Bioinformatic Analysis of Differentially Expressed Genes (DEGs) Detected from RNA-Sequence Profiles of Mouse Striatum

Bandhan Sarker (), Md. Matiur Rahaman (), Suman Khan (), Priyanka Bosu () and Md. Nurul Haque Mollah ()
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Bandhan Sarker: Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Department of Statistics, Faculty of Science
Md. Matiur Rahaman: Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Department of Statistics, Faculty of Science
Suman Khan: Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Department of Statistics, Faculty of Science
Priyanka Bosu: Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Department of Statistics, Faculty of Science
Md. Nurul Haque Mollah: University of Rajshahi, Bioinformatics Laboratory, Department of Statistics

A chapter in Data Science and SDGs, 2021, pp 101-122 from Springer

Abstract: Abstract Bioinformatic analysis is a powerful statistical analysis to investigate the significant genes and their biological information from RNA-sequence (RNA-Seq)-based gene expression profiles. The most differentially expressed genes (DEGs) of mouse striatum with their valuable information may be significantly contributed to the neuroscience research. Two inbred mouse strains, for instance, C57BL/6J (B6) and DBA/2J (D2), in neuroscience research are commonly used, and B6 strain sequences are mostly available. Our study’s focus on the identification of significant DEGs of B6 and D2 samples, protein–protein interaction network, to identify their biological functions, molecular pathway analysis, miRNAs-target gene interactions, downstream analysis, and to find out driven genes. Two samples, 10 B6 and 11 D2, were deeply analyzed, which were retrieved from the Gene Expression Omnibus (GEO) database with accession number GSE26024. DESeq2, edgeR, and limma tools were utilized to screen the DEGs somewhere in the range of B6 and D2 samples. DESeq2, edgeR, and limma had identified a total of 736, 757, and 530 DEGs with 37, 48, and 31 up-regulated genes, respectively. Protein–protein interaction network analyses of those DEGs were visualized using a search tool for the Retrieval of Interacting Genes and Cytoscape software. We selected the top 50 high-degree hub DEGs for each of the three methods, and explored 21 common hub genes along with three up-regulated genes Bdkrb2, Aplnr, and Ccl28. To explore the biological insights of these 21 common hub DEGs, Gene Ontology (GO) and KEGG pathway analysis were executed. In downstream analysis, hierarchical and k-means clustering techniques were used, and both the methods clustered Bdkrb2, Aplnr, and Ccl28 genes into the same group. Furthermore, DEGs, specifically the genes Bdkrb2, Aplnr, and Ccl28, are probably the core genes in inbred mouse strains. In conclusion, these genes probably are the biomarkers for further neuroscience research.

Keywords: Mouse RNA-Seq profiles; Differential gene expressions; Functional analysis; Molecular pathway analysis; Bioinformatic approaches (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_9

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DOI: 10.1007/978-981-16-1919-9_9

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