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