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Meta-analysis of lean and obese RNA-seq datasets to identify genes targeting obesity



Lavanya Prabhakar & Dicky John Davis G*



Department of Bioinformatics, Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, Tamil Nadu - 600116, India; *Corresponding author



Lavanya Prabhakar - E-mail: lavanyap@sriramachandra.edu.in

Dicky John Davis G E-mail: dicky@sriramachandra.edu.in


Article Type

Research Article



Received March 1, 2023; Revised March 31, 2023; Accepted March 31, 2023, Published March 31, 2023



Obesity is a global crisis leading to several metabolic disorders. Modernization and technology innovation has been easier for next generation sequencing using open-source online software galaxy, which allows the users to share their data and workflow mapping in an effortless manner. This study is to identify candidate genes for obesity by performing differential expression of genes. RNA-Seq analysis was performed for six different datasets retrieved from GEO database. 258 datasets from obese patients and 55 datasets from lean patients were analysed for differentially expressed genes (DEGs). DEGs analysis showed 1971 upregulated genes and 615 downregulated genes with log2FC count ≥ 2.5 and p-value < 0.05. The Gene enrichment analysis performed using Gene Ontology resource highlighted pathways associated to obesity such as cholesterol metabolism, Fat digestion and absorption and glycerolipid metabolism. Using string database protein-protein interactions network was built and the network clusters were visualized using Cytoscape software. The protein-protein interactions of the upregulated and downregulated genes were mapped to form a network, wherein PNLIP (Pancreatic lipase) and FTO (Fat mass and obesity associated protein) gene clusters were visualized as densely connected clusters in MCODE. PNLIP and FTO with its associated genes were identified as candidate genes for targeting obesity.



Differential gene expression (DEG), Galaxy server, Gene enrichment analysis and MCODE 



Prabhakar & Dicky John, Bioinformation 19(3): 331-335 (2023)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.