Gene Regulation and MicroarraysOverviewA. Regulation of Gene ExpressionCells respond to environmentGenome is fixed – Cells are dynamicGene regulationWhere gene regulation takes placeTranscriptional RegulationTranscription Factors Binding to DNAPromoter and EnhancersRegulation of GenesSlide 12Slide 13Example: A Human heat shock proteinThe Cell as a Regulatory NetworkThe Cell as a Regulatory Network (2)B. DNA MicroarraysWhat is a microarrayWhat is a microarray (2)How to make a microarraySample DataVisualization ToolsGoal of Microarray ExperimentsAnalysis of Microarray DataHierarchical Agglomerative ClusteringResults of Clustering Gene ExpressionK-Means Clustering AlgorithmK-Means AlgorithmSlide 29Slide 30Slide 31Slide 32Slide 33Slide 34Multiple-pass K-Means clusteringInitializationExampleSlide 38Iteration of the approachRemoving Duplicate CentroidsRepeat 3 times4. Analysis of Clustered DataC. Finding Regulatory MotifsFinding Regulatory MotifsCharacteristics of Regulatory MotifsSequence LogosProblem DefinitionEssentially a Multiple Local AlignmentAlgorithmsDiscrete Approaches to Motif FindingDiscrete FormulationsGene Regulation and Gene Regulation and MicroarraysMicroarraysOverview•A. Gene Expression and Regulation•B. Measuring Gene Expression: Microarrays•C. Finding Regulatory MotifsA. Regulation of Gene ExpressionCells respond to environmentHeatFoodSupplyResponds toenvironmentalconditionsVarious external messagesGenome is fixed – Cells are dynamic•A genome is staticEvery cell in our body has a copy of same genome•A cell is dynamicResponds to external conditionsMost cells follow a cell cycle of division•Cells differentiate during developmentGene regulation•Gene regulation is responsible for dynamic cell•Gene expression varies according to:Cell typeCell cycleExternal conditionsLocationWhere gene regulation takes place•Opening of chromatin•Transcription•Translation•Protein stability•Protein modificationsTranscriptional Regulation•Strongest regulation happens during transcription•Best place to regulate: No energy wasted making intermediate products•However, slowest response timeAfter a receptor notices a change:1. Cascade message to nucleus2. Open chromatin & bind transcription factors3. Recruit RNA polymerase and transcribe4. Splice mRNA and send to cytoplasm5. Translate into proteinTranscription Factors Binding to DNATranscription regulation:Certain transcription factors bind DNABinding recognizes DNA substrings:Regulatory motifsPromoter and Enhancers•Promoter necessary to start transcription•Enhancers can affect transcription from afarRegulation of Genes GeneRegulatory ElementRNA polymerase(Protein)Transcription Factor(Protein)DNARegulation of Genes GeneRNA polymeraseTranscription Factor(Protein)Regulatory ElementDNARegulation of Genes GeneRNA polymeraseTranscription FactorRegulatory ElementDNANew proteinExample: A Human heat shock protein•TATA box: positioning transcription start•TATA, CCAAT: constitutive transcription•GRE: glucocorticoid response•MRE: metal response•HSE: heat shock elementTATASP1CCA ATAP2HSEAP2CCA ATSP1promoter of heat shock hsp700--158GENEThe Cell as a Regulatory Network•Genes = wires•Motifs = gatesA B Make DCIf C then DIf B then NOT DIf A and B then DDMake BDIf D then BCgene Dgene BThe Cell as a Regulatory Network (2)B. DNA MicroarraysMeasuring gene transcription in a high-throughput fashionWhat is a microarrayWhat is a microarray (2)•A 2D array of DNA sequences from thousands of genes•Each spot has many copies of same gene•Allow mRNAs from a sample to hybridize•Measure number of hybridizations per spotHow to make a microarray•Method 1: DNA microarray (Stanford)Use PCR to amplify a 1Kb portion of each geneApply each sample on glass slide•Method 2: DNA Chip (Affymetrix)Grow oligonucleotides (25bp) on glassSeveral words per gene (choose unique words)If we know the gene sequences,Can sample all genes in one experiment!Sample DataVisualization ToolsGoal of Microarray Experiments•Measure level of gene expression across many different conditions:Expression Matrix M: {genes}{conditions}:Mij = |genei| in conditionj•Deduce gene functionGenes with similar function are expressed under similar conditions•Deduce gene regulatory networks – parts and connections-level description of biologyAnalysis of Microarray Data•ClusteringIdea: Groups of genes that share similar function have similar expression patterns•Hierarchical clustering•k-means •Bayesian approaches•Projection techniques•Principal Component Analysis•Independent Component Analysis•ClassificationIdea: A cell can be in one of several states •(Diseased vs. Healthy, Cancer X vs. Cancer Y vs. Normal) Can we train an algorithm to use the gene expression patterns to determine which state a cell is in?•Support Vector Machines•Decision Trees•Neural Networks•K-Nearest NeighborsHierarchical Agglomerative ClusteringMichael Eisen, 1998•Hierarchical Agglomerative ClusteringStep 1: Similarity score between all pairs of genes•Pearson CorrelationStep 2: Find the two most similar genes, replace with a node that contains the average•Builds a tree of genesStep 3: Repeat.Can do the same with experimentsResults of Clustering Gene Expression•CLUSTER is simple and easy to use•De facto standard for microarray analysisTime: O(N2M)N: #genesM: #conditionsK-Means Clustering Algorithm•Randomly initialize k cluster means•Iterate:Assign each genes to the nearest cluster mean Recompute cluster means•Stop when clustering convergesNotes:•Really fast•Genes are partitioned into clusters•How do we select k?K-Means Algorithm•Randomly Initialize ClustersK-Means Algorithm•Assign data points to nearest clustersK-Means Algorithm•Recalculate ClustersK-Means Algorithm•Recalculate ClustersK-Means Algorithm•RepeatK-Means Algorithm•RepeatK-Means Algorithm•Repeat … until convergenceTime: O(KNM) per iterationN: #genesM: #conditionsMultiple-pass K-Means clustering(A Gasch, MB Eisen 2002)•Each gene can belong to many clusters•Soft (fuzzy) assignment of genes to clustersEach gene has 1.0 membership units, allocated amongst clusters based on correlation with means•Cluster means are calculated by taking the weighted average of all the genes in the clusterAlgorithm:•Use PCA to initialize cluster means•3 applications of k-means clustering,
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