UNVEILING GENE SIMILARITIES THROUGH RNA-SEQ ANALYSIS
Poster Number
4A
Research or Creativity Area
Engineering & Computer Science
Abstract
In the evolving field of genomics, RNA sequencing analysis predominantly focuses on identifying differential gene expression between distinct biological samples. However, this conventional approach often overlooks the significance of genes that exhibit equivalent expression levels across varying conditions, which could be equally vital for understanding the intricate dynamics of gene regulation. This study introduces a novel paradigm by aiming to identify statistically significant equivalence in gene expression through RNA-sequencing data analysis. Employing a computing cluster with Linux commands for navigation, Python for script development, and a SLURM Job Scheduler for task management, a robust analytical framework has been established. This framework incorporates the use of Jupyter Notebooks for statistical analysis, argparse for parameter input handling, and the integration of NumPy and Pandas libraries for data manipulation. Through detailed hypothesis testing, the study seeks to discern patterns of equivalent gene expression by altering analysis parameters and examining their impact on p-values and computational efficiency. The study is incorporating single-cell RNA-sequencing data from the Human Lung Cell Atlas (https://www.nature.com/articles/s41591-023-02327-2) to deepen insights into gene expression dynamics. Additionally, the research plans to test the effect of parameter changes on the results, assess time complexity, and explore additional methods to enhance computational efficiency. The study leverages GitHub for version control, Anaconda for environment management, and rclone for data transfer efficiency which also enables the handling of extensive genomic datasets. In doing so, this RNA-seq analytical method offers to enhance our understanding of gene functions, highlighting both differential and equivalent expression patterns and thus fostering a more detailed perspective on genetic regulatory mechanisms.
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
UNVEILING GENE SIMILARITIES THROUGH RNA-SEQ ANALYSIS
Don and Karen DeRosa University Center (DUC) Poster Hall
In the evolving field of genomics, RNA sequencing analysis predominantly focuses on identifying differential gene expression between distinct biological samples. However, this conventional approach often overlooks the significance of genes that exhibit equivalent expression levels across varying conditions, which could be equally vital for understanding the intricate dynamics of gene regulation. This study introduces a novel paradigm by aiming to identify statistically significant equivalence in gene expression through RNA-sequencing data analysis. Employing a computing cluster with Linux commands for navigation, Python for script development, and a SLURM Job Scheduler for task management, a robust analytical framework has been established. This framework incorporates the use of Jupyter Notebooks for statistical analysis, argparse for parameter input handling, and the integration of NumPy and Pandas libraries for data manipulation. Through detailed hypothesis testing, the study seeks to discern patterns of equivalent gene expression by altering analysis parameters and examining their impact on p-values and computational efficiency. The study is incorporating single-cell RNA-sequencing data from the Human Lung Cell Atlas (https://www.nature.com/articles/s41591-023-02327-2) to deepen insights into gene expression dynamics. Additionally, the research plans to test the effect of parameter changes on the results, assess time complexity, and explore additional methods to enhance computational efficiency. The study leverages GitHub for version control, Anaconda for environment management, and rclone for data transfer efficiency which also enables the handling of extensive genomic datasets. In doing so, this RNA-seq analytical method offers to enhance our understanding of gene functions, highlighting both differential and equivalent expression patterns and thus fostering a more detailed perspective on genetic regulatory mechanisms.
