Park et al. BMC Genomics 2012, 13:473 http://www.biomedcentral.com/1471-2164/13/473 Background The thoroughbred, a “hot-blooded” horse breed, is the favorite breed for use in horse racing [1]. The speed and agility of thoroughbred horses has resulted in the emergence of an industry involved in the breeding, training, and racing of elite racehorses worth many billions of dollars [2]. Until now, rela- tively few genes related to their athletic phenotypes have been identified, even though physical and physiological adaptations underlying their elite ath- leticism are well characterized [3]. Muscle is the most critical tissue for athletic performance. The skeletal muscle of the thoroughbred horse comprises over 55% of its total body mass [4,5] and has re- markable functional and structural plasticity [3]. Fur- thermore, over 90 hereditary conditions in horses have corresponding human disorders [3,6], and many muscle disorders in humans and horses share com- mon clinical and histopathological characteristics, as well as molecular features [7-9]. Therefore, the horse can be an invaluable animal model for muscle diseases. An international team of researchers has decoded the genome of the domestic horse, Equus caballus, and has reported that its genome structure is re- markably similar to the human genome [8]. An add- itional nine domesticated horse breeds have also been sequenced, identifying around one million sin- gle nucleotide polymorphisms (SNPs) [8]. However, there has been little progress in refining the func- tional annotation of horse genome using expressed genes. Although a small number (around 30,000) of expressed sequence tags (ESTs) has been deposited in the dbEST [10], this is insufficient to identify all the key genes related to specific functions, such as racing performance. RNA-Seq is one of the most useful next generation se- quencing (NGS) methods used to fully understand the landscape of a transcriptome, because it produces sev- eral tens of millions of short reads (17 bp to 101 bp) from the expressed genes in vivo. RNA-Seq has been used successfully to investigate the transcriptome pro- files of human, mouse, Arabidopsis, and yeast [11-14]. The RNA-Seq data generally exhibit a high degree of concordance with established gene annotations [15,16]. Using RNA-Seq, researchers have identified numerous novel genes and additional alternative splicing forms [14,17,18], as well as unraveling expression profiles underlying phenotypic changes, such as development stages [19,20]. In addition, RNA-Seq permits the identifi- cation of single nucleotide variations (SNVs) in coding regions from various organisms because of the large number of reads [14,21,22]. Moreover, RNA-Seq has identified novel unannotated transcriptionally active Page 2 of 8 regions in rice [23], indicating that there are novel genes that cannot be detected by conventional gene prediction methods. In horses, two transcriptome studies using RNA- Seq have been reported: one study refined the struc- tural annotation of protein-coding genes based on the RNA-Seq sequences from several equine tissues [24], while the other analyzed RNA-Seq sequences acquired from skeletal muscle to find long-term training-related genes [25]. Both studies used very short RNA-Seq sequences, 17 bp and 35 bp, respect- ively, because of the limitation of NGS technologies available at that time. These very short-read RNA sequences have one critical limitation. When aligned against the reference genome, the typical success rate is as low as 66% in the case of 17 bp RNA frag- ments [24]. This was caused not only by the very short-read sequences, but also by intron junctions, which were not included in the short-reads. This disadvantage must be overcome to advance horse transcriptome research. Here, we present a large scale analysis of whole tran- scriptome data. The samples were taken from blood and muscle tissues of six thoroughbred horses before and after 30 minutes of exercises, resulting in 24 samples. Results and discussion Gene cluster analysis and identification of novel transcripts from the horse RNA-Seq sequences To construct high quality horse transcriptome data, we generated over 1.3 billion 90-bp pair-end reads using an Illumina HiSeq2000 (Additional file 1: Figure S1, Additional file 1: Table S1, and Table S2). Using TopHat [26] and Cufflinks [27], 84.60% of all the reads were successfully mapped against the current horse reference genome (Additional file 1: Table S3). A novel bioinfor- matics protocol for processing large amounts of tran- scriptome sequences was built (Additional file 1: Figure S2). RNA sequences were obtained from 24 different samples; therefore, we defined a new concept, unigene cluster (UC), which contains overlapped unigene sequences originating from multiple samples. Utilizing the current annotation (Ensembl 62), 32,361 unigene clusters (UCs), with a total length of 51.83 Mb, were identified. 11,933 UCs matched current gene models, which comprised 36.87% of the 32,361 UCs (Figure 1A and Additional file 1: Supplementary Methods) [8]. The remaining 20,428 UCs (63.13%), which contained more than 60% of the transcripts, were novel (Additional file 1: Supplementary Methods and Additional file 1: Figure S3). The expressions of eight randomly selected novel UCs were confirmed by reverse transcription PCR (Additional file 1: Figure S4 and Additional file 1: Table S4). In addition, the unmapped raw sequences were processed by