Cell/Tissue di erences are re ected in...

Rickard Sandberg

Transcriptomics with RNA-Seq

Assistant ProfessorLudwig Institute for Cancer ResearchDepartment of Cell and Molecular BiologyKarolinska Institutet

Feb 2012Thursday, May 24, 12

hair cells hippocampal neuron

kidney cells

Mammals: 100s of cell types, tissues, organs, systems

muscle cells

Cell/Tissue di!erences are re"ected in gene expression patterns

Thursday, May 24, 12


kidney cells


muscle cells




kidney cells


muscle cells


zygote blastocyst



kidney cells


muscle cells


zygote blastocyst

All the information needed to encode an organisms is captured in the genome of the zygote together with the proteins that act on the genome


Transcriptome analyses


Transcriptome analyses

- rRNAs (dominating, ~95%)

- mRNAs (~5%)

- long non-coding RNAs (e.g. lincRNAs) (~0.05%)

- snoRNAs, snRNAs

- microRNAs, piRNAs


Di!erent protocols identify di!erent parts of the transcriptome

- rRNAs

- mRNAs

- long non-coding RNAs (e.g. lincRNAs)

- snoRNAs, snRNAs

- microRNAs, piRNAs

PolyA selection



Ribominus(removal of

ribosomal RNAs)

- rRNAs

- mRNAs


- snoRNAs, snRNAs

- microRNAs, piRNAs



Ribominus(removal of

ribosomal RNAs)

- rRNAs

- mRNAs


- snoRNAs, snRNAs

- microRNAs, piRNAs

not so randomhexamers or DSN



small RNA protocol

- rRNAs

- mRNAs


- snoRNAs, snRNAs

- microRNAs, piRNAs


Methods for sequence library generation


Isolate polyA+ RNA

mRNA-seq protocol

Wang et al. 2009 Nat Rev Gen


Isolate polyA+ RNA

mRNA-seq protocol



Isolate polyA+ RNA

mRNA-seq protocol


! polyA+ RNAs! rRNA- RNAs! short RNAs (e.g. miRNAs)! Ribosome footprint

sequencing! GRO-Seq (Global Run On

sequencing)! CLIP-Seq (RNA-protein

interactions)

! non-RNA applications:ChIP-Seq, DNAse hypersensitive sites,...


Strand-speci#c RNA-Seq protocols


TestesLiverSkeletal MuscleHeartAK074759BC011574AK092689

log 1

0(read

s) 02

02

02

02

3B

3A

3B

RNA-Seq generate quantitative expression estimates

<10M readsThursday, May 24, 12


log 1

0(read

s) 02

02

02

02

3B

3A

3B

RNA-Seq generate quantitative expression estimates

<10M reads

Brain expression / UHR expression (Taqman)

Bra

in R

eads

/ U

HR

Rea

ds (

RN

A-S

EQ

)

104

R = 0.953slope = .933103

102

101

100

10-1

10-2

10-3

10-4

104 103 102 101 100 10-1 10-2 10-3 10-4

Mortazavi et al. Nat Methods 2008Ramskold et al. PLoS Comp Biol 2009

03691215 12.3

0.13 0.10Exon Intron Intergenic

MKPR

Wang*, Sandberg* et al. Nature 2008

150x


How gene expression levels are estimated

gene A (2 kb transcript)gene B (600 bp transcript)




FragmentationThe number of fragments are proportional to the abundance and length of the transcript.




ACGCG...TCGAG...AGGTA...CCGTG...CTGCG...

Sequencing






Sequencing


Normalize for different transcripts lengths and different sequence depths in different samples.

RPKM (Reads per kilobase and million mappable reads): Given 10 million mappable reads:

RPKM, Gene A: 500 reads x 1000/2000 x 106/107

500 / (2 x 10) = 25 RPKM

RPKM roughly corresponds to transcripts per cell (Mortazavi et al. 2008)(assuming a standard cell with ~ 300.000 transcripts)





Sequencing


Normalize for different transcripts lengths and different sequence depths in different samples.

