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RNA-seqanalysis:Fromreadstodifferen6alexpression
AliciaOshlackMurdochChildrensResearchIns6tute
@AliciaOshlack
The current MCRI bioinformatics team • Dr Nadia Davidson • Dr Anthony Hawkins • Dr Jovana Maksimovic • Dr Katrina Bell • Dr Belinda Phipson • Dr Simon Sadedin • Harriet Dashnow • Luke Zappia • Rebecca Evans
PhDposi6onsavailable
MCRIBioinforma6csCollabora6veanalysisandmethodsdevelopment
Transcriptomics Epigenomics Genomics ClinicalGenomics
• RNA-seq• miRNA-seq• Differen6al
expression• Cancer• Non-model
organisms• Alterna6ve
splicing• Singlecell
RNA-seq
• Methyla6onarrays
• Bisulfitesequencing
• Histonemodifica6ons
• ChIP-seq
• Exomeanalysis
• WGS• Targeted
capture• SNPs• CNV• Tandem
repeats
• Clinicalexomes
• TumourRNA-seq
RNA-seqanalysis
Genesandtranscripts
Gene
transcript
SlidefromAliciaOshlack
RNA-seq
Pepkeetal,NatureMethods,2009
Twowaystolookatsequencingdata
Sequenceof(mapped)read• genomesequencing• variantdetec6on• genomicrearrangements• Bisulfite-seq(methyla6on)• RNAedi6ngetc.
Posi6onofmappedread• RNA-seq• ChIP-seq• MeDIP-seqforDNAmethyla6onetc.
7
TwowaystolookatRNA-seqdata
Sequenceof(mapped)read• Assembly• Determininggenes/transcripts
Posi6onofmappedread• Expressionlevels• Differen6alexpression
8
Rawdata(fastqfiles)
• Shortsequencereads• Qualityscores
@HWI-ST1148:308:C694RACXX:5:1101:1768:1990 1:N:0:CGTACG NTAGGCCTTGGCAGTTTTGGAGAATCACTGCTGCCAAAGAGTCTACTTGG + #0<FFFFFFFFFFIIIIIIIIIIIIIIIIIIIIIIIIIIIIFFIIIIIII @HWI-ST1148:308:C694RACXX:5:1101:3409:1990 1:N:0:CGTACG NAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAAC + #000BFBFFFFFFF<BFFFFBBBBBFBBFF<<FBFFIBFFFBFFFIIBFF
50bpsequence
Differen6alexpression• Whichgenesarechangingexpressionlevelbetweensamples?– treatedvsuntreated,– diseasevsnondisease,– earlyvslate6mepoints,– cellsinoneenvironmentvsanotherenvironment,– etc,…
• Thousandsofgenesbutonlyafewsamples– Sophis6catedsta6s6calmethodsarerequiredforanalysis
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
Mappingreadstothegenome• Wheredothemillionsofshortsequencescomefrominthegenome?
• Sequencingtranscripts,notthegenome
CDS CDS CDS CDS
CDS CDS CDS CDS
Gene
transcript
Lotsofgoodalignershandlesplicejunc6onswell(e.g.TopHat,Star)
Exon1 Exon2
RNA-seqdatainIGV
Whichtranscriptometouse?
Rawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
RNA-seqanalysissteps
DenovoassemblyAnnota6onbased GenomeguidedassemblyWhichtranscriptome?
