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 Resources>>Transcriptomics Analysis>>Determinat and Indeterminate:
I. Phenotypes for determinate and indeterminate sesame samples

Sample instruction: For determinate and indeterminate sesame plants, samples of stem tips were collected at the second true leaf stage (before differentiating) and at the 3rd true leaf stage (in differentiating), respectively, and the first pair of leaves from top were also collected at the 3rd true leaf stage, two biological replicates.

II. Expression analysis among different sesame determinate and indeterminate sesame samples

In this project, we employed TopHat2 [1] to calculate the expression values of sesame genes among 6 samples. After curation, 25,838 genes, representing 95.175% of all sesame genes, were detected to express among 6 samples in sesame.

III. Co-expression analysis by WGCNA

WGCNA is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings [2]. In this project,

Note: Click the different color blocks on the left side of below pictures to get co-expression genes in this experiments

 
Reference:

1. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013; 14(4): R36, doi: 10.1186/gb-2013-14-4-r36.

2. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9(1):559, doi: 10.1186/1471-2105-9-559.

 

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