1. Transcriptomics
Jim Noonan
GENE 760

3. Transcriptomics

4. Introduction to RNA-seq

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

6. 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

7. Ribosomal RNA subtraction
RiboMinus

8. Quantifying relative expression levels in 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?
Differential expression
De novo transcript assembly:
Assemble transcripts directly from reads
Allows transcriptome analyses of species without reference genomes

9. Mapping RNA-seq reads

10. Quantifying relative expression levels in RNA-seq
Reads per kilobase of feature length per million mapped reads (RPKM)
Fragments per kilobase per million mapped reads (FPKM) (paired-end reads)
Transcripts per million (TPM)
Counts per million (CPM)
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)

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

12. Which gene annotation to use?

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

14. The ‘Tuxedo’ suite
Trapnell et al. Nature Protocols 7:562 (2012)

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

18. Differential expression
Popular methods:
EdgeR
DEseq
Cuffdiff
Require count data
Assume negative binomial or
Poisson distribution
Garber et al. Nat Methods 8:469 (2011)

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

20. 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

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

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

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

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

25. Small RNA sequencing
Rother and Meister,
Biochimie 93: 1905 (2011)

26. Small RNA sequencing microRNAs ~22 nt piRNAs ~25-30 nt
Rother and Meister,
Biochimie 93: 1905 (2011)

27. Small RNA sequencing: Illumina protocol
microRNAs ~22 nt
piRNAs ~25-30 nt

28. Distinguishing functional small RNAs from noise
Structural similarity to known small RNAs: miR-deep, miR-cat
Binding to small RNA processing proteins
Genetic requirements for processing
Friedlander et al. Nat Biotechnology 26:407 (2008)

29. Measuring translation by ribosome footprinting
Ingolia, Nat Rev Genet 15:205(2014)

30. Measuring translation by ribosome footprinting
Ingolia et al. Science 324:218 (2009)

31. Measuring translation by ribosome footprinting
Ingolia et al. Science 324:218 (2009)

32. Some lincRNAs are translated in mouse ES cells
Ingolia et al. Cell 147:789 (2011)

33. Detecting RNA-protein interactions: CLIP
Rother and Meister,
Biochimie 93: 1905 (2011)

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

35. Enhancer-associated RNAs (eRNAs)
Kim et al Nature 465:182 (2010)

36. How much of the genome is transcribed?
Estimates from
ENCODE
Kellis et al. Proc. Natl. Acad. Sci. USA 111:6131 (2014)