1. De novo Motif Finding using ChIP-Seq
Presenter: Zhizhuo Zhang
Supervisor: Wing-Kin Sung

2. Copyright 2009 @ Zhang ZhiZhuo
Outline
Introduction of Chip-Seq Data
The Impact of Chip-Seq’s Properties in Motif Finding
Our proposing algorithm (Pomoda)
Experiment Result
Exploring Center Distribution
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3. Copyright 2009 @ Zhang ZhiZhuo
Chip-Seq Technique
chromatin immunoprecipitation
(ChIP) with specific antibodies against these TFs was used to
enrich the DNA fragments bound by these TFs, followed by di-
rect ultra-high-throughput sequencing with the Solexa Genome
Analyzer platform. Genomic regions defined bymultiple overlap-
ping DNA fragments derived from the ChIP enrichments were
considered as putative binding sites.
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4. Comparison with Chip-Chip
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5. What Chip-Seq means to us？
Sequences
Motif Finding Tools
Motif models
More data Good news for data mining, but necessary for denovo motif finding Higher resolution job becomes easier, localization
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How large the data is?
The definition of “large data” keeps changing!
10 years before, tens of sequences (Promoter Sequences: MEME,AlignACE)
5 years before, hundreds of sequences (Chip-Chip: Weeder)
2 years before, thousands of sequences (higher throughput Chip-Chip: Trawler, Amandeus)
Now, tens of thousands of sequences (Chip-Seq: ?)
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7. Higher Resolution Means?
Means finding main motif (antibody targeting TF) becomes a easy job! Main Motif would be very over-represented The Peak range just about 50 bp, simply align all the peak region, we can get the good motif. It means our focuses may change from the main TF to the TFs who are working with the main one.
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8. Localization =？Over-Representation
Use Transfac motif scanning, result:
If u use the center region as input sequence, u will see the OR
If u use the surrounding region as bg, u will see localized.
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9. Peak Oriented Motif Discovery
What information of Peak can be helpful? Peak Intensity Peak location Our targets: not only the main motif, but also the co-motifs sitting around the main motif.
PI: higher intensity, higher change of main motif, imply higher change of co-motif
PL: surrounding region would be enriched.
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POMODA
Peak Oriented Motif Discovery Algorithm
Centered on
ChIP-seq peak of
With the above discussion in mind, we look into an example.
The colored dots are motif matches predicted by our scan.
The histogram of each of them can be studied
The main motif
A co-motif
Should be noise as it does not exhibit distance preference to the main motif
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Motif Modeling
String Motif : Smaller searching space, enable fast string matching algorithm
PWM Motif: More precise approximation to the real motif, statistics sound. (PWM—Position Weighted Matrix)
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Background Modeling
Organism Specified Background: Hard to capture the negative information in background Position Specified Background: Reveal the biological context, and easier to capture the negative information
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13. Position Specified Background
Given the peak position in chip-seq, we not only identify the active position(center) of the master TF, but also the active region of its co-motif.
Active Region
Background Region
Peak in Chip-Seq
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14. Center Enrichment Score
Since we don’t know the exact size of the active region, and it may vary for different motif. Hence, we define a odd-ratio score base on dynamic window size.
We can utilize the Peak intensity in calculating the Occurrences
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Algorithm Overview
Seed Finding
PWM Extending & Refinement
Redundant Motifs Filtering
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Seeds Finding
GGTCAC CGGTCA GGGTCA AGGTCA … ATGACC CAGGTC AGGTCG CGTGAC CTGACC
Enumerate all length 6 patterns Po 1 2 3 4 5 6 A
0.97
0.01 C G T
AACTTG
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17. PWM Extending & Refinement
Encapsulate the core PWM into a wide PWM For example, we implant the length 6 PWM into a length 26 PWM, as following: Po 1 2 …… 9 10 11 12 13 14 15 16 25 26 A
0.25
0.97
0.01 C G T
Core PWM
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18. PWM Extending & Refinement
Background
Instances
PWM Extending & Refinement
A…A…GGTCA…C…C
T…G…GGTCA…A…G
G…A…GGTCA…T…T
T…G…GGTCA…G…G ……
C…T…GGTCA…T…A
Select the best column to update based on Center PWM and Bg PWM.
Center Instances
A…A…GGTCA…C…C
T…G…GGTCA…C…G ……
C…T…GGTCA…C…A
GGTCANNNNC
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19. Redundant Motifs Filtering
Positions overlap more than 5%
PWM divergence less than 0.18
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Results – Comparison
Dataset:
MCF7 dataset (ER), 4361 sequences
LNCAP dataset (AR), sequences
Evaluate “PWM divergence” with Transfac motif as in Harbison et al (2004) and Amadeus (2008)
+/ bases from peak (Pomoda), and +/- 200 bases from peak for other algorithms
Each motif finder report its top20 results
Reason: 1. they can’t handle such large range, 2. their result will be worse when the width increase.
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Cell TF
Pomoda
Amadeus
Trawler
Weeder
Mcf7 ER
HNF3
GATA
AP1
SP1
BACH1
E2F
OCT1
AP4
LNCAP AR
NF1
OCT
ETS
<0.12
<0.18
<0.24
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Comparison
Pomoda
Amadeus
Trawler
Weeder
Background model
Position Specified
Organism Specified
Motif model
PWM
(k-mer exact match)
PWM (k-mer with mismatches )
PWM (IUPAC string in initial scan)
k-mer with mismatches
Algorithm
Exhaustive search
+PWM column updating
Add mismatches
Merge (recursively) EM
Exhaustive search + clustering
Motif Length
Various length
Fixed length
Semi-various length
Gap Detection
Supported
Not Supported
Localization
center windows size
Over-represented bins
Not supported
Sequence Weighting
Average
Running time
30min
93min
>4hours
>4 hours
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Center Distribution
Mixture Model:
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Thank You!
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