Tools and Algorithms in Bioinformatics• Unmapped Ids: Unidentified by the IPA database • Check for typos and try to map as many as possible • Analysis –ready molecules: Identified

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__________________________________________________________________________________________________Fall, 2017 GCBA/MGCB/BMI 815

Tools and Algorithms in BioinformaticsGCBA815/MCGB815/BMI815, Fall 2017

Week 11: IPA: Ingenuity Pathway Analysis

Babu Guda, PhDProfessor

Department of Genetics, Cell Biology and Anatomy

University of Nebraska Medical Center

(Some slides were borrowed from IPA Tutorial with permission)


• The leading commercial software for biological pathway and network analysis

• Ingenuity knowledge Base (expert curated information from the literature)

• Integrated with public databases (Entrez Gene, RefSeq, OMIM, UniProt, GO,

HMDB, GNF, KEGG, IntAct, BIND, Clinical trials, DrugBank, etc.)

• Major products

• Ingenuity Pathway Analysis (UNMC has a site license)

• IPA Variant Analysis: To identify variants from sequencing data

Ingenuity Softwarehttp://www.ingenuity.com

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TheIngenuityKnowledgeBase

TheIngenuityOntology

IngenuityFindingsIngenuity® Expert Findings – Manually curated Findings that are reviewed, from the full-text, rich with contextual details, and are derived from top journals.

Ingenuity® ExpertAssist Findings –Automated text Findings that are reviewed, from abstracts, timely, and cover a broad range of publications.

IngenuityModeledKnowledgeIngenuity® Expert Knowledge – Content we model such as pathways, toxicity lists, etc.

Ingenuity® Supported Third Party Information – Content areas include Protein-Protein, miRNA, biomarker, clinical trial information, and others

IngenuityContent• IngenuityKnowledgeBase

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HowcanIPAhelpyou?

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• IPA

– Deeppathwayunderstandingofasinglegene/protein• Drug/therapeutictargetdiscovery

– Biologicalunderstandingoflargedatasets,including• Transcriptomics– differentialgeneexpression(arrayandRNAseq)

– IsoProfiler:filterfortranscriptexpressionandannotationofinterest• Proteomics– differentialproteinexpression• Phosphoproteomics – differentialproteinphosphorylation• Geneswithloss/gain-of-functionvariants• Metabolomics• miRNAexpression• Methylation• GeneLists

– ChIP-Seq– siRNAscreening

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Different types of analyses

• Browsing the database• genes, molecules, diseases, pathways and functions using the IPA

knowledgebase• Core Analysis

• Gene expression changes, cellular processes• Upstream regulator analysis• Pathway analysis (signaling, metabolic, disease)• Interaction network analysis

• Explorative Analysis• Grow/build networks from seed molecules• Mechanistic network analysis

• Human Isoform view• Molecule activity predictor (MAP)

• Predicted cascading effects of altering one molecule in a pathway• Hypothesis generation

Identifykeycellularpathwaysmostlikelytobeaffected

• CanonicalPathwaysAnalysis

• Whichmetabolicandcellsignalingpathwaysshowasignificanceofenrichmentforagroupofgenes?

• Whatarethepredictedupstreamand/ordownstreameffectsofactivationorinhibitionofmoleculesinapathwaygivenmoleculeswith“known”activity?(MoleculeActivityPredictor)

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Upstream Analysis

• Use published experimental molecular interactions to identify upstream regulators

• Identify upstream regulators by determining gene enrichment in downstream genes

• Predict the activity state of regulators by correlating literature reported effects with observed gene expression

Identifylikelyupstreamregulatorsandtheiractivitystate

Createdenovopathwaysofregulatorsandgenes• UpstreamAnalysis– MechanisticNetworks

• Identify potential upstream regulator signal transduction

• Using shared downstream gene effects and gene-gene interactions, pathways (mechanistic networks) are created.

