Introduction of KEGG

KEGG (Kyoto Encyclopedia of Genes and Genomes) database is an important bioinformatics data set, which is the integration and interpretation of large-scale molecular datasets generated by genome sequencing and other high-throughput experimental techniques. A variety of genomic, chemical and system functional information have been integrated into the KEGG database, in which biological metabolic pathways are classified into 6 categories, namely, cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismal systems. Aiming to carry on annotations and metabolic pathway analyses of differential expressed metabolites, Creative Proteomics has introduced multiple analysis software packages to perform KEGG enrichment analysis.

Application of KEGG pathway enrichment analysis in biology

KEGG annotations and enrichment of differentially expressed metabolites.  Figure 1. KEGG annotations and enrichment of differentially expressed metabolites. (Meng, Y.; et al. 2019)

Our Services

Creative Proteomics has developed a novel metabolic flux analysis platform to provide KEGG enrichment analysis service in a competitive fashion. We can offer a wide range of services to support all research and development activities.

Advantages of Our Services

Features of Our MFA Platform

Based on high-performance quantitative techniques and advanced equipment, Creative Proteomics has constantly updating our metabolic flux analysis platform and is committed to offering professional, rapid and high-quality services of KEGG enrichment analysis at competitive prices for global customers. Our personalized and comprehensive services can satisfy any innovative scientific study demands, please contact our specialists to discuss your specific needs. We are looking forward to cooperating with you!

Reference

  1. Meng, Y.; et al. Metabolic profile analysis and identification of key metabolites during rice seed germination under low-temperature stress - ScienceDirect. Plant Science. 2019. 289: 110282-110282.

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