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

机译:基因组广泛搜索以鉴定Gossypium Hirsutum中基因表达分析的参考基因候选者

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摘要

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. 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. 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.
机译:棉是最重要的商业作物之一,是天然纤维,油和饲料的来源。为了保护其免受有害的害虫群体,已经开发出更新的转基因系数。为了在成功的农业中进行快速表达检查,QPCR(定量聚合酶链反应)变得非常流行。当方法与参考文献相比,选择适当参考基因的选择在这些实验的结果中起着关键作用。传统上,最常用的参考基因是“储屋基因”,涉及基本的细胞过程。然而,这种基因的表达水平通常响应于实验条件而变化,迫使研究人员验证每个实验平台的参考基因。本研究提出了一种数据科学驱动了非偏见的基因组,通过评估棉花种类Gossymium hirsutum的公共RNA-SEQ数据集中的>?50,000基因的变异来选择参考基因。通过数据科学驱动分析鉴定的五个基因(TMN5,TBL6,UTR5B,AT1G65240和CYP76B6)以及在由不同的33个实验样本中通过QPCR捕获文献中发现的两个常用参考基因​​(PP2A1和UBQ14)中的常用参考基因组织(叶,方形,茎和根),叶(幼年和成熟)的不同阶段和转基因和非转基因植物的方形发育(小,中等和大)。使用四种算法,BESTKEEKER,NORMFINDER和REFFIDED评估基因的表达稳定性。基于该结果,我们建议使用TMN5和TBL6作为具有正常和转基因棉植物组织的QPCR实验中的最佳候选参考基因。如果表达研究包括正方形,也可以使用AT1G65240和PP2A1。本研究首次成功地显示了数据科学驱动的基因组视图方法,然后是实验验证作为选择基于单独的功能的选择选择稳定参考基因的方法。

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