Abstract
BackgroundIn quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology.ResultsBased on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown that NCBP3 + SDHA + PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released.ConclusionThis study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3 + SDHA + PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.
Highlights
In quantitative real-time polymerase chain reaction experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs)
Selection of novel candidate HKGs based on RNA Sequencing (RNA-seq) data From a complete transcriptome dataset, the fragments per kilobase of exon model per million mapped reads (FPKM) of all transcripts from each sample were obtained
While this study demonstrated the advantages of using RNA-seq datasets in the discovery of new HKGs, it is possible that the prediction of HKGs by RNA-seq datasets may be lacking in some respects
Summary
In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs). Uniform and reliable HKGs for skin research have not been identified in goat. This study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology. In molecular biology research, determining the relative changes in target gene expression at the transcriptional level requires precise quantitative analysis. The emergence and development of quantitative real-time polymerase chain reaction (qRT-PCR) has enabled comprehensive mRNA quantification. The copy number of nucleic acid was calculated through the changes in real-time fluorescence reaction. The qRT-PCR assay relies on house-keeping genes (HKGs) to obtain relative gene expression data [4, 5], choosing HKGs has become a major source of error and bottlenecks in qRT-PCR experiments
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