This project focuses on genomic data analysis to determine differential gene expressions and identify upregulated and downregulated genes associated with disease progression. By leveraging modern genomics technologies, this study evaluates gene expression patterns, providing insights into molecular mechanisms underlying disease conditions.
Using open-source tools like R and Bioconductor, we analyze genetic data to explore gene regulation. This approach helps in understanding mRNA and miRNA interactions, where:
- Partial complementarity between mRNA and miRNA inhibits translation.
- Complete complementarity leads to transcript degradation.
In diseases like cancer, miRNA activity alterations disrupt normal cell functions, contributing to abnormal cell division, apoptosis, and angiogenesis. This analysis allows researchers to track tumor growth and gene regulation patterns.
- Identify gene expression patterns involved in cell proliferation.
- Determine miRNAs regulating gene expression.
- Analyze genes responsible for cancer development.
- Programming Language: R
- Frameworks & Libraries:
- Bioconductor (genomic data processing)
- limma (differential gene expression analysis)
- ggplot2 (data visualization)
- edgeR (RNA-seq analysis)
This study enhances our understanding of gene regulatory mechanisms in cancer and other diseases, providing valuable insights into potential therapeutic targets for precision medicine.