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Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq.

ABSTRACT: BACKGROUND: Thoroughbred horses are the most expensive domestic animals, and their running ability and knowledge about their muscle-related diseases are important in animal genetics. While the horse reference genome is available, there has been no large-scale functional annotation of the genome using expressed genes derived from transcriptomes. RESULTS: We present a large-scale analysis of whole transcriptome data. We sequenced the whole mRNA from the blood and muscle tissues of six thoroughbred horses before and after exercise. By comparing current genome annotations, we identified 32,361 unigene clusters spanning 51.83?Mb that contained 11,933 (36.87%) annotated genes. More than 60% (20,428) of the unigene clusters did not match any current equine gene model. We also identified 189,973 single nucleotide variations (SNVs) from the sequences aligned against the horse reference genome. Most SNVs (171,558 SNVs; 90.31%) were novel when compared with over 1.1 million equine SNPs from two SNP databases. Using differential expression analysis, we further identified a number of exercise-regulated genes: 62 up-regulated and 80 down-regulated genes in the blood, and 878 up-regulated and 285 down-regulated genes in the muscle. Six of 28 previously-known exercise-related genes were over-expressed in the muscle after exercise. Among the differentially expressed genes, there were 91 transcription factor-encoding genes, which included 56 functionally unknown transcription factor candidates that are probably associated with an early regulatory exercise mechanism. In addition, we found interesting RNA expression patterns where different alternative splicing forms of the same gene showed reversed expressions before and after exercising. CONCLUSION: The first sequencing-based horse transcriptome data, extensive analyses results, deferentially expressed genes before and after exercise, and candidate genes that are related to the exercise are provided in this study.

SUBMITTER: Park KD

PROVIDER: S-EPMC3472166 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq.

Park Kyung-Do KD Park Jongsun J Ko Junsu J Kim Byung Chul BC Kim Heui-Soo HS Ahn Kung K Do Kyoung-Tag KT Choi Hansol H Kim Hak-Min HM Song Sanghoon S Lee Sunghoon S Jho Sungwoong S Kong Hong-Sik HS Yang Young Mok YM Jhun Byung-Hak BH Kim Chulhong C Kim Tae-Hyung TH Hwang Seungwoo S Bhak Jong J Lee Hak-Kyo HK Cho Byung-Wook BW

BMC genomics 20120912

<h4>Background</h4>Thoroughbred horses are the most expensive domestic animals, and their running ability and knowledge about their muscle-related diseases are important in animal genetics. While the horse reference genome is available, there has been no large-scale functional annotation of the genome using expressed genes derived from transcriptomes.<h4>Results</h4>We present a large-scale analysis of whole transcriptome data. We sequenced the whole mRNA from the blood and muscle tissues of six ...[more]

PMID: 22971240

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Project description:Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Dataset Information

Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq.

Publications

Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq.

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