Genome wide search to identify reference genes candidates for gene expression analysis in Gossypium hirsutum

Abstract

Background Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the “house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of > 50,000 genes in a publicly available RNA-seq dataset of cotton species Gossypium hirsutum. Result Five genes (TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6) identified by data-science driven analysis, along with two commonly used reference genes found in literature (PP2A1 and UBQ14) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm, BestKeeper, NormFinder and RefFinder. Conclusion Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.

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

“…As shown in this study, nine reference genes had different rankings when three different analysing tools were used. In some previous studies, similar results were obtained (Qu et al 2019, Smitha et al 2019, Liu et al 2020). 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.…”

Selection and validation of reference genes for measuring gene expression in Toona ciliata under different experimental conditions by quantitative real-time PCR analysis

“…Software packages, including geNorm [ 24 ], NormFinder [ 25 ], and BestKeeper [ 26 ], are widely used to assess the expression stability of candidate reference genes and determine the best choices. Many researchers have used these algorithms to successfully identify reference genes in different species [ 27 , 28 ]. The use of reference genes in expression analysis has greatly facilitated research in plant development and evolutionary mechanisms in species where a reference genome sequence is available [ 29 ].…”

Relative quantification of BCL2 mRNA for diagnostic usage needs stable uncontrolled genes as reference

“…Required optimal number of clusters was calculated using Silhouette graphical method [ 31 ]. For each tissue sample, the gene cluster with the lowest medoid value of parameters was selected and the genes at the intersection of the four clusters were shortlisted [ 24 ].…”