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Manolis Kellis modENCODE analysis group January 11, 2007

Manolis Kellis modENCODE analysis group January 11, 2007. Part 1 : Target identification: comparative vs. exprmt. (really the topic for today) Part 2 : Target validation (optional) Part 3 : Motif discovery (optional) Part 4 : Enhancer identification (optional). Part 1.

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Manolis Kellis modENCODE analysis group January 11, 2007

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  1. Manolis KellismodENCODE analysis groupJanuary 11, 2007 Part 1: Target identification: comparative vs. exprmt. (really the topic for today) Part 2: Target validation (optional) Part 3: Motif discovery (optional) Part 4: Enhancer identification (optional)

  2. Part 1 Identifying targets using comparative genomics

  3. Evolutionary signatures of motif instances • Allow for motif movements • Sequencing/alignment errors • Loss, movement, divergence • Measure branch-length score • Sum evidence along branches • Close species little contribution BLS: 25% BLS: 83% Mef2:YTAWWWWTAR

  4. Motif confidence selects functional instances Transcription factor motifs Confidence Confidence Increasing BLS  Increasing confidence Confidence selects functional regions Confidence selects in vivo bound sites High sensitivity microRNA motifs Confidence selects positive strand Increasing BLS  Increasing confidence Confidence selects functional regions

  5. Initial regulatory network for an animal genome • ChIP-grade quality • Similar functional enrichment • High sens. High spec. • Systems-level • 81% of Transc. Factors • 86% of microRNAs • 8k + 2k targets • 46k connections • Lessons learned • Pre- and post- are correlated (hihi/lolo) • Regulators are heavily targeted, feedback loop

  6. Network captures literature-supported connections

  7. Network captures co-expression supported edges Red = co-expressed Grey = not co-expressed Named = literature-supported Bold = literature-supported 46% of edges are supported (P=10-3)

  8. ChIP vs. conservation: similar power / complementary • Together: best  complementary • Bound but not conserved: reduced enrichmnt  Selects functional • All-ChIP vs. All-cons: similar enr.  Similar power • Cons-only vs. ChIP-all: similar  Additional sites

  9. Part 2 Cool story of miRNA targets for a new anti-sense miRNA

  10. Surprise: miR-Anti-sense function A single miRNA locus transcribed from both strands Both processed to mature miRNAs: mir-iab-4, miR-iab-4AS (anti-sense) The two miRNAs show distinct expression domains (mutually exclusive) The two show distinct Hox targets – another Hox master regulator

  11. Surprise: miR-Anti-sense function Mis-expression of mir-iab-4S & AS: altereswingshomeotic transform. Stronger phenotype for AS miRNA Sense/anti-sense pairs as general building blocks for miRNA regulation 9 new anti-sense miRNAs in mouse wing w/bristles Sensory bristles haltere wing haltere WT Note: C,D,E same magnification wing sense Antisense

  12. Part 3 (optional) Discovering motifs

  13. Evolutionary signatures for regulatory motifs Individual motif instances are preferentially conserved Measure conservation across entire genome Over thousands of motif instances  Increased discovery power Couple to rapid enumeration and rapid string search  De novo discovery of regulatory motifs 5’-UTR 3’-UTR Known engrailed site (footprint) D.mel CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC D.sim CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC D.sec CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC D.yak CAGC--TAGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC D.ere CAGCGGTCGCCAAACTCTCTAATTAGCGACCAAGTC-CAAGTC D.ana CACTAGTTCCTAGGCACTCTAATTAGCAAGTTAGTCTCTAGAG ** * * *********** * **** * ** D.mel D. ere D. ana D. pse.

  14. Power of evolutionary signatures for motif discovery Ability to discover full dictionary of regulatory motifs de novo

  15. Tissue-specific enrichment and clustering • Infer candidate functions for novel motifs • Reveal ‘modules’ of co-operating motifs Functional clusters emerge

  16. Discovered motifs show positional biases • May represent new core promoter elements • Show enrichment in distinct functional categories

  17. Recognizing functional motifs in coding regions • Challenge: • Overlapping selective pressures • Most ‘motifs’ from di-codon biases • Hundreds of motifs due to noise • Solution: • Test each frame offset separately • Di-codon biases  Frame biased • True motifs  Frame unbiased • Result: • Top 20 motifs  11 miRNA seeds • (before: 11 seeds in 200+ motifs) miRNAs Top motifs Ability to distinguish overlapping pressures Evidence of miRNA targeting in coding reg.

  18. miRNA targeting in protein-coding regions • MicroRNA seeds are specifically selected • Coding & 3’UTRs show same conservation profile

  19. Part 4 (optional) Characterizing enhancers

  20. Bound in vivo. Conserved D/Tw/Sn motifs in 12 flies. Clear DV expression pattern (lacZ/end). • Large number of novel enhancers (428 Dorsal/Twi/Sna). They validate! Developmental enhancer identification in Drosophila • Supported by tiling arrays and regulatory motifs (nucleotide resolution) • Identify nearly all known enhancers (20 of 22 highly bound)

  21. Surprise 1: AP genes targeted by DV regulators • Novel DorsoVentral enhancers in known AntPosterior genes • Bound in vivo by DV genes (by all three DV master regulators) • Show evolutionarily conserved motifs for all three DV factors • Yet, found in known AP genes, with clear AP expression patterns  Integration of DV and AP patterning networks

  22. Active Repressed Poised Surprise 2: Some silent genes show Pol II binding • Distinct modes of Pol II occupancy • Active genes (27%): Pol II throughout the gene, transcribing • Repressed genes (37%): Pol II simply absent, no expression • Third class (12%): Pol II found only at the TSS, stalled • Qualitatively different: abundantly bound, but strongly punctate • Genes not expressed: known repressed genes, confirmed by arrays • Enriched in development, neurogenesis, ectoderm, muscle differ. • Hypothesis: Developmental genes poised for expression • Reminiscent of ‘bivalent’ K4/K27 domains in mammals

  23. Surprise 3: Master regulators also bind downstream targets • Abundant feed-forward loops in DV patterning • Cooperation of master reg. & downstream reg.

  24. Manolis Kellis - modENCODE analysis - summary • Part 1: Target identification • Comp. vs. Expt: each has unique advantages • Bound & not conserved appear less functional! • Part 2: Target validation (for anti-sense miRNA) • It’s nice when expected outcome comes true • Need more collaborations for target validation • Part 3: Motif discovery • Methods for genome-wide motif discovery • Expect increased power in bound regions • Part 4: Enhancer identification • Many new enhancers – with motifs & validation • AP / DV system cross-talk – expect dense network • PolII stalling: spatial dynamics matter

  25. Who’s actually doing the work Main contributors: Alex Stark Pouya Kheradpour Julia Zeitlinger Collaborators: Targets Sushmita Roy @ UNM iab-4AS Natascha Bushati, Steve Cohen @ EMBL Julius Brennecke, Greg Hannon @ CSHL Calvin Jan, David Bartel @ Whitehead Enhancers Julia Zeitlinger, Rick Young @ Whitehead Robert Zinzen, Mike Levine @ UC Berkeley

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