Identification of Differential Gene Expression in Glioma Patients
Poster Number
12C
Research or Creativity Area
Engineering & Computer Science
Abstract
During the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas - both tumoral and peritumoral (area surrounding the tumor) - using a computational cluster. First, the data was downloaded onto the cluster from the Gene Expression Omnibus, then aligned using a batch script. Following this, a table of gene counts for each file was created using another batch script, then downloaded onto a GitHub repository for analysis using RStudio’s DeSeq2 package. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for specific targeting.
Location
Don and Karen DeRosa University Center (DUC) Poster Hall
Start Date
27-4-2024 10:30 AM
End Date
27-4-2024 12:30 PM
Identification of Differential Gene Expression in Glioma Patients
Don and Karen DeRosa University Center (DUC) Poster Hall
During the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas - both tumoral and peritumoral (area surrounding the tumor) - using a computational cluster. First, the data was downloaded onto the cluster from the Gene Expression Omnibus, then aligned using a batch script. Following this, a table of gene counts for each file was created using another batch script, then downloaded onto a GitHub repository for analysis using RStudio’s DeSeq2 package. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for specific targeting.