Op6on1
• Useannota6on(knowngenes)– Workswellforwellstudiedorganisms(human,mouse,arabidopsis,drosophila,…)
– onlyasgoodasyourannota6on– Nonoveltranscriptsareanalysed
Op6on2:Genomeguidedtranscriptassembly
• Usestheloca6onanddensityofreadsalongthegenometoassembletranscripts
• E.g.Cufflinks
• Can’tassembleacrossbreaksinthegenome– Cancer,poorgenomes
Op6on3:Denovotranscriptomeassembly
• Assembletranscriptsfromthedatawithoutusingareferencegenome
• “Harder”thangenomeassembly– Ordersofmagnitudevaria6onincoverage– Con6gsareshort– Alterna6veisoforms/transcriptshaveoverlappingsequences– *Very*computa6onallyintensive
• Sokwareincludes – Trinity– Oases(velvet)– TransAbyss– …
Example:Annota6ngthechickenWchromosome
ZZ ZW
Male Female
Twohypothesesformechanismsofaviansexdetermina6on:1. DominantovarydetermininggeneonW(cfmammals)2. DosageofZ-linkedgenes
Thereisanannotatedchickengenome
• ChickenWchromosomeispoorlyassembled• AregenesonotherchromosomesreallyontheW,inpar6culartherandomchromosome?
Chromosome Assembled Size (Mb)
Size inc. random (Mb)
Estimated Size (Mb)
Estimated Genes
(Ensembl) Z 69 70 80 796
W 0.24 0.89 18-54 46
Un_random 56 - - 1287
Experimentaldesign
PCRSexing
+12hourBlastoderms
HandplateforPCRSexing
RNA
Stage26pairedgonads(day4.5)
12Female
RNA
16Femalegonads
PooledSamples
12Female
16Femalegonads
12Male
12Male
16Malegonads
16Malegonads
RNA-seq• IlluminaHiSeq2000• Paired-end100bp• 4lanes• >80millionreads/sample
Definingthetranscriptome
• Annota6on~20,000genes• Genomeguidedassembly(Cufflinks)~45,000genes
• Denovotranscriptomeassembly~2.5milliontranscripts(Abysswithfiltering)!
Acombinedapproach• Assemblecufflinkgenesusingtranscriptsfromourdenovoassembly
Annota6onofthechickenWcombinedallthreeapproaches
W/W_random ChromsomeUn_random ChromosomeAutosomes
Blastoderm CoverageGonads Coverage
Abyss TranscriptsCufflinks TranscriptsEnsembl Transcripts
1000 1500 2000 2500 3000 3500
base position
039
1
Coverage
Genome
EnsemblCufflinks
AbyssRASA1−W
1400 1600 1800 2000 2200
base position
012
6
Coverage
Genome
EnsemblCufflinks
AbyssST8SIA3−W
2000 2200 2400 2600 2800
base position
069
9
Coverage
Genome
EnsemblCufflinks
AbyssGOLPH3−W
0 500 1000 1500 2000 2500
base position
019
4
Coverage
Genome
EnsemblCufflinks
AbyssZSWIM6−W
0 200 400 600 800 1000 1200 1400
base position
082
Coverage
Genome
EnsemblCufflinks
AbyssNEDD4−like−W
FulllistofWgenes/transcriptsfordifferen6alexpressionAyersetal,2013
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
Summariza6on
Takeyour“transcriptome”andaddupthereads
Coun6ngoverexonsvscoun6ngovergenes
Exon1 Exon2
Exon1=8readsExon2=10reads
Coun6ngoverwholegene(Exon1+Exon2)=15
Non-modelorganismsDenovotranscriptomeassembly
1. Clusterassembledcon6gsinto“genes”(independentoftheassembler)
2. Performreadcoun6ngpercluster
Corset Ourmethod
Davidson&Oshlack,GenomeBiology,2014
Summariza6onturnsmappedreadsintoatableofcounts
**veryhighdimensionaldata**
TagID A1 B1ENSG00000124208 478 4830
ENSG00000182463 27 48
ENSG00000125835 132 560ENSG00000125834 42 131ENSG00000197818 21 52ENSG00000125831 0 0ENSG00000215443 4 9ENSG00000222008 30 0ENSG00000101444 46 54ENSG00000101333 2256 2702
… …tensofthousandsmoretags…
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
Summariza6onturnsmappedreadsintoatableofcounts
**veryhighdimensionaldata**
TagID A1 B1ENSG00000124208 478 4830
ENSG00000182463 27 48
ENSG00000125835 132 560ENSG00000125834 320 131ENSG00000197818 21 52ENSG00000125831 0 0ENSG00000215443 4 9ENSG00000222008 30 0ENSG00000101444 46 54ENSG00000101333 2256 2702
… …tensofthousandsmoretags…
Whichgenesaredifferen6allyexpressed?
