Learn the complete workflow of RNA-Seq — from essential biological principles and next-generation sequencing (NGS) concepts to practical data analysis. This no-coding course is designed for medical and biomedical learners who want to confidently perform RNA-Seq analysis.
This course introduces you to the world of NGS and transcriptomics in a clear, step-by-step manner. The focus is on building a broad conceptual understanding that empowers you to perform RNA-Seq data analysis independently — without getting lost in complex statistics, technology, or coding.
The course begins with the fundamental principles of gene expression and NGS, then guides you through the complete RNA-Seq analysis pipeline using a user-friendly analysis environment that requires no coding skills.
Through video lectures, guided readings and worksheets, detailed instructions, quizzes, and live interactive lectures and practical sessions, you will learn to perform key steps in RNA-Seq analysis — including quality control, read alignment, gene quantification, differential expression, and enrichment analysis. A test dataset will be used throughout the course, allowing you to practice each step of the workflow in a structured, hands-on manner. In the final phase, you will analyze a real-world dataset for a capstone project, presenting your findings in a format that simulates a research presentation — building your confidence and competence to independently perform RNA-Seq analyses.
Undergraduate students, graduate students, postdocs, and professionals in medicine, biology, or related fields who have no prior coding or bioinformatics experience. It is particularly helpful for those who want to conduct research using RNA-Seq or prepare themselves for higher studies in competitive research programs.
This course will be taught by Dr. Md Anwarul Karim (Mijan), currently a Postdoctoral Researcher at Baylor College of Medicine, USA. Dr. Karim is a medical graduate from Chittagong Medical College and holds a PhD in Genetics from the University of Hong Kong.