RNA-Seq Data Analysis
This RNA-Seq workshop aims to get you started with your own RNA-seq analysis and assumes you are already familiar with the basics of bash and R.
Content¶
| Lesson | Overview |
|---|---|
| 1. Background | General overview of a RNA-Seq workflow |
| 2. Quality Assessment | Assess the quality of data, How to use FastQC and MultiQC |
| 3. Trimming and Filtering | How to remove adapter sequences |
| 4. Mapping and Count | Align/Map reads back to genome and number of reads from each sample that originated from that gene. |
| 5. Differential Expression Analysis | Differential expression analysis with R |
| 6. Over-representation analysis | Determine whether known biological functions or processes are over-represented (= enriched) in an experimentally-derived gene list |
| Supplementary 1 - A Guide to RNA-Seq | |
| Supplementary 2 - Lecture notes : DE analysis | |
| Supplementary 3 - Lecture notes : Annotation Data & Gene Set Analysis |
Attribution notice
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The material used to prepare for the workshop was extracted from Professor.Mik Black's lectures for STAT435 - Data Analysis for Bioinformatics (University of Otago, Dunedin, New Zealand).
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Some code used in this workshop has been extracted from a collection of presentations, workshops and lectures by Prof. Thomas Girke, Institute for Integrative Genome Biology, UC Riverside: Link_1 and Link_2