Recent Advancement on In-Silico Tools for Whole Transcriptome Analysis

Abstract

Delving into the intricate world of transcriptome analysis, this chapter unfolds the story of gene expression in organisms. The classic DNA microarray and RNA-seq methods have long been the pillars, with RNA-seq taking the spotlight for its superior resolution in understanding dynamic aspects. Yet, tools like Hisat2 and DESeq2, while effective, come with the drawback of being time-consuming and reliant on powerful GPUs. The need for quicker, less resource-intensive techniques has sparked a shift toward simpler R and Python-based tools that not only sidestep GPU dependence but also offer enhanced graphical representations. As we navigate through the content, the chapter draws a vivid comparison between the established tools and the emerging ones, highlighting the pressing need for innovative approaches in transcriptome analysis. The narrative guides readers through the fundamentals, from the Central Dogma’s backstory to the pivotal role of RNA in gene expression and disease. It uncovers the nuances between RNA-Seq and microarray technologies, providing a comprehensive overview of tools for data collection and interpreting changes in gene expression. Our journey extends to the latest breakthroughs, such as the TACITuS platform and the TALON pipeline, tailored for in-depth analysis of transcriptomes using long-read data. The chapter concludes by emphasizing the ever-growing significance of transcriptomics in unraveling complex biological phenomena, with a spotlight on the promising applications of next-generation sequencing. A comprehensive summary ties it all together, detailing the step-by-step protocol of transcriptome analysis, along with insights into current tools, their advantages, and limitations, providing readers with a holistic understanding of their practical application and outcomes.