• LikeanyotherexperimentRNA-seqneedstobereplicatedsowecangetameasureofvariance
Replica6onisessen6al
TagID A1 A2 A3 B1 B2 B2ENSG00000124208 478 619 559 4830 7165 6651
ENSG00000182463 27 20 18 48 55 56
ENSG00000125835 132 290 450 560 408 266ENSG00000125834 320 462 355 131 99 91ENSG00000197818 21 29 23 52 44 65ENSG00000125831 0 0 0 0 0 0ENSG00000215443 4 4 4 9 7 3ENSG00000222008 30 23 23 0 0 0ENSG00000101444 46 63 55 54 53 52ENSG00000101333 2256 2793 2931 2702 2976 2226
… …tensofthousandsmoretags…
**veryhighdimensionaldata**
Qualitycontrol–checkyourdata!
DatafromAndrewElefanty
Sortedcellpopula6ons
Dataexplora6on
Sta6s6caltestsfordifferen6alexpression
TagID A1 A2 A3 B1 B2 B2ENSG00000124208 478 619 559 4830 7165 6651
ENSG00000182463 27 20 18 48 55 56
ENSG00000125835 132 290 450 560 408 266ENSG00000125834 320 462 355 131 99 91ENSG00000197818 21 29 23 52 44 65ENSG00000125831 0 0 0 0 0 0ENSG00000215443 4 4 4 9 7 3ENSG00000222008 30 23 23 0 0 0ENSG00000101444 46 63 55 54 53 52ENSG00000101333 2256 2793 2931 2702 2976 2226
… …tensofthousandsmoretags…
**veryhighdimensionaldata**
• Normalisa6on– Librarysize(sequencingdepth)
• IncludeasoffsetinGLM• Scalingnormalisa6on(sizefactors)
– Composi6onbias(TMM)– Batcheffects(RUVSeq)
Thingstothinkaboutbeforesta6s6caltes6ng
.
(a)
log2(Kidney1 NK1) − log2(Kidney2 NK2)
Den
sity
-6 -4 -2 0 2 4 6
0.0
0.4
0.8
log2(Liver NL) - log2(Kidney NK)
Den
sity
-6 -4 -2 0 2 4 6
0.0
0.2
0.4(b)
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-20 -15 -10
-50
5
A = log2( Liver NL Kidney NK)
M=
log 2
(Liv
erN
L)-
log 2
(Kid
ney
NK)
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Housekeeping genesUnique to a sample
(c)
Sta6s6caltes6ngforDE
• ForEACHGENE,isthemeanexpressionlevelforthegeneunderonecondi6onsignificantlydifferentfromthemeanexpressionlevelunderadifferentcondi6on?
