2023
DOI: 10.3390/plants12091810
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Effects of Magnesium on Transcriptome and Physicochemical Index of Tea Leaves

Abstract: Magnesium (Mg) is one of the essential elements for the growth of tea tree and is extremely important for its development. In this study, we investigated the effect of Mg on the transcriptome and physicochemical indexes of tea leaves, and the results showed that Mg could significantly affect the gene expression of tea leaves. The results of Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) model analysis showed that a total of 300 key genes (Variable Importance for the Projection, VIP > 1) we… Show more

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Cited by 12 publications
(4 citation statements)
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“…Orthogonal partial least squares discriminant analysis (OPLS-DA) can be used to model the relationship among different indexes and samples to screen key genes and characterize sample differences by variable importance projection values (VIP values) (Zhang et al, 2023a). Meanwhile, in order to test the reliability of the model, a permutation test is usually used to validate the accuracy of the model (Zhang et al, 2023b).…”
Section: Transcriptomic Analysis Of Tea Leavesmentioning
confidence: 99%
“…Orthogonal partial least squares discriminant analysis (OPLS-DA) can be used to model the relationship among different indexes and samples to screen key genes and characterize sample differences by variable importance projection values (VIP values) (Zhang et al, 2023a). Meanwhile, in order to test the reliability of the model, a permutation test is usually used to validate the accuracy of the model (Zhang et al, 2023b).…”
Section: Transcriptomic Analysis Of Tea Leavesmentioning
confidence: 99%
“…Difference analysis: DESeq2 v1.22.1 was used to analyze the differential expression between the two treatments, and the P value was corrected using the Benjamini & Hochberg method. The corrected P value and |log2foldchange| were used as the threshold for significant difference expression [48].…”
Section: Discussionmentioning
confidence: 99%
“…raw data were filtered using fastp to remove the adapter sequence of reads to obtain clean reads for subsequent analysis ( Robinson et al., 2011 ). The clean reads obtained were matched with the reference genome (gcf_004153795.1_ahu_css_1_genomics.fna.gz), and the matching efficiency was analyzed ( Zhang et al., 2023 ). After matching was completed, gene matching was calculated using featureCounts, and then the FPKM of each gene was calculated based on gene length and used to quantify gene expression level ( Liao et al., 2014 ).…”
Section: Methodsmentioning
confidence: 99%