Integrated Bioinformatics Tools For The Evaluation Of Psychiatric Disorders

Abstract

Background: However, it can be challenging to assess the complicated interactions between genetics and disease. With a high global prevalence rate, high morbidity, high resource use, and high disability rates, mental disorders are significant diseases. Major depression, bipolar disorder, and schizophrenia are widespread, complicated disorders with high heritabilities that fall under the category of polygenic genetic diseases. High-throughput sequencing, recombinant DNA technologies, and multidimensional nuclear magnetic resonance have all contributed to the rapid advancement of molecular biology. The sequencing of many biological genomes, including the human genome, has produced a wealth of nucleic acid knowledge. These genes can be categorised or grouped depending on different phenotypes, such as disease types or cell types, and their relationships can then be determined through experimental research.
Methodology: The ncbi geo database was used to gather published microarray data gene expression patterns of mental diseases such depression, bipolar disorder, and schizophrenia. The gene expression patterns were analysed and compared using integrated bioinformatics methods. R was used to process the data, Gene ontology (GO) and the KEGG database were used to analyse the function and pathway enrichment of differentially expressed genes (DEGs), and string database was used to study protein-protein interactions.
Results: 12,626 differentially expressed genes (DEGs) in all were investigated. However, there was no statistically significant difference in any of the genes between the patients and controls. Further research was done on the genes that were enriched by greater than 1.5 times. The genes with differential expression were compared using a volcano, mean difference plot, box plot, histogram, and Venn's diagram. In comparison to controls, none of the genes were significantly enriched, downregulated, or elevated in any of the three mental diseases.
Conclusion: The study demonstrates the efficacy of bioinformatics analytic approaches in identifying probable pathogenic genes for mental diseases like schizophrenia, bipolar disorder, and depression as well as their underlying mechanisms.