Week 6- Introduction to Gene Ontology and Overrepresentation Analysis

Arnab M -

Hi guys, It’s Arnab and this is my sixth weekly update on my senior research project: Exploring the Genomic Effects of PNPLA7 Mutations on Cerebral Palsy through RNA Sequencing.

This week at the lab I was introduced to this new term: Gene Ontology. Gene Ontology (GO) is a powerful resource utilized in bioinformatics and computational biology to systematically annotate genes and their products across species where it provides a structured vocabulary to describe the molecular functions, biological processes, and cellular components of genes. By categorizing genes based on their roles in biological systems, GO facilitates the interpretation of high-throughput genomic data, and it also aids researchers in understanding the underlying biological mechanisms.

One common analysis technique used with GO annotations is Overrepresentation Analysis (ORA) the other topic I was introduced to. ORA assesses whether particular functional categories are overrepresented within a set of genes compared to what would be expected by chance. This statistical approach helps researchers identify biological pathways or functions that are significantly enriched in a list of differentially expressed genes or genes associated with a particular phenotype.

The process of ORA typically involves three main steps: selecting a gene set of interest (e.g., differentially expressed genes), mapping these genes to GO terms, and assessing the enrichment of specific GO terms within the gene set. Statistical tests, such as Fisher’s exact test or hypergeometric test, are commonly employed to determine the significance of enrichment.

Understanding the biological significance of gene expression changes or genomic variations often requires more than just identifying individual genes. GO and ORA offer a systematic framework for interpreting complex genomic data, providing insights into the underlying biological processes driving observed changes. By leveraging these tools, the researchers at Kruer’s Lab and myself can gain a deeper understanding of the functional implications of our data that should be processed next, ultimately advancing our understanding of molecular biology and disease mechanisms.

I can’t wait to put these tools and concepts to work next week!

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