Prediction and Identification of Signature Genes Expressed in Different Brain Regions through RNA-Seq Data Analysis
Ruchi Yadav, Akanksha Sharma and Jyoti Prakash

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow -226028, U.P., India.

Corresponding Author E-mail: jprakash@lko.amity.edu

Abstract: Brain is one of the most intricate organs in the human body that works with billions of cells. A brain tumor emerges when there is uncontrolled division of cells shaping a strange gathering of cells around or inside the cerebrum. To understand the complexity of brain function and gene expression in different regions of brain is most challenging and crucial. RNAseq techniques along with computational analysis has made this task much easier and accurate. In this current research RNAseq data of brain regions have been used to predict functional genes in different regions of brains. Differential expression of genes in different regions of brain highlights the function of genes and associated pathways in brain function. The aim of this study was to identify differentially expressed gene in the brain tumor samples. RNA-seq data was retrieved from ENA database with the accession no.- PRJNA294929. Total 5 samples were retrieved in fastq format, out of which 4 samples were of corpous callosum and 1 sample was of frontal cortex. Differentially Expressed Genes (DEG) analysis was done using Galaxy platform and R software, functional enrichment of DEGs was done using DAVID and GO databases. The RNA-seq data analysis shows the up regulation of PHGDH, TUBB4A, HSPA2, GFAP, NKX3-1, COX3 genes and three genes PHGDH, COX3 and MT3 shows significant difference in their gene expression. This result can have importance in understanding the complexity of brain transcriptomics and DEGs can be further studied to evaluate their expression in brain cells and associated diseases. Further wet lab verification is required as these genes can be used as potential drug target and can be used for drug designing for brain tumor.

Keywords: Brain cancer; DEG analysis; Galaxy Server; RNAseq; R and Bioconductor

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