RYR1) located in the sarco- plasmic reticulum [34,35], whose mutations caused several muscle-related diseases, including central core disease [36]. The SNV distribution profile of the TTN was specific to in- dividual horses (Figure 1C and Additional file 1: Table S15). Comparison of the expressed genes in blood and muscle tissues with other organisms In muscle and blood tissues, 17,484 and 25,220 UCs were identified as expressed genes, respectively, showing that blood expressed about 40% more genes than muscle tissue (Figure 1A). By comparison with two previous RNA-Seq studies conducted in human (Illumina BodyMap2 transcriptome) and in mouse skeletal muscle tissue [12], we observed that the GO classifications of all the expressed genes in the three organisms were similar to each other (Additional file 1: Figure S6). The differences in GO assignments be- tween tissues (muscle and blood) were larger than those of the same tissue among the three species (Additional file 1: Figure S7). Identification and characterization of differentially expressed genes in blood and muscle tissues regulated by exercise We calculated the expression level of all unigenes from the 24 samples. The distribution of 32,361 UCs’ average expression levels in the 24 samples showed that half of Page 4 of 8 the UCs were expressed at less than 0.19 FPKM (frag- ments per kilobase of exon model per million mapped reads) and three-quarters of the UCs were expressed at less than 4.81 FPKM (Additional file 1: Figure S10). The correlation coefficients of the FPKM values among the samples were comparable to previous RNA-Seq studies [15,37] (Additional file 1: Figures S8 and S9). By compar- ing before and after 30 minutes of exercise, we identified 1,285 differentially expressed genes (DEGs), consisting of 62 up- and 80 downregulated UCs in blood and 878 up- and 285 downregulated UCs in muscle (Additional file 1: Supplementary Methods and Figure 1D). While the overall number of all the differentially expressed UCs was much larger in muscle than in blood, the number of novel differentially expressed UCs was larger in blood (42 UCs) than in muscle (8 UCs) (Additional file 1: Table S16). We examined 28 genes that are known to be asso- ciated with racing performance in horses [38]. Twelve of the 25 genes successfully mapped on the genome annotation were expressed in muscle and blood, and six were differentially expressed in muscle (Additional file 1: Table S17). The rest were detected in neither of the two tissues. The six genes upregu- lated by exercise were: HIF1A, which encodes a tran- scription factor that responds to hypoxia; ADRB2, which is involved in the regulation of energy ex- penditure and lipid mobilization from adipose; PPARD, which regulates expression of genes involved in lipid and carbohydrate metabolisms; VEGF, which is an important angiogenic factor recovering the oxy- gen supply to tissues when blood vessels are blocked; TNC, which is located in positive-selected regions for racing performance [4]; and BDNF, which is a candidate gene that may be associated with exercise behavior [39]. We also compared differentially expressed genes (DEGs) in muscle tissue with the 15 upregulated and 53 downregulated DEGs that are associated with ex- ercise training [25] (Additional file 1: Table S18). Among these 68 DEGs, only five genes, ACTR3B, FBXO32, PER3, C1orf51, and GATM, were identified as DEGs in this study, among which C1orf51 and PER3 showed a different expression profile. Sampling the transcriptomes immediately after ex- ercise enabled the identification of differentially expressed early response genes that are rapidly induced by exercise. Many early response proteins include transcriptional regulators, such as Mitogen- activated protein kinases (MAPKs) and NF-κB, which promote fuel homeostasis and prevent skeletal muscle atrophy [40]. In addition, important oxidative stress-sensitive enzymes that can be activated by NF- κB and MAPKs after exercise, such as inducible nitric