Peter Tsai Bioinformatics Institute, University of Auckland RNA Sequencing Peter Tsai Bioinformatics Institute, University of Auckland

What is RNA-seq? Study of transcriptomes Identify known genes, exons, splicing events, ncRNA, miRNA Novel genes or transcripts Abundances of transcripts (quantitive expression) Differential expressed transcripts between different conditions Reconstructing transcriptome.

General workflow Raw data QC De novo transcriptome assembly Map to reference genome De novo transcriptome assembly Require downstream annotation Estimate abundance Normalisation Differential expression analysis

Quality checks and mapping Use FastQC, SolexQA Trim off low quality region, keep only proper-paired reads Most QC software assume normality, but in RNA-seq data you will probably see none-normality You might see some duplicated reads, its probably due to highly expressed gene. Specific reference mapping tool that can map across splice junctions between exons, i.e. Tophat Specific de novo transcriptome assembly software for reconstruction of transcriptomes from RNA-seq data, i.e. Trinity

Expression value in RNA-seq The total number of reads mapped to a gene/transcript (Count data or raw counts or digital gene expression) Complexity of using simple counts Sequencing depth: the higher the sequencing depth, the higher the counts Gene length: Counts are proportional to the length of the gene times mRNA expression level Counts distribution: difference on how counts are distributed among samples.

Normalisation RPKM (Mortazavi et al, 2008) Reads Per Kilobase of exon model per Million mapped reads FPKM (Mortazavi et al, 2010) Fragments Per Kilobase of exon model per Million mapped reads Paired-end RNA-Seq experiments produce two reads per fragment, but that doesn't necessarily mean that both reads will be mappable.

Data exploration Replicate 2 Replicate 1

 Gene.ID/Description logFC logCPM LR PValue FDR 1 2.563086301 5.07961611 28.4599795 9.57E-08 2.72E-05 2 4.003686266 2.330395704 28.3288251 1.02E-07 3 2.71372512 9.704651395 25.01930526 5.68E-07 0.000100653 4 -2.052703196 3.402621025 21.11492168 4.33E-06 0.000575287 5 1.95117636 4.438847349 19.21195535 1.17E-05 0.001244651 6 2.465833373 12.20593577 10.91756889 0.000952565 0.084460792 7 1.817858683 5.308092036 10.3738524 0.001278126 0.097137553 8 1.577603322 6.556675456 9.690419768 0.001852312 0.110687766 9 1.20515812 4.542565518 9.670466698 0.001872537 10 1.233090336 10.08249873 9.289827985 0.002304298 0.122588652 11 1.120581944 12.14988136 7.710102379 0.005491264 0.265577482 12 1.045292369 4.913492018 7.039209923 0.00797442 0.350270537 13 1.089867189 3.885246135 6.912558621 0.008559242 14 1.353955354 2.21406615 5.976193603 0.014500264 0.551010036 15 1.049933686 3.281031472 5.737563572 0.016605812 0.588952795 16 -1.032999983 1.480514873 4.712476717 0.029944481 0.995653998 17 -1.313778857 4.325330722 4.169234925 0.041164384 0.998742102 18 0.864451602 4.338668381 3.479808135 0.062121942 19 -0.766266641 5.2972332 3.443865378 0.063486998

Up-regulated Down-regulated

ERCC spike-in control Set of external RNA transcripts with known concentration. Dynamic range and lower limit of detection Fold-change response Internal control, in order to measure against defined performance criteria

Dynamic range and lower limit of detection The dynamic range can be measured as the difference between the highest and lowest concentration. Measure of sensitivity, and it is defined as the lowest molar amount of ERCC transcript detected in each sample The dynamic range can be measured as the difference between the highest and lowest concentration of ERCC transcript detected in each sample. The LLD is a measure of sensitivity, and it is defined as the lowest molar amount of ERCC transcript detected in each sample, with user-defined threshold values for determining detection. This translates to ~323,000 control molecules detected per 100 ng poly(A) RNA.

Fold-change response

How much library depth is needed for RNA-seq? Depends on a number of factors Biological questions Complexity of the organism Types of analysis Types of RNA, miRNA, lncRNA. Literature search for similar work Pilot experiment

Summary Have 3 or more biological replicates Analysis your data with different normalisation methods Perform data exploration Use a standard spike-in as internal control Validation with qPCR