RPKM (Reads per kilobase and million mappable reads): Given 10 million mappable reads:


500 / (2 x 10) = 25 RPKM

RPKM roughly corresponds to transcripts per cell (Mortazavi et al. 2008)(assuming a standard cell with ~ 300.000 transcripts)

Fragments PKM (FPKM)


Gene quanti#cation and mRNA copy numbers in cells

CN

X LT

=

X =109R T

C, number of reads mapping to transcriptN, total number of sequenced reads

X, copies per cell of transcriptT, total length of transcriptomeL, transcript length

R, RPKM (reads per kilobase and million mappable reads)

T, can be estimated from

1. starting amount of mRNA2. spiked in controls3. estimate transcriptome length - if 300.000 transcript of around 1500 nt each -> 4.5 *108

- 1 RPKM ~ 0.5 transcripts per cell

XN LC T= =

106R T103


Use molecular barcodes

Kivioja et al. Nature Methods 9, 72–74 (2012)


RNA sequencing of blastocyst-derived cell lines

Read counts for selected genes

ES TS XEN EpiSCNanog 6525 20 1 263

Cdx2 124 6256 1 1

Sox17 11 5 9814 99

Sox3 151 1234 6 796

Shh 0 0 0 1

Ihh 4 12 107 17

Dhh 10 212 575 80


Signi#cance of expression level

background RPKM ~ 0.05 RPKMdetection level of 0.3 RPKMan average 1 500 nt transcript20 M uniquely mapping reads

background model:0.05 x 1.5 x 20 = 1.5 reads

expressed at 0.3 RPKM:0.3 x 1.5 x 20 = 9 readsbinomial test for 9 reads out of 20 M mappingto transcript given a background probability of 1.5 / 20x109

gives a p-value of 2.8e-5

expressed at 1 RPKM:1 x 1.5 x 20 = 30 reads

0.05 RPKM1 RPKM


Depth needed for accurate expression level estimation

Perc

enta

ge o

f gen

es w

ithin

±20

% o

f fin

al e

xpre

ssio

n

100

80

60

40

20

01 5 10 15 20 25 30 35 40 45

1-9 RPKM (n=4338)10-29 RPKM (n=3048)30-99 RPKM (n=2817)100-999 RPKM (n=1469)1000-6705 RPKM (n=56)

Million mapped reads

B

A

01 5 10 15 20 25 30 35 40 45

Million mapped reads

Perc

enta

ge o

f gen

es w

ithin

fold

-cha

nge

of fi

nal e

xpre

ssio

n

100

80

60

40

20

2-fold1.5-fold1.2-fold1.1-fold1.05-fold

Mortazavi et al. 2008 Ramskold/Kavak et al. 2011 (bookchapter)


Our default view of gene expression?


mRNA isoform regulation

Alternative Promoters

CoreExtens.

Alternative Splice Sites

MXE1 MXE2

Mutually Exclusive Exons

5‘ Exon 3‘ ExonSE

Skipped Exons

Alternative Polyadenylation

pA pA


• Expressed Sequence Tags• Traditional 3’UTR focused microarrays• Exon and Tiling Arrays• Deep Sequencing using Illumina/Solexa, SOLiD, (454)

Genome-wide detection of mRNA isoforms


RNA-Sequencing: Challenges and opportunities

! Aligning RNA-Seq data

! Di!erential expression

! De novo transcriptome reconstruction

! Alternative RNA isoforms

! Gene fusion events

! SNPs and mutations

! RNA-Seq analyses pipelines

! Single-cell RNA-Sequencing


Mapping of millions of short reads

Task: Map millions of short sequences (25-100 nt) onto a genome (3 000 Mbp ) or transcriptome

Mismatches (sequencing errors and SNPs)

Unique / Repetitive matches

Indels (Normal variation, CNVs)

Large rearrangements (translocations)

BLAST, BLAT tools not designed for these tasks


Strategies for mapping splice junctions

- Compilations of known and putative splice junctions and consequent mapping towards genome and junctions

- Mapping of reads towards genome to !nd exons, then search unmapped reads towards all combinations of exon-exon border from the exons found (and given some maximal distance)

