RNA-Seq: Sequencing the Transcriptome - Bioconductor · PDF fileRNA-Seq: Sequencing the...

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RNA-Seq: Sequencing the Transcriptome Kasper Daniel Hansen Department of Biostatistics Johns Hopkins Bloomberg School of Public Health FHCRC, Seattle 12th-14th of November 2008 Friday, November 20, 2009

Transcript of RNA-Seq: Sequencing the Transcriptome - Bioconductor · PDF fileRNA-Seq: Sequencing the...

RNA-Seq: Sequencing the Transcriptome

Kasper Daniel HansenDepartment of Biostatistics

Johns Hopkins Bloomberg School of Public HealthFHCRC, Seattle 12th-14th of November 2008

Friday, November 20, 2009

RNA-Seq: Comparison with Microarrays

Potential for surveying the entire transcriptome, including novel, un-annotated regions.

Potential for determining gene structure and isoform level expression using reads mapping to splice junctions.

Potential for making better presence/absence calls on regions.

Potential for allelle specific expression combined with SNP calling.

Con: the assay is dependent on sequencing effort, low expressed regions will be missed.

Friday, November 20, 2009

Protocol

The current standard protocol for RNA-Seq is

Extraction of RNA, polyA purification Fragmentation of RNA RT of RNA to cDNA Ligation of adapters Size selection ~ 200bp (perhaps ~300bp) PCR amplification (15 rounds or so) Injection into flowcell

This produces reads from polyadenylated RNA without strand information.

Attempts are being made to make the assay strand specific and to assay total RNA as well.

Friday, November 20, 2009

Data from D. melanogaster:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Friday, November 20, 2009

Base effect - single sample

14.1m reads

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

Friday, November 20, 2009

Base effect - multiple samples

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

wt

rrp

ski

Friday, November 20, 2009

Base effect - different study (and prep)

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

WT

Nagalakshmi

Friday, November 20, 2009

Base effect - different prep

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

original

ribominus

Friday, November 20, 2009

Base effect - different aligners50 100 150

050

100

150

MAQ

position

counts

UHR(MAQ)

UHR(ELAND)

50 100 150

0.00

0.02

0.04

MAQ

position

counts

MAQ and ELAND, Human data

Friday, November 20, 2009

Base effect - conclusions

Reproducible base effect - like probe affinities in microarrays.

Seems to be prep dependent.

Creates issues for comparing differentregions in the genome.

Less of an issue for comparing thesame region across samples?

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

?

Friday, November 20, 2009

Mapping reads to the transcriptome

Transcriptome

2^Genome

Reads

Genome

Illustration from Lior Patcher

Well established

Friday, November 20, 2009

Mapping transcripts

Genome

Transcript

Length in genome space

paired-end reads

Friday, November 20, 2009

Junction reads:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Friday, November 20, 2009

Junction reads, zoom

:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Friday, November 20, 2009

The basic approachesthe ‘noise’ level generated by mismapped reads or intronic RNA from incompletely spliced heterogenous nuclear RNA (hnRNA). In mouse and human samples, we have especially noticed that prominent read densities often extend well beyond the annotated 3 untranslated regions or as alternatively spliced 5 untranslated regions, internal exons or retained introns. ERANGE, G-Mo.R-Se and TopHat first aggregate reads into transfrags. Whereas G-Mo.R-Se and TopHat rely primarily on spliced reads to connect transfrags together, ERANGE uses two different strategies depending on the availability of paired reads. In the currently conventional unpaired sequence read case, ERANGE assigns transfrags to genes based on an arbitrary user-selected radius, whereas in the paired-end read case, it will bring together transfrags only when they are connected by at least one paired read. Both strategies work much better with data that preserve RNA strandedness.

