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Stanford CS 374 - Regulatory Motif Finding

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Regulatory Motif Finding (II)OverviewBiology of MotifsSlide 4Slide 5Slide 6Slide 7Slide 8Slide 9Why motifs?Recap #1Motif Finding OverviewMotif ShootoutSlide 14Slide 15Slide 16Motifs via Functional GenomicsSlide 18Slide 19Motifs via Phylogenetic FootprintingSlide 21Slide 22Slide 23RecapIntegrated Motif FindingSlide 26Slide 27Slide 28Open QuestionsConclusionRegulatory Motif Finding (II)Balaji S. SrinivasanCS 374Lecture 18 12/6/2005OverviewBiology of DNA binding motifsWhy motifs?Overview of motif finding algorithmsOpen problems in this areaBiology of MotifsFrom last time…Biology of MotifsFrom last time…Biology of MotifsGiven transcription factor (TF) of fixed sequence…binding affected bysecondary, tertiary structure of DNAmethylation stateDNA binding motifsBiology of MotifsDNA Motifs (regulatory elements)Binding sites for proteinsShort sequences (5-25)Up to 1000 bp (or farther) from geneInexactly repeating patternsBiology of MotifsTF binding affected bysecondary, tertiary structure of DNAmethylation stateDNA binding motifsShould be on your radar…motifs frontier of research why? sequence data existsstatic, not dynamicdynamic chromosome:accessibility affectstranscription…dynamic epigenome(methylation state)Biology of MotifsProkaryotesfewer TFslong motifsaffinity dep on matchEukaryotes (HARD)more TFs per geneshorter motifsMUCH more noncoding seqregulatory moduleslong range effectsproks:immediateupstream regeuks:long range regulationBiology of MotifsTranscription Factorsoften dimer, tetramer: palindromic binding sitebindingstochasticaffinity = structural/sequence matchhigh affinity not always desirablecombinatorial regulation (esp. eukaryotes)order important!site spacing important!Why motifs?Given: all TF/motif pairsGet: global genetic regulatory networkmicrobialeukaryoticRecap #1To figure out transcriptional control…find transcription factor binding sitesEukaryotes: hard b/cmuch more noncoding sequenceshorter motifslonger range interactionsMotif Finding OverviewMethods1 genomesequence overrepresentation (NBT shootout, not good)Functional Genomicspredict regulons (Segal, etc.)N genomes phylogenetic footprinting (Kellis, etc.)N genomes + Func GenomicsPhylocon (Tompa)New ideas…Motif ShootoutNature Biotech Jan. 200513 way shootoutdisappointing resultsUseful in thatshows importance of using all infobenchmarking is clearly trouble areaMotif ShootoutMotif ShootoutConceptuallyload FASTA hopper of intergenic sequence from 1 genome into black boxoutput: motif matrices But…how to pick sequences?comparison?functional clustering?benchmarking? upstreamsMotif ShootoutBut…how to pick sequences?comparison?functional clustering?benchmarking? Sonot as useful as it seems…huge, artificial limitations“consider a spherical cow”What if limitations removed?Motifs via Functional GenomicsCoexpressionmost popular (e.g. Segal 2003)Functional clusteringthen hunt upstreamMotifs via Functional GenomicsChip/CHIPkey idea: assay DNA segments where TF bindsdirect test of motif binding (e.g. Laub 2002)Disadvantagesone TF at a timeneed an antibody!Motifs via Functional GenomicsCoinheritance, etc.predict regulons, then look upstreamheuristic network integrationwill return to this pointdecent signal in prokaryotes (Manson-Mcguire 2001)Motifs via Phylogenetic FootprintingKey ideafunctional sequence evolves more slowlyconservation hierarchyultraconserved NC elems (Bejerano & Haussler 2004)proteins, ncRNAsDNA binding motifsunconstrained, neutrally drifting regionsno conservationultraconservedMotifs via Phylogenetic FootprintingPhylogenetic footprint“footprint” is conservationsimple versionmultiple alignment of orthologous upstream regionsProblem: nonfunctional sequence drifts rapidlymultiple align difficult if only small % conservedprotein twilight zone: 30% identity nucleic acids upstream regions: often much less…Motifs via Phylogenetic FootprintingPhylogenetic FootprintProblem: multiple alignment of upstreams hits twilight zoneOne solutionsearch for parsimonious substrings…without direct alignment (Blanchette 2003)Motifs via Phylogenetic FootprintingMultiple genome alignment can workneed close enough species Kellis 2003 (four yeasts, genome alignments)Xie 2005 (“four” mammals, genome alignment)Discussed last timeKey pointsGenome wide searchMotif Conservation Score: null model based testRecapMany programs for motif searchmost are useless!Lesson: must use comparative genomics (e.g. alignment)…or functional genomics (e.g. expression)what about both together??Integrated Motif FindingRecallcomparative genomicsone upstream region in N speciesfunctional genomicsN upstream regions in one speciesPhylocon (Tompa 2003)N upstreams in N speciesIntegrated Motif FindingPhylocongiven N speciesalign upstream regionskey idea: align the alignmentsBoosts sensitivityLEU3 hard to find…Integrated Motif FindingBoosts sensitivityLEU3 hard to find…but align the alignmentstrue motif pops out!Integrated Motif FindingImportant featuresno prior motif length reqd.profile approach matches distribution, not sample (robust to subs)several alignments for each upstream are OKdoes well vs. real data…ALLR (avg. log. like. ratio)Q: are 2 profile columns samples from same distribution?if so, that may be a matching motif position…Open QuestionsPhylocon is strong step in right direction…align the alignmentsBut how do we…choose species?choose upstreams?validate motifs? find TF/motif pairs?ConclusionMotifs important static, tractable, impt. want: genetic regulatory networksMotif finder selectionDon’t: use 1 genome w/o comparison or func. genomicsDo: use alignment & func genomicsPhylocon (Tompa), MCS (Kellis)best to date b/c use N genes and M


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