RNA sequence practical
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1 RNA sequence practical Michael Inouye Baker Heart and Diabetes Institute Univ of Melbourne / Monash Univ Summer Institute in Statistical Genetics 2017 Integrative Genomics Module 1
2 What are we going to do? RNAseq data files Align fastq s to a reference Quantify gene expression Explore the data Perform a differential gene expression analysis This tutorial makes no assumptions about proficiency in programming or R 2
3 What you need Software R (v3.0 or above) Rsubread package Enter the following into R: source(" bioclite( Rsubread") edger package Enter the following into R: source(" bioclite("edger") Data Mouse genome (mm10) 3
4 Load Rsubread and set working directory library(rsubread) setwd( whatever_dir_your_data_is_in ) 4
5 What is Rsubread doing? Alignment of reads to a reference genome The first step in many genomics analyses RNAseq can be used to align to splice junctions (same as many other aligners) Aligned read counts at genomic features then become a quantitative measure of the expression 5
6 Rsubread: Building an index Usage (will take a minute or 2): buildindex(basename="reference_index",reference="chr1.fa") <reference> <basename> Reference genome in FASTA format The basename for output index files Why does an aligner need an index? Alignment is basically a search algorithm Indexing is a way of parsing and storing data to enable accurate and rapid retrieval Align millions of reads very quickly and with low memory Human genome fits into 2.2Gb on disk with ~1.3Gb memory 6
7 Alignment Usage (for each pair of read files): align(index="reference_index", readfile1="sub_chr1_cellstatea_replicate1_1.fq", readfile2="sub_chr1_cellstatea_replicate1_2.fq", output_file="sub_chr1_cellstatea_replicate1_pe.sam") -index -readfile1 -readfile2 -output_file Basename for ref index file File for fastq for one end of paired end sequencing Other end of PE sequencing Output file in SAM format (doesn t automatically append.sam ) Try typing?align This prints out a help text 7
8 Rsubread: STDERR 8
9 Load mouse genome (mm10) annotation Thankfully it s built in here Command mm10 <- getinbuiltannotation("mm10") mm10[1:5,] fc_a1 <- featurecounts("sub_chr1_cellstatea_replicate1_pe.sam",annot.ext=mm10,ispairedend=true) fc_a2 <- featurecounts("sub_chr1_cellstatea_replicate2_pe.sam",annot.ext=mm10,ispairedend=true) fc_b1 <- featurecounts("sub_chr1_cellstateb_replicate1_pe.sam",annot.ext=mm10,ispairedend=true) fc_b2 <- featurecounts("sub_chr1_cellstateb_replicate2_pe.sam",annot.ext=mm10,ispairedend=true) 9
10 Count reads at genomic features Many tools for this Here we use generic featurecounts What do we need? What features are we looking at? Command fc_a1 <- featurecounts("sub_chr1_cellstatea_replicate1_pe.sam",annot.ext=mm10,ispairedend=true) fc_a2 <- featurecounts("sub_chr1_cellstatea_replicate2_pe.sam",annot.ext=mm10,ispairedend=true) fc_b1 <- featurecounts("sub_chr1_cellstateb_replicate1_pe.sam",annot.ext=mm10,ispairedend=true) fc_b2 <- featurecounts("sub_chr1_cellstateb_replicate2_pe.sam",annot.ext=mm10,ispairedend=true) fc_a1$stat fc_a2$stat fc_b1$stat fc_b2$stat 10
11 Tabulate counts, load edger, etc D <- cbind(fc_a1$counts, fc_a2$counts, fc_b1$counts, fc_b2$counts) source(" bioclite("edger") library(edger) g <- c("statea","statea","stateb","stateb") 11
