1. RNA-seq library prep introduction
NESCent Academy

2. Outline Methodologies and history RNA-seq challenges
Library preparation methods
Common queries
Validation
Spike-in and future-proofing your work

3. Gene expression

4. RNA sequencing Isolate RNAs
Generate cDNA, fragment, size select, add linkers
Samples of interest
Condition 1
(normal colon)
Condition 2
(colon tumor)
Sequence ends
Map to genome, transcriptome, and predicted exon junctions
100s of millions of paired reads
10s of billions bases of sequence
Downstream analysis

5. Metholologies for RNA-Seq studies
Mapping transcription start sites
Strand-specific RNA-Seq
Characterization of alternative splicing patterns
Gene fusion detection
Targeted approaches using RNA-Seq
Small RNA profiling
Direct RNA sequencing
Profiling low-quantity RNA samples

6. Pre NGS Transcriptomics
Hybridization-based approaches
Genomic tiling microarrays
Fluorescently labelled cDNA with microarrays
Sequence-based approaches
Sanger sequencing of cDNA or EST libraries
Serial analysis of gene expression (SAGE)
Cap analysis of gene expression (CAGE)
Massively parallel signature sequencing (MPSS)

7. RNA-seq

8. Challenges
RNAs consist of small exons that may be separated by large introns
Mapping reads to genome is challenging
The relative abundance of RNAs vary wildly
105 – 107 orders of magnitude
Since RNA sequencing works by random sampling, a small fraction of highly expressed genes may consume the majority of reads
Ribosomal and mitochondrial genes
RNAs come in a wide range of sizes
Small RNAs must be captured separately
PolyA selection of large RNAs may result in 3’ end bias
RNA is fragile compared to DNA (easily degraded)
Bacterial samples may need to be depleted of rRNA

9. Rubbish in = Rubbish out

10. RNA-seq library prep methodologies
Two main routes for mRNA-seq preparation
Illumina TruSeq prep
Script-seq
Generally Script-seq is our favourite

11. RNA Illumina Tru-Seq library prep
2 days for 8 samples
Size selection step
Adaptor ligation and standard library preparation
5ug of total RNA
~$100 per sample
Not strand-specific

12. Script-seq method 2 hours for 12 samples < 1ug of RNA
~$150 per sample
Strand-specific

13. DNA library preparation: RNA fragmentation and DNA fragmentation compared
a | Fragmentation of oligo-dT primed cDNA (blue line) is more biased towards the 3' end of the transcript. RNA fragmentation (red line) provides more even coverage along the gene body, but is relatively depleted for both the 5' and 3' ends. Note that the ratio between the maximum and minimum expression level (or the dynamic range) for microarrays is 44, for RNA-Seq it is 9,560. The tag count is the average sequencing coverage for 5,000 yeast ORFs. b | A specific yeast gene, SES1 (seryl-tRNA synthetase), is shown.

14. Common questions: How much library depth is needed for RNA-seq?
My advice. Don’t ask this question if you want a simple answer…
Depends on a number of factors:
Question being asked of the data. Gene expression? Alternative expression? Mutation calling?
Tissue type, RNA preparation, quality of input RNA, library construction method, etc.
Sequencing type: read length, paired vs. unpaired, etc.
Computational approach and resources
Identify publications with similar goals
Pilot experiment
Good news: 1/8th -1 lane of recent Illumina HiSeq data should be enough for most purposes

15. Coverage versus depth

16. Common questions: What mapping strategy should I use for RNA-seq?
Depends on read length
< 50 bp reads
Use aligner like BWA and a genome + junction database
Junction database needs to be tailored to read length
Or you can use a standard junction database for all read lengths and an aligner that allows substring alignments for the junctions only (e.g. BLAST … slow).
Assembly strategy may also work (e.g. Trans-ABySS)
> 50 bp reads
Spliced aligner such as TopHat or Trinity

17. Common questions: how reliable are expression predictions from RNA-seq?
Are novel exon-exon junctions real?
What proportion validate by RT-PCR and Sanger sequencing?
Are differential/alternative expression changes observed between tissues accurate?
How well do differential expression values correlate with qPCR?
384 validations
qPCR, RT-PCR, Sanger sequencing
See ALEXA-Seq publication for details:
Also includes comparison to microarrays
Griffith et al. Alternative expression analysis by RNA sequencing. Nature Methods Oct;7(10):

18. Common questions: How many replicates?
As many as you can afford
Tophat/Cufflinks statistics work best with three or more biological replicates

19. Validation (qualitative)
33 of 192 assays shown. Overall validation rate = 85%

20. RNA-seq vs Microarray

21. Spike-in controls
How can you identify limits of detection and ensure your data can be compared to future platforms or new library prep methods? (e.g. How does Oxford Nanopore compare to Illumina sequencing?)
Spike-in RNA to your total RNA which has a known concentration
Cost - $20 per sample

22. RNA-seq spike-in protocol

23. Assessing lower limit of detection

24. Assessing fold change response

25. Take home Good quality total RNA of 1-10ug
Have 3 or more biological replicates
Unless you have good reason, use a Script-seq type protocol
Use a standard spike-in as an internal control and to ensure samples can be compared across platforms
Don’t forget to validate key findings with qPCR!