Quantifying gene expression. Given a gene model and mapped reads, one can sum the read counts for that gene as one measure of the expression level of that gene at that sequencing depth. However, the number of reads from a gene is naturally a function of the length of the mRNA as well as its molar concentration. A simple solution that preserves molarity is to normalize the read count by the length of the mRNA and the number of million mappable reads to obtain reads per kilobase per million (RPKM) values18. RPKMs for genes are then directly comparable within the sample by pro-viding a relative ranking of expression. Although they are straight-forward, RPKM values have several substantive detail differences between software packages, and there are also some caveats in using them. Whereas ERANGE uses a union of known and novel exon models to aggregate reads and determine an RPKM value for the locus, TopHat and RSAT restrict themselves to known or prespeci-fied exons. ERANGE will also include spliced reads and can include assigned multireads in its RPKM calculation, whereas other pack-ages are limited to uniquely mappable reads.

Several experimental issues influence the RPKM quantification, including the integrity of the input RNA, the extent of ribosomal RNA remaining in the sample, size selection steps and the accuracy of the gene models used. RPKMs reflect the true RNA concentration best when samples have relatively uniform sequence coverage across the entire gene model, which is usually approached by using random priming or RNA-ligation protocols, although both protocols cur-rently fall short of providing the desired uniformity. Poly(A) prim-ing has different biases (3 ) from partial extension or when there is partial RNA degradation. Resulting ambiguities in RPKMs from an RNA-seq experiment are akin to microarray intensities that need to be post-processed before comparison to other RNA-seq samples using any number of well-documented normalization methods, such as variance stabilization42, for example.

More sophisticated analyses of RNA-seq data allow users to extract additional information from the data. One area of considerable inter-est and activity is in transcript modeling and quantifying specific isoforms. BASIS calculates transcript levels from coverage of known exons by taking advantage of specifically informative nucleotides from each transcript isoform. A second area is sequence variation. The RNA sequences themselves can be mined to identify positions where the base reported differs from the reference genome(s), identifying either a single-nucleotide polymorphism or a private mutation25,43. When these are heterozygous and phased or informatively related to the source genome, RNA single-nucleotide polymorphisms can be

(SINEs and LINEs) in the untranslated regions of genes as well as the abundance of retroposed pseudogenes for highly expressed housekeeping genes in large genomes. Both of these vary from one genome to the next39. For example, several GAPDH retroposed pseudogenes in the mouse genome differ by less than 2 nucleotides (0.2%) from the mRNA for GAPDH itself, making it difficult to map reads correctly to the originating locus based on RNA-seq data alone. Orthogonal data such as RNA polymerase II occupancy and ChIP-seq measurements can later be brought to bear in some cases, but different software and use parameters make starting choices based on the RNA data alone. Whereas the algorithms are generally sensible, specific cases can be insidious and are worth being aware of. For example, a minority of reads from one paralog can map best to other sites (usually another paralog or pseudogene) because of the error rate in sequencing, which is quite substantial on current platforms (typically around 1%). For highly expressed genes, this can cause a shadow of expression at these pseudogenes, which may then be called as transfrags. Similarly, reads that are intron-spanning from a source gene may map instead perfectly and uniquely to a retroposed pseudogene. The ERANGE package avoids such mis-assignment by mapping reads simultaneously across the genome and splice junctions, thus turning them into multireads that are subsequently handled separately.

Assigning reads to known and new gene models. The next level of RNA-seq analysis associates mapped reads with known or new gene models. Given a set of annotations, all tools can tally the reads that fall on known gene models, and several tools like RSAT40 and BASIS41 deal primarily with the annotated models. However, a sub-stantial fraction of reads fall outside of the annotated exons, above

De novo assembly of the transcriptome

Map onto the genome and splice junctions

Map onto the genome

Highly expressed gene

Lowly expressed gene

Read coverage mustbe high enough to buildEST contigs (solid bar)

Read mapper mustsupport splitting readsto record splices

Splice junctionssequences fromeither annotationsor inferred

AAA

AAA

a

b

c

Figure 6 | Approaches to handle spliced reads. (a) In de novo transcriptome assembly, splice-crossing reads (red) will only contribute to a contig (solid green), when the reads are at high enough density to overlap by more than a set of user-defined assembly parameters. Parts of gene models (dotted green) or entire gene models (dotted magenta) can be missed if expressed at sub-threshold. (b) Splice-crossing reads can be mapped directly onto the genome if the reads are long enough to make gapped-read mappers practical. (c) Alternatively, regular short read mappers can be used to map spliced reads ungapped onto supplied additional known or predicted splice junctions.

