1. Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520
RNA-Seq
Xiaole Shirley Liu
STAT115, STAT215, BIO298, BIST520

2. RNA-seq Protocol
Martin and Wang Nat. Rev. Genet. (2011)

3. RNA-seq Applications Expression levels, differential expression
Alternative splicing, novel isoforms
Novel genes or transcripts, lncRNA
Detect gene fusions
Many different protocols
Can use on any sequenced genome
Better dynamic range, cleaner data

4. Experimental Design
Assessing biological variation requires biological replicates (no need for technical replicates)
3 preferred, 2 OK, 1 only for exploratory assays (not good for publications)
For differential expression, don’t pool RNA from multiple biological replicates
Batch effects still exist, try to be consistent or process all samples at the same time

5. Experimental Design Ribo-minus (remove too abundant genes)
PolyA (mRNA, enrich for exons)
Strand specific (anti-sense lncRNA)
Sequencing:
PE (resolve redundancy) or SE: expression
PE for splicing, novel transcripts
Depth: 30-50M differential expression, deeper transcript assembly
Read length: longer for transcript assembly

6. RNA-seq Analysis

7. Alignment Prefer splice-aware aligners TopHat, BWA, STAR (not DNASTAR)
Sometimes need to trim the beginning bases

8. Reference-based assembly
Transcript Assembly
Reference-based assembly
Cufflinks
De novo assembly
Trinity

9. Quality Control: RSeQC

10. Expression Index
RPKM (Reads per kilobase of transcript per million reads of library)
Corrects for coverage, gene length
1 RPKM ~ transcript / cell
Comparable between different genes within the same dataset
TopHat / Cufflinks
FPKM (Fragments), PE libraries, RPKM/2
TPM (transcripts per million)
Normalizes to transcript copies instead of reads
Longer transcripts have more reads
RSEM, HTSeq

11. Differential Expression

12. Sequencing Read Distribution
Poisson distribution:
# events within an interval
Sequencing data is overdispersed Poisson
Negative binomial
Def: # of successes
before r failures occur, if
Pb(each success) is p

13. Differential Expression
Negative binomial
for RNA-seq
Variance estimated by borrowing information from all the genes – hierarchical models
Test whether μi is the same for gene i between samples j
FDR?

14. Differential Expression
Should we do differential expression on RPKM/FPKM or TPM?
Cufflinks: RPKM/FPKM
LIMMA-VOOM and DESeq: TPM
Power to detect DE is proportional to length
Continued development and updates
Gene A (1kb)
Gene B (8kb)

15. Alternative Splicing
Assign reads to splice isoforms

16. Isoform Inference If given known set of isoforms
Estimate x to maximize the likelihood of observing n

17. Known Isoform Abundance Inference

18. Isoform Inference
With known isoform set, sometimes the gene-level expression level inference is great, although isoform abundances have big uncertainty (e.g. known set incomplete)
De novo isoform inference is a non-identifiable problem if RNA-seq reads are short and gene is long with too many exons
Algorithm: MATS

19. Gene Fusion More seen in cancer samples Still a bit hard to call
TopHatFusion in TopHat2
Maher et al, Nat 2009

20. Other Applications RNA editing Circular RNA
Change on RNA sequence after transcription
Most frequent: A to I (behaves like G), C to U
Evolves from mononucleotide deaminases, might be involved in RNA degradation
Circular RNA
Mostly arise from splicing
Varying length, abundance, and stability
Possible function: sponge for RBP or miRNA

21. Summary RNA-seq design considerations Read mapping
TopHat, BWA, STAR
De novo transcriptome assembly: TRINITY
Expression index: FPKM and TPM
Differential expression
Cufflinks: versatile
LIMMA-VOOM and DESeq: better variance estimates
Alternative splicing: MATS
Gene fusion, genome editing, circular RNA

22. Acknowledgement
Alisha Holloway
Simon Andrews
Radhika Khetani