LOGO title
 Search Sinbase2.0

WWW SITE
 Resources>>Transcriptomics Analysis>>Three Color Seeds:
I. Phenotype for sesame seeds with different color

Sample instruction: sesame pericarp with 10 day; white, black and brown seeds, Each type of seeds with 4 time points (10,20,25,30 days).

II. Expression analysis among different developmental stages in sesame white and black seeds

In this project, we employed TopHat2 [1] to calculate the expression values of sesame genes among 13 samples. After curation, 22,250 genes, representing 81.958% of all sesame genes, were detected to express among 13 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, the members of module were confirmed with the parameter: kME > 0.7, FPKM > 1 and minimum module size: 30. After curation, 11,895 genes were used to perform co-expression analysis of sesame genes among 13 samples.

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.

 

Copyright © 2017 Sesame Germplasm Resources Group, OCRI, CAAS, Admin: yujingyin@caas.cn