These genes can be generally classified into two main groups based on their products, effector molecules or regulator molecules, which are also correspondingly designated as delayed-response genes or early-response genes based on their response timing [ 4- 6].
To facilitate this comparison, we also developed a computational pipeline that allowed us to characterize the transcriptional response of each gene based on observations across the entire time-course and between the two genotypes.
Our study demonstrated that large scale coexpression analysis is an efficient approach in characterizing the nutrient response genes based on biological functions and could provide new insights in understanding plant response to nutrient deficiency.
To identify putative MAP kinase cascades in rice − Xoo interaction, we analyzed the co-expression of the three types of genes based on their transcriptional response to pathogen infection.
Among the differentially expressed genes, we focused on 'immune response' genes based on Gene Ontology (GO) classification in order to provide new insights into the expression of chemokines and cytokines, the orchestrated regulation of receptors, the interactions between the two subsets, and the homeostasis of resting T cells.
In summary, we found preliminary support for divergent selection on biotic and abiotic stress response genes based on dN/ dS, consistent with the hypothesis that ecological forces are important in the divergence and potentially in the maintenance of species boundaries in California oaks.
We characterized the responses of 16 selected genes, based on their phylogenetic relationship in response to two fungal pathogens and five hormone treatments.
k-means clustering was used to cluster genes based on the combined expression profiles of injury response and development.
Here we apply a classification system to categorize genes based on the combination of genotypic and environmental response data.
However, generalized gene-based response profiles to both lifestyle and pharmacological interventions may become a regular part of a physician's arsenal to improve clinical care.
Next, we calculated the probability of an expressional gene response based on the change of a gene's expression level in a stress condition relative to the normal growth condition.
