Canadian Bioinformacs Workshops 4.pdf · 2018. 11. 21. · 6/16/16 2 Module 4: Downstream analyses...

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CanadianBioinforma1csWorkshops

www.bioinforma1cs.ca

2 Module #: Title of Module

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Animagetorepresentyourworkshopormodule

Module4Downstreamanalyses&integra9ve

toolsDavidBujold

EpigenomicDataAnalysisJune20–June21,2016

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Module4:Downstreamanalyses&integra6vetools bioinformatics.ca

LearningObjec6ves•  Exploresomedownstreamanalysesthatcanbedonewith

epigenomicassaysdata•  Discoversourcesofpubliclyavailabledatasetsthatcanbe

usedinanyone’sprojects•  Learnaboutonlineportalsandtoolsthatcanease

epigenomicsdataanalysis

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•  Over98%ofthehumangenomedoesnotencodeproteinsequences

•  76%ofthegenomegetstranscribed•  Nearlyhalfofthegenomeisaccessibleinsomewaytogene9cregulatoryproteinssuchastranscrip9onfactors

•  PuSngincontexttheinforma9onwecanobtainonvariants,DNAmethyla9on,histonemodifica9ons,transcrip9ontoRNA,chroma9naccessibility,etc.willeaseourunderstandingoftheunderlyingbiology

Mo6va6onforepigenomicintegra6veanalysis

(1) Elgar, G., & Vavouri, T. (2008). Tuning in to the signals: noncoding sequence conservation in vertebrate genomes. Trends in genetics, 24(7), 344-352. (2) Pennisi, E. (6 September 2012). "ENCODE Project Writes Eulogy for Junk DNA". Science 337 (6099): 1159–1161.

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ModuleOutline1. Downstreamfunc9onalanalysistools2. Workingwithpublicdatasets3. Qualitycontrolforonlineresources4. Onlinevisualiza9onandanalysistools

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1-Downstreamfunc6onalanalysistools


Downstreamfunc6onalanalysis•  Onceprimaryanalysisisdoneforourepigenomicassay,wehave:

– AsetofpeakscallsforChIP-Seqassays– Methyla9onlevelsatCpGsitesforWGB-Seqassays

•  Next,wecanusethisdatatorunsomefunc9onalanalysesbycomparing:

– Differentregionsfromthesamedataset– Mul9plesamplesofthesamegroup– Differentgroups

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Differen6allymethylatedsites

Bock C. Analysing and interpreting DNA methylation data. Nat Rev Genet. 2012 Oct;13(10):705-19.


Methyla6ondownstreamanalysis•  Iden9fyingdifferen9allymethylatedregions(DMR)acrosssamplegroups(celltypes,diseasestatus,etc.)

•  Iden9fyingregionsofthegenomewithdifferentmethyla9onpaderns

Roadmap Epigenomics Consortium et al. Nature 518, 317-330 (2015) doi:10.1038/nature14248

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(1) D'haeseleer, Patrik. "What are DNA sequence motifs?." Nature biotechnology 24.4 (2006): 423-425.

Whataremo6fs?•  Short,recurringpadernsinDNAthatarepresumedtohaveabiologicalfunc9on

•  Oeenindicatesequence-specificbindingsitesforproteinssuchasnucleasesandtranscrip9onfactors(TF)

•  Inthisexample,ifallowing1basemismatch,therearetwomo9fs:TTGACAandGCATC:

Example from http://slideplayer.com/slide/8679835/

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Exploringmo6fsinChIP-seqpeaks•  Usingregionspreviouslylabeledaspeaks,wecantrytoiden9fymo9fs

•  Iden9fyingtranscrip9onfactorbindingsites(TFBS)ishelpfultounderstandregulatorynetworkstranscrip9onmechanisms

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HOMER•  Triestoiden9fyregulatoryelementsenrichedinonesetofsequencescomparedtoanother

• Mo9fdiscoveryalgorithmdesignedforregulatoryelementanalysisingenomicsapplica9ons(DNAsequencesonly)

– Knownmo9fscoun9ng– Denovomo9fsiden9fica9on– Ademptstomatchdenovomo9fstoknownones


HOMER•  findMo9fsGenome.plademptstoiden9fymo9fsinaprovidedlistofgenomicregions

•  Input:– BEDfilecontainingtheregions(peaksfile)

•  Column1:chromosome•  Column2:star9ngposi9on•  Column3:endingposi9on•  Column4:UniquePeakID•  Column5:notused•  Column6:Strand(+/-or0/1,where0="+",1="-")

– Referencegenomeassembly

– Size:fragmentsizetouseformo9ffinding

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HOMER-Execu6onsteps1. Verifypeak/BEDfile2. Extractsequencesfromthegenomecorrespondingtotheregionsintheinputfile

3. CalculateGC/CpGcontentofpeaksequences4. Preparsethegenomicsequencesoftheselectedsizetoserveasbackgroundsequences

5. Randomlyselectbackgroundregionsformo9fdiscovery6. Autonormaliza9onofsequencebias7. Checkenrichmentofknownmo9fs8. denovomo9ffinding


