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

2. 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.

4. 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

5. 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

6. 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.

7. 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.

8. Data exploration
Replicate 2
Replicate 1

9.  Gene.ID/Description
logFC
logCPM LR
PValue
FDR 1
9.57E-08
2.72E-05 2
1.02E-07 3
5.68E-07 4
4.33E-06 5
1.17E-05 6 7 8 9 10 11 12 13 14 15 16 17 18 19

10. Up-regulated
Down-regulated

11. 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

12. 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.

13. Fold-change response

14. 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

15. 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