- Split read in smaller parts, map separately, !nd the junctions

- Map towards transcriptome, convert coordinates to genome, remove redundancies,


Genome Chromosome Fasta Files

+

Known and putative splice junctions Fasta File

2. map reads towardsgenome + junction compilation

GTAAGT-----------AG Exon n+1

1. compile sets of junctions

Exon n

Compilation of splice junctions


Tophat MethodIdentifying the transcriptome

A B C identify candidate exons

via genomic mapping

A B C A B C Generate possible

pairings of exons

Align “unmappable”

reads to possible junctions

A B C A B C


Longer readsLonger reads

GATGTTCTCAGTGTCC GATGTAATCAGTGTCC AACCCTCTCAGTGTCC

>HWI-EAS229_75_30DY0AAXX:7:1:0:949

Very long (100Kb+) intron

By segmenting the long reads, and mapping the segments independently, we can

look harder for junctions we might have missed with shorter reads

Running time

independent of

intron size


Mapping to transcriptomeExons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C

pre-mRNA

Transcription

AAAAA

RNA processing (splicing, polyadenylation)

mRNA AAAAA

Exons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C


Microexons and junction coverage


DNA (genome)W

C

2 or more splice junctions within the same read

in-house mapping tophat mapping


Microexons and junction coverage


DNA (genome)W

C

2 or more splice junctions within the same read

in-house mapping tophat mapping

Di"erent read length will have di"erent problems!Thursday, May 24, 12

Mismapping rates for splice junctions

discovery of novel junctions


Mapping of RNA-Seq reads

Garber et al. 2011 Nat Methods


Paired reads mapping can be more accurate

Picking the right alignment

2 mismatches Exact match

Bowtie reports the “best” alignment it comes across, but this isn’t

always the right one. To do a better job, we want paired end

reads


! Check the fraction of reads that mapped

! Check the fraction of splice junctions mapped

! (optional) Reads are positioning along transcripts

! and Visualize the data!

Pro#le the mapped data to #gure out how well the library prep worked


Visualization

Integrated Genome Viewer (Broad Inst.)

Custom tracks at UCSC Genome Browser


Visualization of aligned reads (in IGV)


Integrated Genome Viewer

Imports many mentioned formats (SAM, BAM, BED etc)

Excellent for visualization of RNA-Sequencing or ChIP-sequencing data

Can also download/visualize data from public or private servers



! Di!erential expression









Time for more quality controls:Look at replicates and that samples group by

origin/type

Hierarchical clustering

!100

!50

0

50

100

150

í100 !50 0 50 100 150

PC3 (n=4)

T24(n=4)

Lncap (n=4)

SVD component 1

SVD

com

pone

nt 2

PCA / SVD


Di!erential Expression

Either based on reads or RPKM values


500 / (2 x 10) = 25 RPKM

Most tools developed for microarrays are based on RPKM values,whereas RNA-Seq tools aim to use read counts

Reads • have more statistical power• have unresolved biases• need fewer replicates?

RPKMs• better understood statistics, but lack of power

log 1

0(read

s) 02

02

02

02

3B

3A

3B


Statistical models of di!erential expression


non-coding RNAs in prostate cancer:Expression and di!erential expression


Transcript length e!ects in di!erential expression tests

Oshlack and Wake!eld Biology Direct 2009Thursday, May 24, 12

Transcript length e!ects in di!erential expression tests

Oshlack and Wake!eld Biology Direct 2009

p-values should not be the basis for sorting


! Library generation


! Gene expression calculations and di!erential expression









Finding novel non-annotated genes or transcript variants


Two principal approaches for transcriptome reconstruction


scripture cufflinks

Genome-guided transcriptome reconstruction


Transcript reconstruction

Nature Biotechnology, April 2010


Increased depth improves reconstruction



Discovery of cell-type speci#c alternative isoforms



Genome-independent transcriptome reconstruction

Garbherr et al. Nature Biotechnology, July 2011

Default k = 25



Genome-independent transcriptome reconstruction: accuracy and coverage


Genome-independent transcriptome reconstruction: accuracy and coverage







! Alternative RNA isoforms (e.g. alternative splicing)







mRNA isoform regulation

Alternative Promoters

CoreExtens.

Alternative Splice Sites

MXE1 MXE2

Mutually Exclusive Exons

5‘ Exon 3‘ ExonSE

Skipped Exons

Alternative Polyadenylation

pA pA


Alternative Splicing as a Switch and as a Tuner



Switching on the Fas receptor

5 76

Cascino et al. 1995



Soluble Inhibition of apoptosis5 7


5 76

Cascino et al. 1995




Membrane-bound Apoptosis5 76


5 76

Cascino et al. 1995






5 76

Cascino et al. 1995






5 76

Cascino et al. 1995

Tuning the inner ear: splicing of calcium-activated potassium channels in hair cells

