1. Motif Finding Continued
Xiaole Shirley Liu
STAT115, STAT215, BIO298, BIST520

2. Scoring Motifs Information Content (aka relative entropy)
Suppose you have x aligned segments for the motif
pb(s1 from mtf) / pb(s1 from bg) *
pb(s2 from mtf) / pb(s2 from bg) *…
pb(sx from mtf) / pb(sx from bg)
Pos
ATGGCATG
AGGGTGCG
ATCGCATG
TTGCCACG
ATGGTATT
ATTGCACG
AGGGCGTT
ATGACATG
ACTGGATG
Motif Matrix
Sites
Segment ATGCAGCT score =
p(generate ATGCAGCT from motif matrix)
p(generate ATGCAGCT from background)
p0A  p0T  p0G  p0C  p0A  p0G  p0C  p0T

3. Scoring Motifs pb(s1 from mtf) / pb(s1 from bg) *
pb(sx from mtf) / pb(sx from bg)
= (pA1/pA0)A1 (pT1/pT0)T1 (pT2/pT0)T2 (pG2/pG0)G2 (pC2/pC0)C2…
Take log of this:
= A1 log (pA1/pA0) + T1 log (pT1/pT0) +
T2 log (pT2/pT0) + G2 log (pG2/pG0) + …
Divide by the number of segments (if all the motifs have same number of segments)
= pA1 log (pA1/pA0) + pT1 log (pT1/pT0) + pT2 log (pT2/pT0)…
Pos
ATGGCATG
AGGGTGCG
ATCGCATG
TTGCCACG
ATGGTATT
ATTGCACG
AGGGCGTT
ATGACATG
ACTGGATG

4. Scoring Motifs Original function: Information Content =
Motif Conservedness: How likely to see the current aligned segments from this motif model
Good
ATGCA
ATGCC
TTGCA
ATGGA
Bad
AGGCA
ATCCC
GCGCA
CGGTA
TGCCA
ATGGT
TTGAA

5. Scoring Motifs Original function: Information Content =
Motif Specificity:
How likely to see the current aligned segments from background
Good
AGTCC
Bad
ATAAA

6. Scoring Motifs Original function: Information Content =
Which is better?
(data = 8 seqs) =
Motif 1
AGGCTAAC
Motif 2
AGGCTAAC
AGGCTACC
AGCCTAAC
AGGCCAAC
TGGCTAAC
AGGCTTAC
AGGGTAAC

7. Specific (unlikely in genome background)
Scoring Motifs
Motif scoring function:
Prefer: conserved motifs with many sites, but are not often seen in the genome background
Motif Signal
Abundant
Positions
Conserved
Specific (unlikely in genome background)

8. Markov Background Increases Motif Specificity
Prefers motif segments enriched only in data, but not so likely to occur in the background
Segment ATGTA score =
p(generate ATGTA from )
p(generate ATGTA from 0)
3rd order Markov dependency
p( )
TCAGC = .25  .25  .25  .25  .25
.3  .18  .16  .22  .24
ATATA = .25  .25  .25  .25  .25
.3  .41  .38  .42  .30

9. Position Weight Matrix Update
Advantage
Can look for motifs of any widths
Flexible with base substitutions
Disadvantage:
EM and Gibbs sampling: no guaranteed convergence time
No guaranteed global optimum
Break

10. Motif Finding in Lower Eukaryotes
Upstream sequences longer ( bp), with some simple repeats
Motif width varies (5 – 17 bases)
Expression clusters provide decent input sequences quality for TF motif finding
Motif combination and redundancy appears, although single motifs are usually significant enough for identification

11. Yeast Promoter Architecture
Co-occurring regulators suggest physical interaction between the regulators

12. Introducing Sequence Conservation
Kellis, et al, Nat 2003

13. Motif Finding in Higher Eukaryotes
Regulatory sequences very long with repeats and far from target genes (enhancers)
Motifs can be short or long (6-20 bases), and appear in combination and clusters
Gene expression cluster not good enough input
Need:
Better known motifs: e.g. PBM
Comparative Genomics: phastcons score
Motif modules: motif clusters
ChIP-chip/seq

14. UCSC PhastCons Conservation
Functional regulatory sequences are under stronger evolutionary constraint
Align orthologous sequences together
PhastCons conservation score (0 – 1) for each nucleotide in the genome can be downloaded from UCSC

15. Ultra Conserved Elements
> 200bp ultra conserved in vertebrates
Exonic enriched in RNA processing
Non-exonic enriched in TF binding sites for developmental genes
Bejerano et al, Science 2004

16. Conservation vs Functions
Non-conservation <> non-function
Human Accelerated Region enriched in neurodevelopment
Prabhakar et al, Science 2008

17. PreMod: motif clusters in conserved regions
Blanchette et al, Genome Res 2006

18. ChIP-seq
Break

19. Chromatin ImmunoPrecipitation (ChIP)
There are, of course, lots of transcription factors floating around in the nucleus but not associated with the chromatin.

