How to Analyze RNA-Sequencing Data: A Bioinformatics Overview

Learning Objectives:

• Understand the stages of a standard RNA-seq bioinformatics workflow, including the role of QC, alignment, gene expression quantification, and specialized application analyses
• Identify key QC metrics in RNA-seq analysis and understand what those metrics represent in terms of data characteristics
• Recognize when fusion detection and splicing analyses are applicable and understand the structure of their output files

RNA sequencing (RNA-seq) is a Next-Generation Sequencing (NGS) application for transcriptome profiling. This essential tool enables researchers to derive biological insights by investigating gene expression patterns, identifying novel transcripts, detecting alternative splicing events, and discovering gene fusions. However, analysis of RNA-seq data involves complex computational processes with comprehensive quality-control (QC) requirements; these can be challenging for researchers with limited experience in bioinformatics. 

This educational webinar provides a comprehensive overview of RNA-seq data analysis. The session covers the standard RNA-seq bioinformatics workflow we used to evaluate libraries prepared using the KAPA RNA EvoPrep Kit, showing how raw sequencing data progresses through quality assessment, read alignment, gene expression quantification, and specialized application analyses. Attendees will learn the technical definitions of key QC metrics and what they represent in the context of RNA-seq data characteristics. The webinar also introduces specialized application analyses including fusion detection and splicing analysis. For each application, attendees will learn relevant use cases and how to understand the structure and content of key results files.