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Regulatory Motif Finding (II). Balaji S. Srinivasan CS 374 Lecture 18 12/6/2005. Overview. Biology of DNA binding motifs Why motifs? Overview of motif finding algorithms Open problems in this area. Biology of Motifs. From last time…. Biology of Motifs. From last time ….

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regulatory motif finding ii

Regulatory Motif Finding (II)

Balaji S. Srinivasan

CS 374

Lecture 18

12/6/2005

overview
Overview
  • Biology of DNA binding motifs
  • Why motifs?
  • Overview of motif finding algorithms
  • Open problems in this area
biology of motifs
Biology of Motifs
  • From last time…
biology of motifs4
Biology of Motifs
  • From last time…
biology of motifs5
Biology of Motifs
  • Given transcription factor (TF) of fixed sequence…
  • binding affected by
    • secondary, tertiary structure of DNA
    • methylation state
    • DNA binding motifs
biology of motifs6
Biology of Motifs
  • DNA Motifs (regulatory elements)
    • Binding sites for proteins
    • Short sequences (5-25)
    • Up to 1000 bp (or farther) from gene
    • Inexactly repeating patterns
biology of motifs7
Biology 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 dynamic

dynamic chromosome:

accessibility affects

transcription…

dynamic epigenome

(methylation state)

biology of motifs8

proks:

immediate

upstream reg

euks:

long range

regulation

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 effects
biology of motifs9
Biology 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
Why motifs?
  • Given: all TF/motif pairs
  • Get: global genetic regulatory network

microbial

eukaryotic

recap 1
Recap #1
  • To figure out transcriptional control…
    • find transcription factor binding sites
  • Eukaryotes: hard b/c
    • much more noncoding sequence
    • shorter motifs
    • longer range interactions
motif finding overview
Motif 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
Motif Shootout
  • Nature Biotech Jan. 2005
    • 13 way shootout
    • disappointing results
  • Useful in that
    • shows importance of using all info
    • benchmarking is clearly trouble area
motif shootout15

upstreams

Motif 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?
motif shootout16
Motif 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
Motifs via Functional Genomics
  • Coexpression
    • most popular (e.g. Segal 2003)
  • Functional clustering
    • then hunt upstream
motifs via functional genomics18
Motifs 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 genomics19
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

ultraconserved

no conservation

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 regions
motifs via phylogenetic footprinting21
Motifs 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 footprinting22
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 footprinting23
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 test
recap
Recap
  • 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
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 species
integrated motif finding26
Integrated Motif Finding
  • Phylocon
    • given N species
    • align upstream regions
    • key idea: align the alignments
  • Boosts sensitivity
    • LEU3 hard to find…
integrated motif finding27
Integrated Motif Finding
  • Boosts sensitivity
    • LEU3 hard to find…
    • but align the alignments

true motif

pops out!

integrated motif finding28
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
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
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 species
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