HOMER•  Amonggeneratedresultfiles,twoHTML-formadedreportswillbeavailable:

– homerResults.html:formadedoutputofdenovomo9ffinding–  knownResults.html:formadedoutputofknownmo9ffinding

http://homer.salk.edu/homer/ngs/peakMotifs.html

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LookingforsignificantGOenrichment• WecanlookatbiologicalsignificanceofourpeaksusingGeneOntologies(GO)termsgenomeannota9ons

– GO:Setofstructured,controlledvocabulariesforcommunityuseinannota9nggenes,geneproductsandsequences

•  Populartool:theGenomicRegionsEnrichmentofAnnota9onsTool(GREAT)

http://bejerano.stanford.edu/great/public/html/index.php

(1) Gene Ontology Consortium. "The gene ontology project in 2008." Nucleic acids research 36.suppl 1 (2008): D440-D444.

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GREAT:Cis-regulatoryregionsfunc6onspredic6on

•  Bindingsitesareoeennotlocatedintheproximalregionofthegeneofinterest

•  GREATlooksbeyondthisproximalregion

McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, Wenger AM, Bejerano G. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010 May;28(5):495-501.

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GREAT:Cis-regulatoryregionsfunc6onspredic6on

McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, Wenger AM, Bejerano G. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010 May;28(5):495-501.


GREAT•  Input:BEDfilewithregionsofinterest•  Output:MatchingGOtermsforMolecularFunc9ons,BiologicalProcesses,Phenotypes,Diseases,etc.

•  ExamplewithH3K27acpeaksfrombonemarrowsample:

http://bejerano.stanford.edu/great/public/html/index.php

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LinkingGWASvariantstoChIP-Seqdata



Integra6veanalysiswithRoadmapdata


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2-Workingwithpublicdatasets


Workingwithpublicdatasets

• Manylargeconsor9aofferdatasetsformul9ple9ssues/diseases/condi9ons

•  Thesearefreeresourcestodomorewithcompara9vestudies

•  Publicdatasetsoffernocontroloverhowassaysweredone,andwhatinforma9onisavailable

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RoadmapEpigenomicsProject

http://www.roadmapepigenomics.org/


ENCODEConsor6um

https://www.encodeproject.org/

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IHEC•  Nowadays,themostrecentandcompleteresourceisIHEC,theInterna9onalHumanEpigenomeConsor9um

•  Interna9onaleffortwithseveralfundingagencies

http://ihec-epigenomes.org/


WhatisIHEC•  Goal:Providingstandardizedreferenceepigenomesforavarietyofnormalanddisease9ssues

–  Membergroupstakepartincommideesworkingonstandards(assays,data/metadatadistribu9on,ethics…)

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IHECDataPortal•  Goal:IntegrateepigenomicpublicdatasetsproducedwithintheInterna9onalHumanEpigenomeConsor9um

– Rawdataisincontrolledaccessrepositories

•  AsofApril2016:– over7,000humandatasets– datasetsfrom7consor9a,otherscoming

•  Offerstoolsfordatasetsdiscovery,visualiza9onandpre-analysis


Publiclyaccessibledatasets•  DatasetsmadeavailableintheIHECDataPortalispubliclyaccessibleforeveryone’sownresearch

•  Humandataofferedbysuchconsor9ausuallyfallsinoneoftwocategories:

– Controlledaccessdata•  Rawdatafromsequencers•  Clinical/sensi9veinforma9onsuchasphenotypes•  ArchivedatrepositoriessuchasEGAanddbGaP

– Publicdata•  Annota9ontracks,touseintoolssuchasUCSCGenomeBrowser,EnsemblandIGV.

•  Somedonor,sampleandlibrarymetadata•  Freelydownloadable

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3-Qualitycontrolforonlineresources


Qualitycontrolonepigenomicsdatasets•  Datasetsobtainedonlineareofvariablelevelsofquality•  Qualityofdownloadeddatasetsmustbeassessed•  Examplesofqualitycontroltests:

– Rawdata:FastQC– Signal:

•  Signal-to-noisera9o•  ChromImpute•  Whole-genomesignalcorrela9onacrosstracks

•  SomeQCtoolsareavailableasonlineresources–  IHECDataPortalincludessomepreliminaryqualitycontroltests,suchasPearsonCorrela9ontestoverwholetracksignal

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ChromImpute•  Allowsimpu9ngmissingsignaltracks•  Toimputeasampleforamark,usestrainingdata:

–  fromothersampleswiththesamemark–  fromtheothermarksforthegivensample

Ernst J, Kellis M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nature Biotechnology, 33:364-376, 2015.


Ernst J, Kellis M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nature Biotechnology, 33:364-376, 2015.

