RNA-seq data analysis: from raw reads to differentially expressed genes

This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of 10-30 minute lectures intertwined with hands-on exercises, and it can be accomplished in a day. As the user-friendly Chipster software is used in the exercises, no prior knowledge of R/Bioconductor or Unix ir required, and the course is thus suitable for everybody. Our book RNA-seq data analysis: A practical approach (CRC Press) can be used as background reading.
The following topics and analysis tools are covered:
1. Introduction to the Chipster analysis platform
2. Quality control of raw reads (FastQC, PRINSEQ)
3. Preprocessing (Trimmomatic, PRINSEQ)
4. Alignment to reference genome (TopHat2)
5. Alignment level quality control (RseQC)
6. Quantitation (HTSeq)
7. Experiment level quality control with PCA and MDS plots
8. Differential expression analysis (DESeq2, edgeR)
-normalization
-dispersion estimation
-statistical testing
-controlling for batch effects, multifactor designs
-filtering
-multiple testing correction
9. Visualization of reads and results
-genome browser
-Venn diagram
-volcano plot
-plotting normalized counts for a gene
-expression profiles
10. Experimental design

Scientific topics: Sequencing, RNA, Data architecture, analysis and design, Bioinformatics

Keywords: Bioinformatics, Differential expression, Ngs, Rna seq

Target audience: Bench biologists, Life Science Researchers

Authors: Eija Korpelainen

Remote created date: 2015-12-04

Remote updated date: 2017-10-09

RNA-seq data analysis: from raw reads to differentially expressed genes https://tess.elixir-europe.org/materials/rna-seq-data-analysis-from-raw-reads-to-differentially-expressed-genes This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of 10-30 minute lectures intertwined with hands-on exercises, and it can be accomplished in a day. As the user-friendly Chipster software is used in the exercises, no prior knowledge of R/Bioconductor or Unix ir required, and the course is thus suitable for everybody. Our book RNA-seq data analysis: A practical approach (CRC Press) can be used as background reading. The following topics and analysis tools are covered: 1. Introduction to the Chipster analysis platform 2. Quality control of raw reads (FastQC, PRINSEQ) 3. Preprocessing (Trimmomatic, PRINSEQ) 4. Alignment to reference genome (TopHat2) 5. Alignment level quality control (RseQC) 6. Quantitation (HTSeq) 7. Experiment level quality control with PCA and MDS plots 8. Differential expression analysis (DESeq2, edgeR) -normalization -dispersion estimation -statistical testing -controlling for batch effects, multifactor designs -filtering -multiple testing correction 9. Visualization of reads and results -genome browser -Venn diagram -volcano plot -plotting normalized counts for a gene -expression profiles 10. Experimental design Sequencing RNA Data architecture, analysis and design Bioinformatics Bioinformatics, Differential expression, Ngs, Rna seq Bench biologists Life Science Researchers 2015-12-04 2017-10-09