S30 | VOL.6 NO.11s | NOVEMBER 2009 | NATURE METHODS SUPPLEMENT

REVIEW

From Pepke (2009 Nat Methods)

Friday, November 20, 2009

Strategies for mapping to junctions

Map to known junctions (or to known transcripts, but that involved a lot of bookkeeping).

Map to combination of known exons.

Map completely de-novo using canonical acceptor and donor sites. (huge!)

Map de-novo, but constrain the search to canonical acceptor and donor sites between and in transcribed region: transcript assembly. (TopHat does this).

Paired-end data will help with this.

Friday, November 20, 2009

FP rates for junctionsDistinguishing Con!dent Junctions from

False Positives

0%

10%

20%

30%

40%

1+ Offsets 2+ Offsets 3+ Offsets 4+ Offsets 5+ Offsets

0%

0.018%

0.035%

0.053%

0.070%

1+ Offsets 2+ Offsets 3+ Offsets 4+ Offsets 5+ Offsets

1

Offset

2

Offsets

3

Offsets

4

Offsets

5

Offsets 3! Exon5! Exon

3! Exon5! Exon

3! Exon5! Exon

3! Exon5! Exon

3! Exon5! Exon

Annotated Junctions (n= 58,212)

Randomly Generated Junctions (n=5,409,600)

% o

f To

tal A

nn

ota

ted

Ju

nc

tio

ns

% o

f To

tal

Ra

nd

om

Ju

nc

tio

ns

Brenner, Graveley, DudoitFriday, November 20, 2009

Mapping - conclusions

Mapping to transcript space is not easy.

But essential for really understanding alternative splicing.

Friday, November 20, 2009

0 5 10 15

020

40

60

80

100

Verified CDSdepth of 3

number of reads in millions

perc

ent bases s

equenced

0 5 10 15

020

40

60

80

100

Dubious CDSdepth of 3

number of reads in millions

perc

ent bases s

equenced

0 5 10 15

020

40

60

80

100

Uncharacterized CDSdepth of 3

number of reads in millions

perc

ent bases s

equenced

0 5 10 15

020

40

60

80

100

Intronic Regionsdepth of 3

number of reads in millions

perc

ent bases s

equenced

0 5 10 15

020

40

60

80

100

Background Regionsdepth of 3

number of reads in millions

perc

ent bases s

equenced

wt

rrp

ski

xrn

Nagalakshmi

Coverage

Friday, November 20, 2009

Detection in Cerevisiae

0 20 40 60 80 100

020

40

60

80

100

Intronic Regions

Percent introns detected

pe

rce

nt

ge

ne

s d

ete

cte

d

wild type

!rrp6!lsm1!pat1

!ski2!ski8!rrp6

!xrn1!rrp6!lsm1!pat1

Nagalakshmi

0 20 40 60 80 1000

20

40

60

80

100

Background Regions

Percent background detected

pe

rce

nt

ge

ne

s d

ete

cte

d

wild type

!rrp6!lsm1!pat1

!ski2!ski8!rrp6

!xrn1!rrp6!lsm1!pat1

Nagalakshmi

A. B.

Background: outside any transcribed feature, subtracted a boundary, subtracted any region detected as transcribed in recent studies

Friday, November 20, 2009

Detection in Drosophila

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

percent of intergenic regions detected

perc

ent of constitu

tive e

xons d

ete

cte

duntreated - 32.9m reads

brr2 - 15.0m reads

pasilla - 28.3m reads

1 read in 200bp, 10m reads

10 reads in 200bp, 10m reads

Friday, November 20, 2009

Replication

Sources of variation

Lane variation

Flowcell variation

Library prep variation

Biological variation

Poisson model

good fit

less good fit

Systematic differences

?: Is absolute quantification possible

Analysis

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0 2 4 6 8 12

02

46

812

mut

theoretical quantiles

observ

ed q

uantile

s

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!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0 5 10 15

05

10

15

wt

theoretical quantiles

observ

ed q

uantile

s

> plot(regionGoodnessOfFit(geneCountsUI, groups = rep("all",+ 4)), chisq = TRUE)

41 / 40

Friday, November 20, 2009

Differential expression (DE)

Various methods have been proposed, all variants on a Poisson model.