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4-Onlinevisualiza6onandanalysistools


Onlinevisualiza6onandanalysistools• Manyaddi9onalresourcesareusefulforvisualizingandmanipula9ngdatasets

•  Inthissec9on,wewillcoverafew:–  IHECDataPortal– UCSCGenomeBrowser– WashUEpigenomeBrowser– Galaxy

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IHECDataPortal-Overview


IHECDataPortal-DataGrid

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IHECDataPortal-DatasetsCorrela6on


IHECDataPortal-DatasetsCorrela6on

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IHECDataPortal-Download


IHECDataPortal-ComingSoon•  Comprehensivefilteringbasedonavailablemetadata• Metadataextrac9onfeatureinhuman-readableandmachine-readableformats

•  Centralizeddataserving•  Linkstopermanentsessions,foreasierci9ngandshareability

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VisualizingtrackswiththeUCSCGenomeBrowser


UCSCGenomeBrowserTrackHubs•  TrackscanbeaggregatedusingatextdocumentintheUCSCGenomeBrowsertrackhubformat

•  Advantage:Canbeeasilydistributedtocollaborators/usersofyourresources

•  Inconvenient:Needtogeneratethistextdocument

•  Documenta9on:– hdps://genome.ucsc.edu/goldenpath/help/hgTrackHubHelp.html

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Smalltrackhubexample

track McGill_MS000101_monocyte_RNASeq_signal_forward type bigWig bigDataUrl http://epigenomesportal.ca/public_data/MS000101.monocyte.RNASeq.signal_forward.bigWig shortLabel 000101mono.rna longLabel MS000101 | human | monocyte | RNA-Seq | signal_forward track McGill_MS000101_monocyte_RNASeq_signal_reverse type bigWig bigDataUrl http://epigenomesportal.ca/public_data/MS000101.monocyte.RNASeq.signal_reverse.bigWig shortLabel 000101mono.rna longLabel MS000101 | human | monocyte | RNA-Seq | signal_reverse

•  Minimumproper9esforatrack:–  track:Symbolicnameofthetrack–  type:Oneofthesupportedformats

•  bigWig,bigBed,bigGenePred,bam,halSnake,vcfTabix– bigDataUrl:Webloca9on(URL)ofthedatafile–  shortLabel:Shorttrackdescrip9on(Max17characters)–  longLabel:Longertrackdescrip9on(displayedovertracksinthebrowser)


WashUEpigenomeBrowser•  SupportsmanytracktypesincludedintheUCSCBrowser

– BigBedsareontheway– CanalsoloadUCSCtrackhubdocuments

http://epigenomegateway.wustl.edu/browser/

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Galaxy• Web-basedframeworkofferingauser-friendlyinterfacemappingtomostpopularbioinforma9cstools

–  "Dataintensivebiologyforeveryone."

•  Allowsforreproducibleresults

– Steps/parameterskeptinhistory


GalaxyInterface• ManytoolscoveredinthisworkshopareavailableinGalaxy

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Galaxy-Pipelinedesign•  Abilitytodesigncustompipelinesandimportothers’

– Allthroughauser-friendlyGUI

•  Tailoredforsmall/mediumscaleprojectswithnottoomanysamples


GenAP

•  GenAPisaCanadiancompu9ngplarormforlifescienceresearchers

•  LeveragesCANARIEhigh-speednetworkandComputeCanada(CC)HighPerformanceCompu9ng

•  Userscancreatetheirownprivate,fullyconfiguredGalaxyandruntheiranalysesonComputeCanadaHPCs

•  FreeforCanadianacademia– AllyouneedistogetaComputeCanadaaccount

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GenAPPipelines•  Free,open-sourcesoewarewithPython• Manypipelinesavailable,suchasforepigenomics:

– RNA-Seq– RNA-SeqDenovo– ChIP-Seq– Methyla9onpipelinecomingsoon

•  Allsoewarerequirementsarepre-installedatmanyComputeCanadaHPCs


GenAPPipelineshdps://bitbucket.org/mugqic/mugqic_pipelines

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•  PrivateGalaxyinstance,sharablewithcollaborators•  Computejobsmakinguseofgroup’sCCalloca9on

– Fasterthanusegalaxy.org

GenAP-Galaxy


GenAPPortal•  LoginwithyourComputeCanadaaccount

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GenAPPortal•  You’re then readyto connect to thePortal


GenAPPortal-PreparingGalaxy•  Instan9a9ngaGalaxyapplica9onwithinGenAP

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Conclusion•  Inthisunit,wehavecovered:

– Sometypesofdownstreamanalyseswithepigenomicdata– Howtoobtainpubliclyaccessibledatasetsforyourownanalyses– Methodstoassessthequalityofpublicdata– Howtovisualizeepigenomicdatasetsusingonlinetools– Someonlineresourcestorunaddi9onalanalyseswithawebinterface

•  Thefollowingworkshopwillprovideanintroduc9ontosomeofthetoolspresentedintheseslides

•  Aeertheworkshop,ifyou’reinCanadianAcademia,getthatComputeCanada/GenAPaccount!☺

Canadian Bioinformacs Workshops 4.pdf · 2018. 11. 21. · 6/16/16 2 Module 4: Downstream analyses...

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Transcript of Canadian Bioinformacs Workshops 4.pdf · 2018. 11. 21. · 6/16/16 2 Module 4: Downstream analyses...