Common functional analysis methods for genes (proteins) include metabolic signaling pathways (pathways) and GO (gene ontology) analysis. In addition, there are COG (protein orthologous clusters), protein domains (protein domains) and other analyses. The application of functional analysis is very common. Taking path analysis as an example, its related literature has been increasing year by year. Therefore, gene function analysis is a common and important analysis content. GO mainly studies gene functions, while pathways study gene and protein functions. GO functions are mainly divided into three categories: BP (biological process), MF (molecular function) and CC (cell composition); among them, the most commonly used is GO BP analysis. Common pathway data include KEGG, Reactome, Biocarta, etc.
Functional enrichment analysis of differentially expressed genes.
Enrichment analysis tools were used to analyze the up-regulated and down-regulated genes involved in CO function and KECG pathway respectively at each time point. The number of parameter enrichment genes count>=2, and the hypergeometric test significance threshold Pvalue<0.05 (considered as a significant enrichment result).
Functional analysis is mainly divided into two categories: functional annotation analysis and functional enrichment analysis.
Functional annotation analysis refers to the annotation of GO and pathway of genes (Annotation)
Gene (protein) common functional analysis methods include metabolic signaling pathway (pathway) and cO (Gene ontology) analysis. In addition, there are COG (Clusters of Orthologous Groups of proteins), protein domain (protein domain) and other analysis.
|Project name||Gene Function Annotation Analysis and
Function Enrichment Analysis Service
Function annotation consists of three main steps:
Identify parts of the genome that do not code for proteins.
The process of identifying elements in the genome is called genetic prediction.
Attach biological information to these elements.
The general steps of enrichment analysis provided by MedAI are summarized as follows:
Calculate a p-value that represents the amount of overexpression of the protein in the list (at the top or bottom of the list).
Evaluate the statistical significance of the node or path based on the p-value.
Normalize the P value of each set and calculate the false discovery rate for multiple hypothesis tests.
|Cycle||Depends on the time you need to simulate and the time required for the system to reach equilibrium.|
|Product delivery mode||The simulation results provide you with the raw data and analysis results of molecular dynamics.|
MedAI provides corresponding professional gene function annotation analysis and function enrichment analysis solutions. Our service has proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). The MedAI team has worked in this field for more than a decade and published his findings in top scientific journals. If you need network analysis services, please feel free to contact us.
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