We find that Fisher’s test or a GLM based LR test performs well. Of these two, the GLM based model is more flexible.

Normalization matters a lot (later). We suggests a simple upper-quartile global normalization; quantile normalization might be necessary for more noisy datasets.

Most datasets only makes it possible to estimate the technical variance; the biological is ignored. This underestimates the variance.

In general, there is a significant flow cell effect, but the effect is small.

Friday, November 20, 2009

Bias based on gene length

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Figure 14: Differential expression statistics, by length. Boxplots of absolute DE t-statistics (delta method) stratified by length for: (a) full-length genes and (b) a randomsample of 250 base-pairs for each full-length gene longer than 250 base-pairs. Thewidth of each boxplot is proportional to the number of genes within each length stra-tum.

29

Bad for interpretation

Friday, November 20, 2009

DE, the effect of normalization

0.0 0.2 0.4 0.6 0.8 1.0

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(b) qRT-PCR positives: LR> 0.5

Figure 6: Comparison of mRNA-Seq and microarray differential expression calls toqRT-PCR: ROC curves. Genes common to all three platforms and present for bothqRT-PCR and sequencing (see Supplementary Text) were evaluated and declared DEif their qRT-PCR absolute log-ratio was (a) greater than 2 or (b) greater than 0.5; geneswere declared non-DE if their absolute log-ratio was less than 0.2. The GLM-basedlikelihood ratio test was used for the sequencing data. Two normalization proceduresare presented for mRNA-Seq: total-count (black) and upper-quartile (blue) normaliza-tion. Microarray data were normalized using RMA (gray). Note that the ROC curvesdo not reach the point (1,1), because of the sign condition in the definition of truepositives.

21

Friday, November 20, 2009

Normalization

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Figure 18: Pairs plot, comparison of normalization. In the lower diagonal are standardx-y plots, while in the upper-diagonal are MA plots of the difference x-y plotted againstthe average of x and y. No difference between the platforms is shown by a grey line;in the MA plots, the value of the median difference is shown as a red line. Note thatdifference between total counts and median normalization is just a shift of the log-ratiovalues and thus we show only the total-counts normalization.

33

Seq (total) isessentiallyRPKMs

Friday, November 20, 2009

Running phi X does not seem necessary

Bra

in:F

2

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FMM

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with phi X

without phi X

Figure 3: Impact of base-calling calibration method on read-mapping. Barplots of av-erage read counts per lane with and without phi X calibration, for each of the four bio-logical sample (Brain, UHR) and flow-cell (F2, F3) combinations. Reads are classifiedinto three nested categories: purity-filtered perfectly matching reads (FPM); purity-filtered reads with either 0, 1, or 2 mismatches (FMM); unfiltered reads with either 0,1, or 2 mismatches (MM).

18

Friday, November 20, 2009

Genome Graphs, example+

!

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conservation

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0.3196

0.32

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00.20.40.60.8

11.2

mcl.9, chr: 5

Friday, November 20, 2009

References

Normalization, PhiX, DE comparisonBullard, Purdom, Hansen, Dudoit (2009, tech report), www.bepress.com/ucbbiostat/paper247/

Gene length biasOshlack, Wakefield (2009, Biology Direct)

Yeast data, coverageLee, Hansen, Bullard, Dudoit, Sherlock (2009, PLoS Genetics)

Current reviewPepke, Wold, Mortazavi (2009, Nat Methods)

A classicMortazavi, Williams, McCue, Schaffer, Wold (2008, Nature)

Friday, November 20, 2009

Acknowledgements

Statistics

Jim BullardSandrine DudoitElizabeth PurdomMargaret TaubSteffen DurinckTerry Speed

RNA assembly

Cole Trapnell

S. Cerevisiae

Gavin SherlockAlbert Lee

D. Melanogaster

Brenton GravelyMike DuffLi YangSteven BrennerAngela Brooks

Friday, November 20, 2009