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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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!!!
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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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564
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19415
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60
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Absolu
te t!
sta
tistics
(a) Full-length UI genes
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(b) 250-bp UI gene regions
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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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
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!! Micro.
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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in:F
3
UH
R:F
2
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R:F
3
Read c
ount
0e+00
1e+06
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7e+06FPM
FMM
MM
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+
!
rrppolyA!
xrnTT!
rrp
xrn
wt
david!
snyder
nucleosome
miura!
conservation
YER015W YER016W
186500 187000 187500 188000 188500 189000
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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