RNAseq Data analysis using Shell scripting and R

Unlock the power of RNAseq data analysis with this comprehensive course! Learn to navigate the Linux command line and master the tools for extracting meaningful insights from your gene expression data.

This course provides a detailed introduction to the entire RNAseq workflow, from raw data to functional interpretation. You'll gain hands-on experience with essential techniques, including:

• Data Preprocessing: Learn how to prepare raw sequencing data for analysis.
• Quality Control (QC): Implement robust QC methods to ensure data integrity.
• Read Alignment: Align sequencing reads to a reference genome or transcriptome.
• Differential Expression Analysis: Discover genes with significant changes in expression levels using statistical methods.
• Functional Annotation: Understand the biological significance of differentially expressed genes.
• Data Visualization: Effectively visualize your results using popular tools like R.
• Clustering & PCA: Apply clustering and Principal Component Analysis to explore gene expression patterns.

You'll also learn how to:

• Download RNAseq data from public repositories.
• Install bioinformatics software using Conda/Anaconda on Mac, Windows, or Linux.
• Perform differential expression analysis using the user-friendly RStudio interface.

Throughout the course, you'll work with real-world datasets, building practical skills with industry-standard bioinformatics tools and software packages.

Who is this course for?

This course is perfect for researchers, scientists, and students interested in understanding the molecular basis of gene expression and exploring the applications of RNAseq technology. No prior bioinformatics or programming experience is required, but a basic understanding of molecular biology and genetics is recommended.

By the end, you'll have a solid foundation in RNAseq data analysis and the ability to independently analyze gene expression data.