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Visualizeandpredictthebiologicalimpactofgeneexpressionchanges

• Diseases&Functions

• Identifykeybiologicalprocessesinfluencedbydifferentiallyexpressedgenes

• Understandwhethercellularprocessesarebeingdrivenupordownbycorrelatingobservedexpressionwithreportedexperimentalgeneeffects

• Each box represents a biological process or disease• The size of the box represents gene enrichment• The color of the box indicates the predicted increase or

decrease

Connectupstream,downstreamanalyses

• Hypothesisforhowaphenotype,functionordiseaseisregulatedinthedatasetbyactivatedorinhibitedupstreamregulators

• Explainimpactofupstreammoleculesondownstreambiology• Explainpotentialmechanismforaphenotypeordrug• Definedrugtargets• Discovernovel(orconfirmknown)regulatorà disease/phenotype/function

relationships

• RegulatorEffects

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Discoveradditionalinterconnectivitywithinyourdata

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Networks

• To show as many interactions between user-specified molecules in a given data set and how they might work together at the molecular level

• Highly-interconnected networks are likely to represent significant biological function


• Search feature against the entire IPA Knowledgebase

• Molecule pages sorted by species (human/mouse)

• Directly access PubMed records for each annotation

• Share your analysis results with other IPA registered users

• Can also share only part of the result

• Import external pathways into IPA (in SBML format)

• Use Path Designer to edit pathway/network diagrams

• MicroRNA target filter

• Biomarker analysis

• Export data and images for reports/publications

Some special features of IPA

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After Login – Landing page


• Data in a structured format (excel, tab-delimited, etc.)

• Limit the header to only the first row

• Keep the ‘flexible format’ option (default)

• Identifier type: Automatic, but change if inappropriate

• Select only the columns that you want to upload and ignore the rest

• Select appropriate units like fold change, z-score, p-value, etc.

• Prompts you to create a new project; all your analysis results will be

stored in this project folder, which can be accessed from the left panel

• General default settings are good unless you want to customize

• Very detailed step-by-step directions are available at

• http://ingenuity.force.com/ipa/IPATutorials?id=kA150000000TPy1#

Upload the data

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WhatgetsuploadedtoIPA?

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• IPACoreAnalysisinput– RNA-seq,microarray,miRNA,proteomic,genomic,SNP,ormetabolicdata– Measurementcalculations(e.g.differentialexpressionandsignificance)are

madeoutsideofIPApriortoupload

– Observation– foragivenexperimentalcondition…• Alistofmoleculeidentifiers(gene,protein,etc.)• Correspondingmeasurementvalues(foldchange,p-value,etc.)

– Single-observationdatasets– oneexperimentalcomparison• Casevs.control• Mutantvs.wild-type• Treatedvs.untreated

– Multiple-observationdatasets– morethanoneexperimentalcondition• Atimecourseexperimentwithmultipletimepoints• Doseresponseexperimentwithmultipledoses• Measurementofmultiplecelltypesordiseasesubtypes

Datauploadformatexamples

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ID(required)

IdentifierExamplesArrayIDsdbSNPEnsemblEntrez GeneGenBankIPIKEGGPubChemRefSeqUniProt…

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ID(required)


DirectionalComparisonsExprRatioExprFoldChangeExprLogRatioVariantLoss/GainPhospho RatioPhospho FoldChangePhospho LogRatio

OtherMeasurementsExprp-value

ExprFDR(q-value)ExprIntensity/RPKM/FPKMVariantACMGClassification

Phospho p-valuePhospho FDR(q-value)

Phospho IntensityPhospho Site

Measurements(recommended)


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ID(required)


DirectionalComparisonsExprRatioExprFoldChangeExprLogRatioVariantLoss/GainPhospho RatioPhospho FoldChangePhospho LogRatio

OtherMeasurementsExprp-value

ExprFDR(q-value)ExprIntensity/RPKM/FPKMVariantACMGClassification

Phospho p-valuePhospho FDR(q-value)

Phospho IntensityPhospho Site

Measurements(recommended) AdditionalObservations(optional)

ExperimentalComparisonExamplesMutantvs.wild-typeTreatedvs.untreatedOthercasevs.control

AdditionaltimepointsMultipledoseresponses

Variouscelllines

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DataUpload

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• Bestpractices– CalculatemetricsoutsideofIPA(e.g.fold-change,p-value)– CreateanExcelspreadsheetortabdelimitedfile

