RNA-seq workflow Martin and Wang Nat Rev Genet 12:671 (2011)
Wang et al. Nat Rev Genet 10:57 (2009)
Slide 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
Slide 7
Ribosomal RNA subtraction RiboMinus
Slide 8
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
Quantifying relative expression levels in RNA-seq
Slide 9
Mapping RNA-seq reads
Slide 10
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) Quantifying relative expression levels in RNA-seq 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)
Slide 11
Map reads to genome Map remaining reads to known splice
junctions Composite gene models Requires good gene models Isoforms
are ignored
Slide 12
Which gene annotation to use?
Slide 13
Martin and Wang Nat Rev Genet 12:671 (2011) Splice-aware short
read aligners
Slide 14
The Tuxedo suite Trapnell et al. Nature Protocols 7:562
(2012)
Slide 15
Cufflinks: ab initio transcript assembly Trapnell et al. Nat.
Biotechnology 28:511 (2010) Step 1: map reads to reference
genome
Slide 16
Trapnell et al. Nat. Biotechnology 28:511 (2010) Isoform
abundances estimated by maximum likelihood Cufflinks: ab initio
transcript assembly
Slide 17
Differential expression Garber et al. Nat Methods 8:469
(2011)
Slide 18
Differential expression Garber et al. Nat Methods 8:469 (2011)
Popular methods: EdgeR DEseq Cuffdiff Require count data Assume
negative binomial or Poisson distribution
Slide 19
Wang et al. Nat Rev Genet 10:57 (2009) What depth of sequencing
is required to characterize a transcriptome?
Slide 20
Considerations Gene length: 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
Slide 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
Slide 22
Wang et al Nature 456:470 (2008) Alternative isoform regulation
in human tissue transcriptomes
Slide 23
Wang et al. Nature 456:470 (2008) Diversity of alternative
splicing events in human tissues
Slide 24
Novel RNA species: annotating lincRNAs Guttman et al Nat
Biotechnol 28:503 (2010)
Slide 25
Small RNA sequencing Rother and Meister, Biochimie 93: 1905
(2011)
Slide 26
Small RNA sequencing Rother and Meister, Biochimie 93: 1905
(2011) microRNAs ~22 nt piRNAs ~25-30 nt
Slide 27
Small RNA sequencing: Illumina protocol microRNAs ~22 nt piRNAs
~25-30 nt
Slide 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)
Slide 29
Measuring translation by ribosome footprinting Ingolia, Nat Rev
Genet 15:205(2014)
Slide 30
Measuring translation by ribosome footprinting Ingolia et al.
Science 324:218 (2009)
Slide 31
Measuring translation by ribosome footprinting Ingolia et al.
Science 324:218 (2009)
Slide 32
Some lincRNAs are translated in mouse ES cells Ingolia et al.
Cell 147:789 (2011)