39
TagID A1 A2 A3 B1 B2 B2ENSG00000124208 478 619 559 4830 7165 6651
ENSG00000182463 27 20 18 48 55 56
ENSG00000125835 132 290 450 560 408 266ENSG00000125834 320 462 355 131 99 91ENSG00000197818 21 29 23 52 44 65
… …tensofthousandsmoretags…
Samplingreadsfrompopula6onofDNAfragmentis(approx.)mul2nomial
• Take sample • Sequence DNA
Library 1
Feature 1 λ1 Feature 2 λ2 Feature 3 λ3 Feature 4 λ4 Feature 5 λ5 Feature 6 λ6 …
DNA population
For a single gene, it’s a coin toss, i.e. Binomial
Library 1
… feature i λi …
Yi ~ Binomial( M, λi ) Yi - observed number of reads for feature i M - total number of sequences λi - proportion Large M, small λi à approximated well by Poisson( μi = M�λi )
42
ASmallRNA-SeqExperiment(TechReps)
Condi6onA Condi6onB
λg1 λg2 λg3 λg4
yg1 yg2 yg3 yg4
Genesg=1,…,30k
M1
M2M3 M4
E(ygi)=MiλgiReadsMi≈20million
TrueTechnicalRepsShowPoissonVaria6onforEachGene
Data:Marionietal.,GenomeRes,2008 43
DavisMcCarthy
44
ASmallRNA-SeqExperiment(BiologicalReps)
Condi6onA Condi6onB
λg1 λg2 λg3 λg4
yg1 yg2 yg3 yg4
Genesg=1,…,30k
M1
M2M3 M4
E(ygi)=MiλgiReadsMi≈20million
DavisMcCarthy
BiologicalReplicateDatashowsQuadra6cMean-VarianceRela6onship
(developmentcycleofslimemould,2samplesathr00,&2athr04)
binnedvariance,samplevariance
Data:Parikhetal,GenomeBiology,2010
45DavisMcCarthy
Manydifferentsta6s6calmethods• Modelthecountsdirectly
– Nega6vebinomialmodellingisbestbecauseitcapturesbiologicalaswellastechnicalvariability
– MostpopularpackagesinR• edgeR• DESeq/DESeq2• Lotsofothersexist(baySeq,NBPSeq,…)
• Transformthecountsandusednormalbasedmethods– Voom+limma
Sta6s6caltes6nggiveseachgeneap-valueforevidenceofDE
TagID P-valueENSG00000124208 0.0002
ENSG00000182463 0.12
ENSG00000125835 0.34
ENSG00000125834 0.08
ENSG00000197818 0.64
ENSG00000125831 1
ENSG00000215443 1
ENSG00000222008 0.06
ENSG00000101444 0.73
ENSG00000101333 0.22
… …tensofthousandsmoretags…
TagID A1 A2 A3 B1 B2 B2ENSG00000124208 478 619 559 4830 7165 6651ENSG00000182463 27 20 18 48 55 36ENSG00000125835 132 290 450 560 408 266ENSG00000125834 320 462 355 131 99 91ENSG00000197818 21 29 23 52 44 65ENSG00000125831 0 0 0 0 0 0ENSG00000215443 4 4 4 9 7 3ENSG00000222008 30 23 23 0 0 0ENSG00000101444 46 63 55 54 53 52ENSG00000101333 2256 2793 2931 2702 2976 2226
… …tensofthousandsmoretags…
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
RNA-seqanalysisstepsRawsequencereads
Mapontogenome
Summarizereadstotranscripts
Sta6s6caltes6ng:Determinedifferen6allyexpressedgenes
Systemsbiology
DenovoassemblyAnnota6onbased Genomeguidedassembly
Whichtranscriptome?
Learnsomething!
MovingbeyondDifferen6alExpression
Findingfusiongenesincancer
• Genomicsbreaksandrearrangementscanleadtogenefusions• Somefusionsareoncogenic,otherarejustbystanders.• Iden6fyingoncogenicgenefusionsisbeneficialfor
• ClinicalTreatmentse.g.• BCR-ABLfusionin95%ofCMLsImaAnib
• Transcriptomesofanyorganismscanbesequencedandanalysed
Non-modelorganisms
Blackwidowvenomtranscriptome
Desertpoplartranscriptomeinsalinecondi6ons
Exploringthehumantranscriptome
Mostgeneshavemorethanoneexpressedisoform Mostgenehaveonemajorisoform
Unknownandrareeventsinthehumantranscriptome
• ENCODEsays–75%ofthegenomeistranscribed
Non-canonicalRNAstructuresCircularRNAs
Intronreten6on
Memczaketal.Nature,2013
Wongetal.Cell,2013
Thefuture
• Analysismethodologyiscri6calands6lldevelopingforspecificpurposes
• Effec6velydealingwithnogenomeorpoorqualitygenomesorcancer
• Integra6ngRNA-seqdatawithothergenomicsdata
• Opportuni6estousethisdatainnewandimagina6veways–requiresnewanalysismethodology
