Optimizing of pre-processing analysis for Illumina RNA-Seq data in Arabidopsis thaliana

Abstract

Gene expression analysis through RNA sequencing (RNA-Seq) has revolutionized molecular biology, providing profound insights into the intricate transcriptional landscapes of organisms. Arabidopsis thaliana, a widely studied model plant, serves as a cornerstone for investigating fundamental biological and ecology processes. However, accurate interpretation of RNASeq data hinges on meticulous pre-processing methods to ensure data integrity and trustworthiness, especially in the context of Illumina sequencing. In this research, we present a comprehensive framework for optimizing pre-processing analysis tailored specifically for Arabidopsis thaliana RNA-Seq datasets generated through Illumina sequencing. Our approach encompasses rigorous quality control, precise read alignment, transcript quantification, and normalization procedures crucial for subsequent differential expression analysis. Additionally, we address unique considerations and challenges inherent to Arabidopsis thaliana datasets, providing valuable insights for researchers in the field.