In today's world of genomics research, where vast RNA-seq datasets are generated, there is a need for bioinformatics tools that provide a user-friendly and potent solution. These tools should enable researchers to swiftly and accurately identify differentially expressed genes, contributing to a deeper understanding of biological mechanisms and pathways of transcription regulators. In this direction, the present study introduces DEXA (Differential Gene Expression Analysis), a user-friendly Python tool for robust RNA-seq data analysis. Utilizing sophisticated statistical methods, DEXA identifies quantitative changes in gene expression by comparing normalized read count data between two conditions. The pipeline is developed from scratch, exhibiting autonomy from any pre-existing bioinformatics package or software. This independence enhances its capability to identify differentially expressed genes at the genetic level. DEXA takes normalized gene counts with replications from two different conditions as input and calculates log2 fold change values based on replicated normalized counts. By identifying genes acting as activators (exclusively expressed in treatment) and deactivators (exclusively expressed in control), DEXA offers valuable insights into the dynamics of gene regulation. DEXA contributes to the advancement of RNA-seq analysis by offering a comprehensive instant solution for researchers in genomics and molecular biology.

Activator, deactivators, genomics, differentially expressed gene, normalization, RNA-seq data

The development and implementation of DEXA, a pipeline for differential gene expression analysis, signify a noteworthy advancement in RNA-seq data analysis. DEXA, a user-friendly Python pipeline, effectively leverages statistical methods and cutting-edge RNA-seq analysis techniques, to identify differentially expressed genes (DEGs) at the genetic level. Robust features within the pipeline, such as log2 fold change calculation, t-test execution, and DEGs classification analysis, contribute to a nuanced understanding of changes in gene expression. DEXA's ability to generate a curated list of DEGs with significant expression alterations enhances its utility for researchers exploring the complexities of biological processes or investigating specific biological pathways. By addressing limitations present in existing methods (DESeq2 and EdgeR), DEXA emerges as a valuable tool for the scientific community, offering a versatile and comprehensive approach to unravelling the complexities of differential gene expression. The successful implementation of DEXA represents a crucial step forward in the field of RNA-seq analysis, providing researchers with an efficient and reliable means to gain insights into the molecular landscape underlying various experimental conditions.

Shbana Begam, Samarth Godara, Ramcharan Bhattacharya, Rajender Parsad and Sudeep Marwaha (2023). DEXA: A Python-based Tool for the Advanced Deciphering of Differential Gene Expression Patterns. Biological Forum – An International Journal, 15(11): 499-504.