Gene Ontology (GO) is an international standard classification system for gene function. GO enrichment analysis is to classify the differential genes according to GO, and perform the significance analysis based on the discrete distribution of the classification results, the error rate analysis, and the enrichment analysis to obtain the gene function classification that is significantly related to the experimental purpose. This classification is the most important functional difference that leads to differences in sample traits. In the data analysis of the chip, the researcher can find out which changed genes belong to a common GO functional branch, and use statistical methods to check whether the results are statistically significant, so as to obtain which biological functions the changed genes are mainly involved in.
- CD ComputaBio performs significant function enrichment analysis on mRNA, and obtains significant and targeted functions and target genes corresponding to significant functions.
- CD ComputaBio's significant function of enrichment is displayed in the form of a histogram.
- CD ComputaBio displays the significant functions obtained by enrichment in the form of dot diagrams.
- One of the main applications of the GO is to perform enrichment analysis on gene or protein sets. For instance, given a set of proteins that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) via annotations for that protein set.
- The GO enrichment analysis have proven to be remarkably useful for the exploring of functional and biological significance from very large datasets, such as Mass Spectral data and microarray results.
- The GO enrichment analysis also facilitates the organization of data from novel, (or fully annotated) genomes and the comparison of biological functions between clade members and across clades.
||GO enrichment service
|Our service process
- Determine the annotated GO terms and all splits.
- Count the number of appearances of each GO term for the proteins in the tested set as well as in the reference set.
Calculate a p-value representing the probability that the enriched numbers of counts could have resulted from randomly distributing this GO term between the tested set and the reference set.
||Decide according to your needs.
||We provide you with raw data and analysis service.
CD ComputaBio' GO enrichment analysis can significantly reduce the cost and labor of the subsequent experiments. GO enrichment analysis is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.
* For Research Use Only.