Transcriptome-based Selection and Validation of Optimal House-keeping Genes for Skin Research in Goats (Capra hircus)

Abstract

Background: In Quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression data is dependent on optimal amplification of house-keeping genes (HKGs). The 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 be used in skin research of goats have not been identified. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using the new high-throughput sequencing technology.Results: Based on the transcriptome dataset of 39 goat skin tissues, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. The classical HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also considered. Four different experimental materials: (1) different development stages, (2) hair follicle cycle stages, (3) breeds and (4) sampling sites were provided 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 observed that NCBP3+SDHA+PTPRA was more stably expressed than previously used genes, in all conditions analyzed. This combination was effective at normalizing target gene expression. Moreover, a new algorithm, ComprFinder, was developed and released for comprehensive analysis.Conclusion: This study presents the first data of candidate HKGs selection for skin tissues of C. hircus based on an RNA-seq dataset. We propose the use of the NCBP3+SDHA+PTPRA combination as the triplet HKGs in skin molecular biology in C. hircus and other closely related species in order to standardize analyses across studies. In addition, we also encourage researchers who are performing candidate HKG evaluations and have the needs of a comprehensive analysis to adopt our new algorithm, ComprFinder.

Identification of optimal and novel reference genes for quantitative real‐time polymerase chain reaction analysis in grapevine

“…It is necessary to summarise the results from the three algorithms and give a final ranking to choose the optimal reference genes for qRT-PCR assays. The ComprFinder is a newly developed algorithm, which comprehensively analyses the results from more than three algorithms and gives a final score for each reference gene (Zhang et al 2020). RankAggreg can evaluate the consistency of three different results and provides an integrated ranking based on the cross-entropy method (Pihur et al 2009).…”

“…To comprehensively assess these reference genes and give a final ranking, the ComprFinder algorithm was used by integrating results from three algorithms (geNorm, NormFinder and BestKeeper) according to the method described by Zhang et al (2020). ComprFinder can calculate a score value, and genes can be ranked according to this score value from low to high.…”

Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research

“…The qRT-PCR experiments were performed on different experimental samples including 11 types of breast cancer tissues and seven different breast cancer cell lines. The stability of expression of these candidate RGs was calculated using geNorm [24], NormFinder [25], ΔCtmethod [26], BestKeeper [27], and ComprFinder [28]. Finally, the optimal RGs were validated and confirmed.…”

“…Based on the method described in our previous study [28], the relationship among different normalized RGs was explored. As shown in Additional file 2: Fig.…”

“…All RGs were amplified using qRT-PCR for subsequent determination and validation. The probability density curves were drawn using Matlab scripts from our previous study [28]. Venn diagram analysis was performed using a webtool (http:// www.…”