• Only1headerrowallowed• Onecolumnmusthaveidentifiers,preferablytheleft-mostcolumn• IPAwillonlylookatthetopworksheetinanExcelworkbook

– Grouprelatedobservationsintoasinglespreadsheetifpossible• Timecourse,drugconcentration,celllines,etc.• Canhaveupto20observations

– Specifyarrayplatform(chip)ifpossible• ItisOKtouse“Notspecified/applicable”

– Pre-filterdataatthelowestthresholdthatyouhaveconfidencein• Forexample,probemeasurementp-valueof.05orothercriteria• FurtherfilterinCoreAnalysis


• Mapped Ids: Identified by the IPA database

• Unmapped Ids: Unidentified by the IPA database

• Check for typos and try to map as many as possible

• Analysis –ready molecules: Identified and also contain

information in the database

• Run the analysis for individual observations

• If more than one observation is selected, analysis is run

independently for each and saved under separate names

such as control, wildtype, treated, untreated, etc.

Running Core Analysis

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CoreAnalysisSteps

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1. LaunchCoreAnalysis:File>New>CoreAnalysis

2. UploadData(geneexpression,proteinexpression,metabolomics,etc.)

3. SetCoreAnalysisSettingsandRunAnalysis

4. InterpretResults

ExpressionValueCalculation

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• Verifythedifferentialexpressioncalculations

– Ratiodifferentialexpression

– Log2(ratio)differentialexpression(recommended)

– FoldChange• Ifincreaseddifferentialexpression

• Ifdecreaseddifferentialexpression

Foldchangewillneverhavevaluesbetween-1and1

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Gene/ProteinExpressionAnalysis,Overview

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• IPACoreAnalysis

– PathwayAnalysis• Identifiesenrichedcanonicalpathwaysandscoresdirectionalchangesbasedongeneexpression

– UpstreamRegulatorAnalysis• Predictswhatregulatorscausedchangesingeneexpressionandthedirectionalstateofregulator

– DiseasesandFunctionsAnalysis• Predictseffectedbiology(cellularprocesses,biologicalfunctions)basedongeneexpressionandpredictsdirectionalchangeonthateffect

– RegulatorEffects• Modelspathwayinteractionsfrompredictedupstreamregulators,throughdifferentiallyexpressedgenes,tobiologicalprocesses

– Networks• Predictsnon-directionalgeneinteractionmap

CanonicalPathwayZ-scores

– AllowsyoutoquicklydetermineifCanonicalPathways,includingfunctionalend-points,areincreasedordecreasedbasedondifferentiallyexpressedgenesorproteinsinyourdataset

– Certainpathwayswithintheknowledgebasearedirectional(proceedfrom“A”to“Z”)

– Aspartofpathwaycuration,asubsetofgenesareselectedtobeactive• Allowsthedirectionalityofothergenestobepredicted• Resultdefinesan“activated”stateforagivenpathway

– Z-scoresarecalculatedbasedonthedataset’scorrelationwiththeactivatedstate

• Pathwayactivityanalysis

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PathwayActivityAnalysis

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Expected Activation State(Knowledge Base)

Positive Z-score(Example Data)

Negative Z-score(Example Data)

IPAUpstreamRegulatorAnalysis• DirectionalEffects:MoleculeActivityPredictor

ExamineExpressionRelationshipConsistency

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• Grow: Connected molecules for a selected molecule can be grown

based on the known connections in the literature

• Trim: Connections can be removed from a larger network based on

certain criteria

• Connect: Two sets of unconnected genes can be connected via the

shortest path

• Pathway Explorer: Above tools can be used to explore pathways

• Pathway Designer: Offers graphical representation of molecules for

generating publication quality figures

• Canonical pathways can be selected and genes from the dataset can

be overlaid

Network/Pathway Analysis

Documents

Tools and Algorithms in Bioinformatics• Unmapped Ids: Unidentified by the IPA database • Check for typos and try to map as many as possible • Analysis –ready molecules: Identified