Session 8. Differential gene expression analysis using RNAseq data
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1 Functional and Comparative Genomics 2018 Session 8. Differential gene expression analysis using RNAseq data Tutors: Hrant Hovhannisyan, PhD student, Uciel Chorostecki, PhD Centre of Genomic Regulation Group of Comparative Genomics 15/06/2018
2 Outline of the class Theoretical part (~ 30 min) Practical part (~ 1h 30 min) Prerequisites 1. Start downloading materials ( tar xvzf name_of_the_arciheve.tar.gz ) 2. Download the Documentation 3. Download this presentation
3 Gene expression What does gene expression mean? Gene expression is a measure of how actively a given gene is transcribed How can we measure gene expression? We can count/quantify the number of RNA molecules (transcripts) of a given gene at a given time in a given condition. What is a transcriptome? It is the set of all RNA molecules in one cell or a population of cells. How can we measure a transcriptome? Today, people usually use RNA sequencing (or RNAseq) and/or it s numerous derivatives.
4 Features of a transcriptome Unlike a genome, transcriptome is highly dynamic We can talk about transcriptome in context of a given condition, given time, given cell or a tissue. Why do we study a transcriptome? To describe it (obviously) to see how different genes are expressed compared to each other, find novel genes and transcripts, genotype-phenotype links To compare it to see how do genes change their expression levels due to different conditions (find differential expression transcriptome-wide)
5 Example of comparative transcriptomics study vs Optimal temperature 37 Low temperature 12 The genes are differentially expressed due to cold temperature.
6 Another appealing example Not only describes transcriptome in different tissues, but also studies how genetic variants influence gene expression levels (eqtl analysis) A Battle et al. Nature 550, (2017) doi: /nature24277
7 How do we study a transcriptome? Today, the most effective approach is by performing RNAseq. The other (older) approaches are discussed in the recent review Transcriptomic technologies What is RNAseq and how it works? RNAseq is a Next-Generation Sequencing method to study the transcriptome qualitatively and quantitively. It is very sensitive, reproducible, allows to analyze transcripts of any length and discover novel transcripts. Down side is that it s expensive and requires deep bioinformatics analysis. What is Next-generation sequencing? A very descriptive video
8 Some definitions Data generated by RNAseq (any NGS technology) are called reads short stretches of RNA/DNA that were actually sequenced Length of the reads can be different depending on the sequencing configuration used (from 20 to 300 base pairs case of Illumina machines) Reads can be single ended or paired ended (depending on sequencing configuration) Reads can be strand specific or non-strand specific (depending on experimental protocol)
9 Some methodological concepts of RNAseq in context of differential gene expression (DGE) analysis Experimental design overall what kind of study one needs to perform (how many conditions, time-series, how many samples, others) DGE analysis is a comparative analysis, so one needs to have a reference point to compare (control samples) To do DGE analysis replications (biological) of the samples is required (at least three)
10 Brief recap of RNAseq 1. Planning and performing experiments (i.e. yeast in different conditions) 2. Extracting RNA (and other laboratory procedures) 3. Sequencing 4. Data analysis (this part is complicated, we will do it today) RNAseq (NGS in general) generates massive amount of data. Data analysis of almost any NGS technology is done by several software, which are collectively called a Pipeline. NGS data is usually analyzed using command line software in Unix/Linux operating system. Some tasks are computationally very demanding and require a lot of hard drive space.
11 Our practical lesson We will find and characterize differentially expressed genes between two conditions (planktonic and biofilm) of pathogenic fungus Candida parapsilosis using RNAseq. C. parapsilosis Planktonic state 3 samples Control C. parapsilosis Biofilm state 3 samples Control Holland et al. ( h.gov/pubmed/ ) Extract RNA and perform RNAseq for all samples Compare gene expression levels between two conditions
12 RNAseq differential gene expression analysis pipeline Raw reads Quality Control with FastQC Trimmomatic Trimming Mapping with STAR Functional analysis DE genes Visualization DE analysis with Deseq2 Read count matrix Counting with STAR 12
13 Following slides are additional technical slides and are explained in the documentation to the course
14 Fastq file format
15 Trimming with Trimmomatic For paired-end data it generates 4 files
16 Read mapping In general read mapping consists of two steps: a. indexing of reference genome b. and mapping itself
17 GFF/GTF file
18 SAM/BAM formats
19 Visualize alignments using IGV (not covered in the Documentation)
20 Visualize alignments using IGV (not covered in the Documentation)
21 Normalization by gene length and sequencing depth Don not use R(F)PKM for differential expression analysis, they are good of within-sample comparisons
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