32v2

Ramanathan et al. 1999



Low frequencies2 32v




5 76

Cascino et al. 1995


32v2




Low frequencies2 32v

High frequencies2 3




5 76

Cascino et al. 1995


32v2



SE

An theoretical example: a skipped exon event

Brain

Liver

Detecting alternatively spliced exons


SE

An theoretical example: a skipped exon eventinclusion exclusion

Brain

Liver

Reads supporting:

(1+2+1)0

1 2

4Brain

Liver



SE


Brain

Liver

Reads supporting:

(1+2+1)0

1 2

4

p~0.14

Brain

Liver



SE

5’ CE 3’ CESE


Brain

Liver

Reads supporting:

(1+2+1)0

1 2

4

p~0.14

Brain

Liver



SE

5’ CE 3’ CESE


Brain

Liver

Reads supporting:

(1+2+1)0

1 2

4

inclusion exclusion

Brain

Liver

Reads supporting:

(1+2+1) (2+1)

1(4+2+4)

4

10

3

p~0.14

Brain

Liver



SE

5’ CE 3’ CESE


Brain

Liver

Reads supporting:

(1+2+1)0

1 2

4

inclusion exclusion

Brain

Liver

Reads supporting:

(1+2+1) (2+1)

1(4+2+4)

4

10

3

p~0.14

p<0.05

Brain

Liver



Tissue-regulated mRNA isoforms

Wang*, Sandberg* et al. 2008 Nature


Coverage needed: Alternative mRNA isoform events

Assuming:comparing two isoforms with 25% vs 75% inclusion levels


The power of paired-end reads (informative length)

Katz et al. Nature Methods 2010


from Expression to Regulation, “RNA-maps”

Licatalosi D., et al. Nature 2008

Binding of NOVA splicing factor

-50 0 0 0 +50

0.0

0.2

0.4

0.6

0.8

1.0

Mean phastCons score

+50 -50

S E

-50 +500

Position relative to splice junction

a d

0.0

0.2

0.4

0.6

0.8

1.0

Exon Inclusion Level, Second Tissue

0.0 0.2 0.4 0.6 0.8 1.0

Exon Inclusion Level, Heart

TPM1 exon 2

SLC25A3 exon 3

Heart: 92%

Brain: <1%

SE

MXE

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Cumulative frequency

Switch score

pKS = 3.7e-5

b

0 - 0.25 0.25 - 0.5 0.5 - 1

Switch score bin

0.0

0.2

0.4

0.6

0.8

1.0

Fraction frame-preserving

c

p = 6e-10

p = 8e-15

p = 0.01SE

MXE

e

Tissue-biased inclusion

Tissue-biased exclusion

skipped exon

constitutive exon

UGCAUG (Fox1/2)

0 4

-log10(p value)

breast

adipose

brain

colon

heart

liver

lymph node

skel. muscle

testes

1 2 3

Figure 4

high (0.5 - 1.0)

medium (0.25 - 0.5)

low (0 - 0.25)

SE switch score

colon

skel. musclelymph nodeliver

adipose

testes

brain

skipped exon mutually exclusive exon

breast cerebellum

cerebellum

Wang*, Sandberg* et al. 2008 Nature


• Model this as a signal separation problem (signal and image processing !eld)

• Improve with more even read densities over exons

Deconvolution of mRNA isoform expression


log 1

0(read

s) 02

02

02

02

3B

3A

3B

Unique regions for di"erent isoforms













Fusion events, e.g. translocations in cancer

Oszolak and Milos, Nature Rev Genet 2011













Single nucleotide polymorphism and mutations

! Estimate SNPs/mutations from RNA-Seq data using similar techniques as for genomic variation, but

! uneven coverage dictated from mRNA copy numbers

! limited to coding sequence and untranslated regions


Quality controls on variations found


Allelic imbalance

Skelly et al. Genome Res 2011


Mixed species/strains experiments

! Mixed species experiments allows mapping of host and pathogen interactions

! Parasite-host interactions

! Tumor-stroma interactions


Cross-strain experiments


Threshold for allele-speci"c expression?


Conclusions

• RNA-seq enables genome-wide transcriptome quanti!cation with more accurate and absolute expression estimates

• Low background enables quanti!cation of lowly expressed transcripts (~1 copy per cell)

• Investigate alternative promoters, splicing and polyadenylation, non-coding RNAs

• Allows for de novo transcriptome reconstruction and gene expression analyses in organisms without reference genome


Cell/Tissue di erences are re ected in...

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