1. mRNA-Seq: methods and applications
Jim Noonan
GENE 760

2. Introduction to mRNA-seq
Technical methodology
Read mapping and normalization
Estimating isoform-level gene expression
De novo transcript reconstruction
Sensitivity and sequencing depth
Differential expression analysis

3. mRNA-seq workflow Wang et al. Nat Rev Genet 10:57 (2009)
Martin and Wang Nat Rev Genet 12:671 (2011)

4. Illumina RNA-seq library preparation
Capture poly-A RNA with poly-T oligo attached beads (100 ng total) (2x)
RNA quality must be high – degradation produces 3’ bias
Non-poly-A RNAs are not recovered
Fragment mRNA
Synthesize ds cDNA
Ligate adapters
Amplify
Generate clusters and
sequence

5. Ribosomal RNA subtraction
RiboMinus

6. Mapping RNA-seq reads and quantifying transcripts

7. RNA-seq reads mapped to a reference genome
Normalization :
Reads per kilobase of feature length per million mapped reads (RPKM)
Quantify expression of known genes (counting)
Gene model level  composite of the whole gene vs constitutive
Differences that we are seeing could be due to splicing  methods for isoform level expression values
Transcriptome reconstruction  combination of Tophat,paired end tags
What is a “feature?”
What about genomes with poor genome annotation?
What about species with no sequenced genome?
For a detailed comparison of normalization methods, see:
Bullard et al. BMC Bioinformatics 11:94 (2010).
Robinson and Oshlack, Genome Biol 11:R25 (2010)

8. Quantifying gene expression by RNA-seq
Use existing gene annotation:
Align to genome plus annotated splices
Depends on high-quality gene annotation
Which annotation to use: RefSeq, GENCODE, UCSC?
Isoform quantification?
Identifying novel transcripts?
Reference-guided alignments:
Align to genome sequence
Infer splice events from reads
Allows transcriptome analyses of genomes with poor gene annotation
De novo transcript assembly:
Assemble transcripts directly from reads
Allows transcriptome analyses of species without reference genomes

9. Composite gene model approach
Map reads to genome
Map remaining reads to
known splice junctions
Requires good gene models
Isoforms are ignored

10. Which gene annotation to use?

11. Strategies for transcript assembly
Garber et al. Nat Methods 8:469 (2011)

12. Splice-aware short read aligners
Martin and Wang Nat Rev Genet 12:671 (2011)

13. Reference based transcript assembly
Martin and Wang Nat Rev Genet 12:671 (2011)

14. Transcript assembly programs
Martin and Wang Nat Rev Genet 12:671 (2011)

15. Cufflinks: ab initio transcript assembly
Step 1: map reads to reference genome
Trapnell et al. Nat. Biotechnology 28:511 (2010)

16. Cufflinks: ab initio transcript assembly
Isoform abundances estimated
by maximum likelihood
Trapnell et al. Nat. Biotechnology 28:511 (2010)

17. Graph-based transcript assembly
Martin and Wang Nat Rev Genet 12:671 (2011)

18. Graph-based transcript assembly
Martin and Wang Nat Rev Genet 12:671 (2011)

19. Trinity: de novo transcript assembly
Grabherr et al. Nat Biotechnol 29:644 (2011)

20. What depth of sequencing is required to characterize a transcriptome?
Wang et al. Nat Rev Genet 10:57 (2009)

21. Considerations Gene length: Expression level:
Long genes are detected before short genes
Expression level:
High expressors are detected before low expressors
Complexity of the transcriptome:
Tissues with many cell types require more sequencing
Feature type
Composite gene models
Common isoforms
Rare isoforms
Detection vs. quantification
Obtaining confident expression level estimates (e.g., “stable” RPKMs) requires greater coverage

22. Transcript detection is biased in favor of long genes
Tarazona et al. Genome Res 21:2213 (2011)

23. Applications of mRNA-seq
Characterizing transcriptome complexity
Alternative splicing
Differential expression analysis
Gene- and isoform-level expression comparisons
Novel RNA species
lincRNAs and eRNAs
Pervasive transcription
Translation
Ribosome profiling
Allele-specific expression
Effect of genetic variation on gene expression
Imprinting
RNA editing
Novel events

24. Alternative isoform regulation in human tissue transcriptomes
Wang et al Nature 456:470 (2008)

25. Diversity of alternative splicing events in human tissues
Wang et al. Nature 456:470 (2008)

26. Differential expression
Garber et al. Nat Methods 8:469 (2011)

27. Programs for identifying DE genes in RNA-seq datasets
Assumed distribution for count data
URL
DESeq
Negative binomial
www-huber.embl.de/users/anders/DESeq/
DEGseq
Poisson
edgeR
baySeq
Cuffdiff
cufflinks.cbcb.umd.edu/

28. Differential expression:
Characterizing transcriptome dynamics during brain development
Neuronal functions
synaptic transmission
cell adhesion
Embryonic mouse cortex
RNA-seq
DEX
Neuronal
migration
“Stemness” functions
Cell cycle
M phase
Sox2, Oct4
Ayoub et al PNAS 1086:14950 (2011)

29. Differential expression:
Characterizing transcriptome dynamics during brain development
Embryonic mouse cortex
Differential isoforms
RNA-seq
DE isoforms
Ayoub et al PNAS 1086:14950 (2011)

30. Novel RNA species: annotating lincRNAs
Guttman et al Nat Biotechnol 28:503 (2010)

31. Enhancer-associated RNAs (eRNAs)
Neurons treated with KCL
Kim et al Nature 465:182 (2010)

32. Enhancer-associated RNAs (eRNAs)
Ren B. Nature 465:173 (2010)

33. How much of the genome is transcribed?
van Bakel et al. PLoS Biol. 8:e (2010)

34. Exploiting sequence information in RNA-seq reads
Majewski and Pastinen. Trends Genet 27:72 (2011)

35. Detecting variants that affect splicing
Pickrell et al . Nature 464:768 (2010)

36. mRNA-seq applications
Summary:
mRNA-seq applications
Quantify transcriptome complexity and compare across biological states
Determine how transcriptomes are translated in
different biological contexts
Effect of genetic variation on gene expression
Imprinting and RNA editing