12 Trim lowly expressed exons Command D2 <- DGEList(counts=D[,1:4], group=g) dim(d2) keep <- rowsums(cpm(d2)>1) >= 1 D2 <- D2[keep,] dim(d2) 12
13 TMM normalisation Command d <- calcnormfactors(d2, method= TMM ) Set of highly of expressed genes 13 Robinson & Oshlack, Genome Biology 2010
14 Data exploration Command plotmds(d) Distance b/n a pair of samples Root mean square deviation (Euclidean distance) for the top 500 genes in terms of foldchange 14
15 Estimate common dispersion What is the overall biological coefficient of variation in the data? Command d <- estimatecommondisp(d, verbose=true) Output: Dispersion parameter and Biological Coefficient of Variation How are these interpreted? How else can we estimate dispersion? 15
16 Differential expression Gene by gene, test for differences in means between 2 groups of NB distributed counts Command de_exact <- exacttest(d) What are the top DE genes? Command top_genes <- toptags(de_exact, n=25, adjust.method= BH, sort.by= PValue ) 16
17 View the toptags What are we looking at? 17
18 How many genes are differentially FDR 5% expressed? Command summary(de <- decidetestsdge(de_exact), adjust.method="bh", p.value=0.05) Output (down, NS, up)
19 Smear plot Command detags <- rownames(d)[as.logical(de)] plotsmear(de_exact, de.tags=detags) abline(h=c(-1, 1), col="blue") 19
20 Write output for further processing Command write.table(toptags(de_exact,n= )$table, file=[output]) 20
21 All the key R commands source(" bioclite("rsubread") library(rsubread) setwd("") buildindex(basename="reference_index",reference="chr1. fa") align(index="reference_index", readfile1="sub_chr1_cellstatea_replicate1 _1.fq", readfile2="sub_chr1_cellstatea_replicate1 _2.fq ", output_file="sub_chr1_cellstatea_r eplicate1_p E.SAM") align(index="reference_index", readfile1="sub_chr1_cellstatea_replicate2 _1.fq", readfile2="sub_chr1_cellstatea_replicate2 _2.fq ", output_file="sub_chr1_cellstatea_r eplicate2_p E.SAM") align(index="reference_index", readfile1="sub_chr1_cellstateb_replicate1_1.fq ", readfile2="sub_chr1_cellstateb_replicate1_2.fq ", output_file="sub_chr1_cellstateb_replicate1 _PE.SAM ) align(index="reference_index", readfile1="sub_chr1_cellstateb_replicate2_1.fq ", readfile2="sub_chr1_cellstateb_replicate2_2.fq ", output_file="sub_chr1_cellstateb_replicate2 _PE.SAM") mm10 <- getinbuiltannotation("mm10") fc_a1 <- featurecounts("sub_chr1_c ellstatea_replicate1 _PE.SAM", annot.ext =mm10,ispaired End=TRUE) fc_a2 <- featurecounts("sub_chr1_c ellstatea_replicate2 _PE.SAM", annot.ext =mm10,ispaired End=TRUE) fc_b1 <- featurecounts("sub_chr1_c ellstateb_r eplicate1_p E.SAM",annot.ext =mm10,ispaired End =TRUE) fc_b2 <- featurecounts("sub_chr1_c ellstateb_r eplicate2_p E.SAM",annot.ext =mm10,ispaired End =TRUE) D <- cbind(fc_a1$counts, fc_a2$counts, fc_b1$counts, fc_b2$counts) source(" bioclite("edger") library(edger) g <- c("statea","statea","stateb", "stat eb") D2 <- DGEList(counts=D[,1:4], group=g) keep <- rowsums(cpm(d2)>1) >= 1 D2 <- D2[keep,] d <- calcnormfactors(d2, method="tmm") d <- estimatecommondisp(d, verbose=true) ### Disp = , BCV = de_exact <- exacttest(d) top_genes <- toptags(de_exact, n=25, adjust.method="bh", sort.by="pvalue") summary(de <- decidetestsdge(de_exact), adjust.method="bh", p.value=0.05) detags <- rownames(d)[as.logical(de)] plotsmear(de_exact, de.tags=detags) abline(h=c(-1, 1), col="blue") write.table(toptags(de_exact,n= )$table, file=[output]) 21
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