Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus)

Background 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). 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 ofC. hircususing high-throughput sequencing technology. Results Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68,NCBP3,RRAGA,EIF4H,CTBP2,PTPRA,CNBP, andEEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs includingSDHAandYWHAZfrom a previous study, and 2 conventional genes (ACTBandGAPDH) 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 Delta Ct method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown thatNCBP3 + SDHA + PTPRAwere 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. Conclusion This study presents the first list of candidate HKGs forC. hircusskin tissues based on an RNA-seq dataset. We propose that theNCBP3 + SDHA + PTPRAcombination could be regarded as a triplet set of HKGs in skin molecular biology experiments inC. hircusand 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.