20. TF/DNA Crosslinking in vivo
This is typically accomplished through the addition of formaldehyde. Note that there will also be protein-protein cross-linking, and the relative efficiency of protein-protein vs. protein-DNA links is still poorly understood.

21. Sonication (~500bp)

22. TF-specific Antibody

23. Immunoprecipitation

24. Reverse Crosslink and DNA Purification
Per Buck and Lieb, “;ow DNA yields from the IP reactions usually make DNA amplification a requirement for DNA microarray-based detection. Randomly-primed or ligation-mediated PCR-based methods have been most commonly used.” There are also newer and perhaps more accurate (consistent) methods currently being explored.

25. ChIP-Seq Map 30-50 mers back to the genome
ChIP-DNA
Noise
Sequence millions of mer ends of fragments
Map mers back to the genome
These are DNA fragments pulled down from different cells, all containing the binding site. When they are hybridized on the tiling array, probe signals will be high in the middle, and gradually decrease with distance. However, in the control, we should just see aggregate random noise and no peak. Therefore, by comparing ChIP with control, we can identify the factor’s binding locations.

26. MACS: Model-based Analysis for ChIP-Seq
Use confident peaks to model shift size
Binding

27. Peak Calls
Tag distribution along the genome ~ Poisson distribution (λBG = total tag / genome size)
ChIP-Seq show local biases in the genome
Chromatin and sequencing bias

28. Peak Calls
Tag distribution along the genome ~ Poisson distribution (λBG = total tag / genome size)
ChIP-Seq show local biases in the genome
Chromatin and sequencing bias
bp control windows have to few tags
But can look
further
Dynamic λlocal =
max(λBG, [λctrl, λ1k,] λ5k, λ10k)
ChIP
Control
300bp
1kb
5kb
10kb
Zhang et al, Genome Bio, 2008

29. ChIP-seq QC
Break

30. ChIP-seq QC Read quality (FASTQC) Read mapping % (higher the better)
Library complexity (avoid PCR amplification bias): # locations with 1 unique reads / # locations
Good to keep one read / location in peak calling (default MACS)
FDR adjustment?

31. ChIP-seq QC
Number of peaks with good FDR and fold change (2-5 fold cutoff)
FRiP score:
Fraction of reads in peaks, factor-dependent
Evolutionary Conservation
Majority of sites not conserved
Overlap with DNase-peaks
Later lectures

32. ChIP-seq Downstream Analysis
Break

33. Human TF Binding Distribution
Most TF binding sites are outside promoters
How to assign targets?
Binding in promoter (how far)?
Nearest distance?
Number of binding?
Other knowledge?
Still open Q

34. Higher Order Chromatin Interactions
Interactions ~ follows exponential decay with distance
Lieberman-Aiden et al, Science 2009

35. How to Assign Targets for Enhancer Binding Transcription Factors?
Regulatory potential: sum of binding sites weighted by distance to TSS with exponential decay
Rank1 of genes based on binding
TSS

36. How to Assign Targets for Enhancer Binding Transcription Factors?
Regulatory potential: sum of binding sites weighted by distance to TSS with exponential decay
Rank1 of genes based on binding
Rank2 of genes based on expression
Differential expression upon TF perturbation
Gene expression correlation in cohorts
Rank ordered list of targets: Rank1 * Rank2
Only minority of binding sites is functional in a condition

37. BETA Binding expression target analysis
Is TF an activator, repressor or both?
Do genes with higher regulatory potential show more up/down expression than random genes

38. BETA Binding expression target analysis
Is TF an activator, repressor or both?
Do genes with higher regulatory potential show more up/down expression than random genes
Functional analysis of targets?
How can a factor be both activator and repressor?
Collaborating transcription factors
Motif analysis on the binding sites near up vs down genes separately
TF?? ER

39. Summary
ChIP-seq identifies genome-wide in vivo protein-DNA interaction sites
ChIP-seq peak calling to shift reads
ChIP-seq QC: FDR, fold, conservation, etc
Functional analysis of ChIP-seq data:
Target identification
Activator / repressor function
Motif analysis