Identification Of Overlooked Genes In DEG Analysis By Integrating Metabolic Network Topology Analysis | 86553
Journal of Molecular and Genetic Medicine
Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.
Most of the gene expression studies reveal differentially expressed genes (DEG) followed with gene set enrichment analysis (GSEA). Although this approach is practical for reducing the number of targets to engage, it is very much prone to overlook important targets. This is because the enrichment analysis ignores the metabolic pathway topology. A single gene in DEG list that is involved in very crucial reaction will not be identified as Ã¢ÂÂenrichedÃ¢ÂÂ if there are more than few genes are found in same pathway with this candidate gene. Thus, metabolic network topology should be strongly integrated with DEG analysis to uncover genes from a given DEG list which affect critical points in metabolic network. In our study, we parsed and merged available pathway and reaction data to construct whole human metabolic network. Then, by graph theory algorithms, identified critical nodes in whole network, perturbation of which would impact the whole network. To pinpoint overlooked targets in already published or calculated DEG lists, we gathered available DEG lists and expression data and mapped resulting DEG to metabolic network. Our approach could recover previously undetected important genes. As a result, the veil called Ã¢ÂÂenriched geneÃ¢ÂÂ is lifted so that not enriched but critically important genes are exposed. [email protected]
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals