introduction to chromatin ip – sequencing (chip-seq) data ...€¦ · • chip-seq quality...

IntroductiontoChromatinIP–sequencing(ChIP-seq)dataanalysis

Stockholm,7November2018

AgataSmialowskaNBIS,SciLifeLab,StockholmUniversity

WorkshoponChIP-seqdataanalysis

ChromatinstateandgeneexpressionPEVPositioneffectvariegationinDrosophilaeye(nature.com)

Juxtapositionofeyecolourgeneswithheterochromatinresultsinthe“mottled”eyecolouration(redandwhite).

Proteins,whichbindheterochromatin,actto“spread”thesilencingsignalbyprovidingaforwardfeedbackloop.

HeterochromatinProtein1;HistonemethyltransferaseSu(var)3-9;H3K9methylation

FirstobservedbyH.Muller1930

www.pollev.com/AGATASMIALOW506

Chromatinimmunoprecipitation

RnDsystems

ApplicationsGeneraltranscriptionmachinery

Applications

Promoter-associatedtranscriptionfactors

Applications

Distalenhancers

Applications

Histonemodificationsandvariants Activationstates

Co-factors

ChIP-seqworkflow

Liu,PottandHuss,BMCBiology2010

designstudyobtaininputchromatinperformprecipitationconstructlibrarysequencelibrarybioinformaticanalysis

WorkflowofaChIP-seqstudy

Wetlab

Criticalfactors

•  Antibodyselection•  Propercontrolsample(inputchromatinormockIP)•  Librarycloningandsequencing•  Algorithmforpeakdetection

•  Enoughmaterialandbiologicalreplicates•  Reproducibilityinchromatinfragmentation•  Cross-linkerchoice

Experimentdesign

•  Soundexperimentaldesign:replication,randomisationandblocking(R.A.Fisher,1935)

•  Intheabsenceofaproperdesign,itisessentiallyimpossibletopartitionbiologicalvariationfromtechnicalvariation

•  Sequencingdepth:dependsonthestructureofthesignal;cannotbelinearlyscaledtogenomesize

•  Single-vs.paired-endreads:PEimprovesreadmappingconfidenceandgivesadirectmeasureoffragmentsize,whichotherwisehastobemodelledorestimated

Idealdesign:EachsamplehasamatchedinputInputsequencedtoacomparabledepthasIPsample

input library/sequencing

XChIP

replicates

input library/sequencingChIP

replicates

✓input library/sequencing

ChIPreplicates

under-sequencedinput

ChIP

well-sequencedinput

ChIP

X

Experimentdesign

Biologicalreplicatesandrandomisation

technicalreplicatesaregenerallyawasteoftimeandmoney

sample

libraries sequencing

X

✓time------->

experiment1 experiment2 Experiment3… libraries,sequencing,etc

manystudiesdonotaccountforbatcheffectsi.  timeii.  Origin

replicates libraries sequencing

origin

samples

experimentX

≥2biologicalreplicatesforsiteidentification≥3biologicalreplicatesfordifferentialbinding

pooleddata

under-sequenceddata

X

ifyouneedtopoolyourdata,thenitisunder-sequenced

pooleddata actualreplicates

✓

Importanceofsequencingdepth

Sequencingdepthdependsondatatype

TF:20M

point-source mixedsignal broadsignal

Noclearguidelinesformixedandbroadtypeofpeaks

TranscriptionFactors

ChromatinRemodellers

Histonemarks


Histonemarks

RNApolymeraseII

Human: ? ?

H3K4me3:25M H3K36me3:35M H3K27me3:40M

H3K9me3:>55M

Source:TheENCODEconsortium;Jungetal,NAR2014

•  ChIP–sequencing:introductionfromabioinformaticspointofview

•  PrinciplesofanalysisofChIP-seqdata

•  ChIP-seq:downstreamanalyses

•  Resources

Chromatin=DNA+proteins

Park,NatureRevGenetics,2009

Dataanalysis

WorkflowofaChIP-seqstudy

Iterativeprocess

Wetlab

designstudyobtaininputchromatinperformprecipitationconstructlibrarysequencelibrarylibraryqualitycontrolfiltersequencesalignsequencesfilteralignmentsidentifypeaks/regionsofenrichmentassessdataqualityunderstandthedata/resultsdownstreamanalyses




•  Resources

Twoquestionstoaddress

•  1.DidtheChIPpartoftheChIP-seqexperimentwork?Wastheenrichmentsuccessful?

•  2.Wherearethebindingsites(oftheproteinofinterest)?

Wordofcaution!

ChIP-seqexperimentsaremoreunpredictablethanRNA-seq!Errorsources:chromatinstructurePCRover-amplificationnon-specificantibodyotherthings?

ChIP-seqQC:didtheChIPwork?

•  1.Inspectthesignal(mappedreads,coverageprofiles)ingenomebrowser

•  2.Computepeak-independentqualitymetrics(crosscorrelation,cumulativeenrichment)

•  3.Assessreplicateconsistency(correlationsbetweenreplicatesofthesamecondition)

tagdensitydistributionreproducibilitysimilarityofcoveragesignalatknownsites…SpottinginconsistenciesConfoundingfactorsUnder-sequencedlibraries…

HowdoIknowmydataisofgoodquality?

Marinovetal,G32013

Librarycomplexity

Sequenceduplicationlevel>80%(lowcomplexitylibrary)

Qualitycontrol:taguniqueness–librarycomplexitymetric

NRF:Non-redundantfraction(ofreads):proportionofuniquetags/total

FastQCBabrahamInstitute

HowdoIknowmydataisofgoodquality?

Marinovetal,G32013

Objective(i.e.peakindependent)metricstoquantifyenrichmentinChIP-seq;forTFinmammaliansystems:NormalisedStrandCorrelationNSCRelativeStrandCorrelationRSC

Large-scalequalityanalysisofpublishedChIP-seqdatasets:20%lowquality25%intermediatequality30%inputshavemetricssimilartoIPs

Strandcross-correlation

Carrolletal,FrontGenet2014

Thecorrelationbetweensignalofthe5ʹendofreadsonthe(+)and(-)strandsisassessedaftersuccessiveshiftsofthereadsonthe(+)strandandthepointofmaximumcorrelationbetweenthetwostrandsisusedasanestimationoffragmentlength.

Strandshift

Crossc

orrelatio

n

Strandcross-correlation


NSC=MaxCCvalue(fLen)

MinCCRSC=

MaxCC–MinCC

PhantomCC–MinCC

Cross-correlationplots

−500 0 500 1000 1500

0.20

00.

205

0.21

00.

215

0.22

00.

225

strand−shift (105,455)

cros

s−co

rrela

tion

ENCFF000OWMed.sorted.1.bam.picard.bam

NSC=1.14102,RSC=1.06452,Qtag=1

−500 0 500 1000 1500

0.28

60.

288

0.29

00.

292

0.29

40.

296

0.29

80.

300

strand−shift (100,265,245)

cros

s−co

rrela

tion

ENCFF000PET.sorted.1.bam.picard.bam

NSC=1.01443,RSC=0.289702,Qtag=−1

−500 0 500 1000 1500

0.19

0.20

0.21

0.22

0.23

strand−shift (130)

cros

s−co

rrela

tion

ENCFF000PMG.sorted.1.bam

NSC=1.28071,RSC=0.987276,Qtag=0

−500 0 500 1000 15000.25

0.26

0.27

0.28

0.29

0.30


cros

s−co

rrela

tion

ENCFF000PMJ.sorted.1.bam

NSC=1.21367,RSC=1.39752,Qtag=1

−500 0 500 1000 1500

0.27

40.

275

0.27

60.

277

0.27

8

strand−shift (90,200,210)

cros

s−co

rrela

tion

ENCFF000PON.sorted.1.bam.picard.bam

NSC=1.0166,RSC=0.92739,Qtag=0

Verygoodenrichment

Acceptableenrichment Poorenrichment,

possiblyundersequenced

NoclusteringGoodinput

ReadclusteringBadinput

Input

ChIP

Cumulativeenrichmentaka“Fingerprint”isanothermetricforsuccessfulenrichment

http://deeptools.readthedocs.orgDiazetal,GenomeBiol2012

Park,NatureRevGenetics,2009

Peakcalling

appropriatemethodologiesdependondatatype

SPPMACS2

punctate mixedsignal broadsignal

- -

Thisisanactiveareaofalgorithmdevelopment

TranscriptionFactors


Histonemarks


Histonemarks

RNApolymeraseII

MACS2inbroadmode,windowsapproaches

Principleofpeakdetection

SymmetryinreadsmappedtooppositeDNAstrands

Computationofenrichmentmodel

Pepke,2009

Point-sourcevs.broadpeakdetection

Wilbanks2010

Sequence-specificbinding(TFs) Distributedbinding(histones,RNApol2)

Comparisonofpeakcallingalgorithms

Wilbanks2010

Peakoverlap(Hoetal,2012)

>50%

20%

“Hyper-chippable”regions


DER–DukeExcludedRegions(11repeatclasses)UHS–UltraHighSignal(openchromatin)DAC–consensusexcludedregions

ReadsmappedtotheseregionsshouldbefilteredoutpriortopeakcallingTracksavailablefromUCSCforhuman,mouse,flyandworm

Qualityconsiderations

•  ChIP-seqqualityguidelinesfromtheENCODEproject(Relativestrandcross-correlation,Irreproduciblediscoveryrate)

•  Antibodyvalidation•  Appropriatesequencingdepth(dependingongenomesizeand

peaktype).Forhumangenomeandbroad-sourcepeaks,min.40-50Mreadsisrequired.

•  Experimentalreplication•  Fractionofreadsinpeaks(FRiP)>1%•  Crosscorrelation(correlationofthedensityofsequencesalignedto

oppositeDNAstrandsaftershiftingbythefragmentsize)•  Experimentalverificationofknownbindingsites(andsitesnot

boundasnegativecontrols)

ChIP-exo:improvementinbindingsiteidentification

RheeandPugh,Cell2011

Otherfunctionalgenomicstechniques

Cliffordetal,NatureRevGenet,2014




•  Resources

ChIPseqdownstreamanalyses

•  Validation(wetlab)

•  Downstreamanalysis–  Motifdiscovery–  Annotation–  Integrationofbindingandexpressiondata–  Integrationofvariousbindingdatasets–  Differentialbinding

Signalvisualisationandinterpretation

BindingprofileofaTFinrelationtothetranscriptionstartsite

deepToolsngsplotsseqMiner

•  Clustering•  Heatmaps•  Profiles•  Comparisonof

differentdatasets




•  Resources

Furtherreading

•  ImpactofartifactremovalonChIPqualitymetricsinChIP-seqandChIP-exodata.Carroletal,Front.Genet.2014

•  ImpactofsequencingdepthinChIP-seqexperiments.Jungetal,NAR2014

•  ChIP-seqguidelinesandpracticesoftheENCODEandmodENCODEconsortia.Landtetal,GenomeRes.2012

•  http://genome.ucsc.edu/ENCODE/qualityMetrics.html#definitions

•  https://www.encodeproject.org/data-standards

BioconductorChIP-seqresources•  Generalpurposetools:

–  Rsubread(readmapping;notidealforglobalalignment)–  Rbowtie(globalalignment)–  GenomicRanges(toolsformanipulatingrangedata)–  Rsamtools(SAM/BAMsupport)–  htSeqTools(toolsforNGSdata;post-alignmentQC)–  chipseq(utilitiesforChIP-seqanalysis)

•  Peakcalling–  SPP–  BayesPeak(HMMandBayesianstatistics)–  MOSAiCS(model-basedoneandtwoSampleAnalysisandInferenceforChIP-Seq)–  iSeq(HiddenIsingmodels)–  ChIPseqR(developedtoanalysenucleosomepositioningdata)–  Csaw(apipelineforChIP-seqanalysis,includingstatisticalanalysisofdifferentialoccupancy)

•  Qualitycontrol–  ChIPQC

•  Differentialoccupancy–  edgeR–  DESeq2–  DiffBind(compatiblewithobjectsusedforChIPQC,wrapperforDESeqandedgeRDEfunctions)

•  PeakAnnotation–  ChIPpeakAnno(annotatingpeakswithgenomecontextinformation)–  ChIPSeeker(functionalannotationofpeaks)

TheEpigenomicsRoadmapProject

http://www.roadmapepigenomics.org/•  Referencehumanepigenomes•  DNAmethylation,histonemodifications,chromatin

accessibilityandsmallRNAtranscripts•  Stemcellsandprimaryexvivotissues•  111tissueandcelltypes•  2,804genome-widedatasets

Questions?

[email protected]




•  Resources

•  Exerciseoverview

Exercise•  1.Qualitycontrol•  2.Readpreprocessing•  3.Peakcalling•  4.Exploratoryanalysis(sampleclustering)•  5.Visualisation

DidmyChIPwork?

Cross-correlation Cumulativeenrichment

−500 0 500 1000 1500

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24


cros

s−co

rrela

tion

ENCFF000PED.chr12.bam

NSC=2.50193,RSC=1.87725,Qtag=2

Exploratoryanalysis

Clusteringoflibrariesbyreadsmappedinbins,genome–wide(spearman)

Clusteringoflibrariesbyreadsmappedinpeaks(pearson)

HeLa

Sknsh&

HepG

2ne

ural

HepG

2

neural

SknshHeLa

HepG2

I

Ch

Ch

I

I

Ch

BindingprofilearoundTSS

That’sallfornow,

timetodosomehands-onwork

Libraryqualitycontrolandpreprocessing

•  FastQC/Prinseq

•  Trimadaptersifanyadaptersequencesarepresentinthereads(asdeterminedbytheQC)

•  Insomecases,you’llobservek-merenrichment(especiallyifthedataisChIP-exo,anewvariationofChIP-seq)–itisnotnecessarilyabadthing,ifsequenceduplicationlevelsarelow;howeveritmayindicatelowcomplexityofthelibrary–awarningsignthattheenrichmentinChIPwasnotsuccessfulorthelibrariesareover-amplified(oftenthelatteristheconsequenceoftheformer)

Mappingreadstothereferencegenome

•  Choosetherightreference:assemblyversion(notalwaysthenewestisbest)andtype(primaryassembly,orassemblyfromindividualchromosomesequences+non-chromosomalcontigs;notthetoplevelassembly);choosethematchingannotationfile(GTF,GFF)

•  Readmapping:globalalignment•  Mappers(=aligners):Bowtie,BWA,BBMap,Novoalign,…(lotsoftoolsare

available)

•  Visualisedataingenomebrowser–  BAMfilesortracks(wig,bedgraph,bigWig)–  Local(IGV)orweb-based(UCSCgenomebrowser)–  Dataqualityassessment

Cross-correlationprofiles,RSCandNSC

•  Metricstoquantifythefragmentlengthsignalandtheratiooffragmentlengthsignaltoreadlengthsignal

•  RelativeCrossCorrelation(RSC)-ChIPtoartifactsignal

•  NormalisedCrossCorrelation(NSC)

•  TFs:fragmentlengthsareoftengreaterthanthesizeoftheDNAbindingevent,thedistinctclusteringof(+)and(-)readsaroundthissiteisveryapparent

•  NSC>1.1(highervaluesindicatemoreenrichment;1=noenrichment)•  RSC>0.8(0=nosignal;<1lowqualityChIP;>1highenrichment•  Broadpeaks:thisclusteringmaybemorediffuse(fragmentlength<peak)

CC(Fragmentlength)min(CC)

CC(Fragmentlength)-min(CC)CC(readlength)–min(CC)

introduction to chromatin ip – sequencing (chip-seq) data ...€¦ · • chip-seq quality...

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