1. De novo Motif Finding using ChIP-Seq
My Paper is rejected!
De novo Motif Finding using ChIP-Seq
Presenter: Zhizhuo Zhang
Supervisor: Wing-Kin Sung

2. Copyright 2009 @ Zhang ZhiZhuo
Outline
The Impact of Chip-Seq’s Properties in Motif Finding
Our proposing algorithm (Pomoda)
Problem 1
Solution
Problem 2
Comment from Review
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De novo Motif Finding
Input: A set of regulatory sequence that possibly bind to the same transcription factor obtained using experiment techniques such as ChIP, orthologous genes across various species or co-regulated genes identified by micro array analysis. Aim: We want to use a computational algorithm to search for a set of recurring motif models (de novo) in the given input sequences.
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Motif Modeling
Consensus-based Motif : Guarantee global optimality and they are appropriate for short motifs. Very fast when implemented with optimized data structures: Suffix Tree, Mismatch Tree, Lookup Table…
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Motif Modeling
PWM-based Motif: Assuming each position is independent with each other. It is more precise approximation to the real motif than consensus-based motif. (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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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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8. Comparison with Chip-Chip
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9. 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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11. 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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12. 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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How to Score a PWM
Instead of comparing with sophicated background model, we just look at distribution from center to flanking region!
Traditionally, people first extract DNA sequences from every peak around a fixed window
d (says, d=400bp); then, utilizing some motif finder, we mine motifs which are over-represented in those
sequences under some organism specific background model.
There are two issues with the above method. First, we need to specify the window size d. If d is too big,
the motif signal is diluted . If d is too small, we may fail to capture some motifs which occur in a window
bigger than d. Second, we need to specify the background model. However, selecting background model is
still an art. There is no guideline for selecting the correct background.
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Peak Enrichment Score
Given a PWM motif Ɵ and a set of input sequences of length L centered by peak location, the peak enrichment score is defined as: H h
Signal Noise Ratio = (H-h)/h
A simple Score =H/h
Noise Level
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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Problem 1
Although the scoring function is quite simple, an efficient optimization method is badly wanted, due to the huge size of datasets and many parameters in a PWM.
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Solution
Hierarchically pruning the searching space! (Easy testing for the large number of candidates and perform more complicated work on the few left)
Column based PWM updating by comparing Center components and Flanking components!
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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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20. 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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21. PWM Extending & Refinement
Flank
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
The frequency C’s in the highlighted column is 100% and 20%, so if we take C at that column, we can gain 5 times higher score!
GGTCANNNNC
Select the best column to update based on Center PWM and Flank PWM.
Center Instances
A…A…GGTCA…C…C
T…G…GGTCA…C…G ……
C…T…GGTCA…C…A
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More Details
Two Cases: 1. Update a trivial column (extension) 2. Update a non-trivial column (refinement)
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24. Update a trivial column (extension)
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25. Update a non-trivial column (refinement)
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26. Update a non-trivial column (refinement)
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27. When the data is not large
The method above will be fail when the number of occurrences is small, that means the noise level not high enough!
Fixed Method: when Flanking PWM element larger than max Flanking region ACGT frequency, that element will be set the value as that frequency. Then a discriminant process becomes an associational process!
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Problem 2
There are many extended sub-patterns after phase2, we need to generalize them and filter the redundant ones.
How to measure the similarity of two sub-patterns?
How to cluster similar ones and merge them to a generalized pattern?
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Problem 2
GGTCACHSTGAC CMRGGTCAS AGGTCASSCTGMCC CAGGTCASNNTGMCC CWGGGTCASNNTSNS GGTCARGGTCA GTCANCGT RRGNYRNCCTGACC AGGTCAAS GGTSACCCWG
All these sub-patterns from
the same generalized pattern!
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30. Redundant Motifs Filtering (old solution)
Two levels filtering:
Level 1: comparing to the accepted motifs
Positions overlap more than 2%
PWM divergence less than 0.18
Level 2: comparing to the filtered motifs
Positions overlap more than 20%
PWM divergence less than 0.14
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31. Only Filter better than Merge
People like to suggest me to cluster the sub-patterns and perform a weighted merging. However, there are several problems make merging screw up.
Similarity may be not correct, introducing noise in the cluster.
Alignments between two PWMs may be not correct, diverge the column signals
The weighted is may be not correct, the merged PWM is not optimized for the scoring anymore.
To solve any problem above needs the state of art skill.
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Old solution
There are 4 parameters in the 2-level filtering, and actually there are still other parameters as the significant levels of other statistics test like binominal and hyper-geometrics. All these parameters are tuned to filter out the unwanted motifs in the top list. That’s why you can always see a crazy guy tuning program in the lab!
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New Solution
Do one level filter (2 common used parameter 5% and 0.24)
Used the accepted sub-patterns as the starting point for MEME. That is, we use zoops MEME to generalize the pattern. (Only for the hot regions, which are covered by at least two sub-patterns)
Do step1 again, and rank the final motifs by Peak Enrichment Score.
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34. Results – Comparison (old)
Dataset:
MCF7 dataset (ER), 4361 sequences
LNCAP dataset (AR), sequences
ES Cell datasets (OCT4,C-myc,Sox2,Zfx,Smad,…,CTCF)
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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36. The points of Reviewers
Length-6 conserved seed assumption may not true;
There is no complexity analysis given;
The selections of co-motifs is biased to my program;
The parameters in my program are tuned to fit the data, not fair to other programs;
Fixed input length to all other programs is not fair.
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Merry Christmas!
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