motif discovery: algorithm and application dan scanfeld hong xue sumeet gupta varun aggarwal
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Motif Discovery:Algorithm and
Application
Dan Scanfeld
Hong Xue
Sumeet Gupta
Varun Aggarwal
Objective: Motif discovery and use for deriving biological information
Get bound and unbound sequences by TF nanog
in human ES cells
Find a motif using a motif finding algorithm
Genome wide functional analysis using motif to find biological pattern
Why nanog: Relevance to ES Cells
1 Genome1 Cell
>200 Phenotypes1013 Cells
• Activate certain genes essential for cell growth
• Repress a key set of genes needed for an embryo to develop.
• This key set of repressed genes activate entire networks for generating many different specialized cells and tissues.
Objective: Motif discovery and use for deriving biological information
Find a motif (nanog) using a motif finding algorithm
Get bound and unbound Sequences by TF nanog
in Human ES cells
Genome wide Functional Analysis using motif to find biological signals
Location Analysis (ChIP-CHIP) in Human ES Cells (Cell Boyer et al 122: 947-956)
Differentially label
Crosslink Fragment Enrich for Nanog
44k 10 SetAgilent
ChIP-CHIP Data Analysis
Probe-set p-valuep=0.005
P<=0.001
P<=0.005
P<=0.01
Enr
ich
me
nt
ratio
Chromosomal position WCE signal
IP s
igna
l
0
Set - normalized negative control-
subtracted
Perform Median
Normalization
Sequences (500 bp)
May 2004 Genome Release
Obtain Intensities
using Genepix
Objective: Motif discovery and use for deriving biological information
Find a motif (nanog) using a motif finding algorithm
(State-of-the-art)
Get bound and unbound Sequences by TF nanog
in Human ES cells
Genome wide functional analysis using motif to find biological pattern
Motif Finding Algorithm(Mac Isaac, et. al., 2006)
Use Structural Prior(Database, MacIssac, et. al.)
Refinement:Expectation-Maximization (ZOOPS)
Score of found motifs:Classification on unseen data
Significance testing on score:Use of Empirical p-value
Refinement:Expectation-Maximization
Differences from EM in Lab 1
Use of structural prior (beta = Strength of prior)
ZOOPS (Zero or One per sequence) model
5th order Markov Model for background trained over unbound sequences
SVM for hypothesis testing
ZOOPS Model (Bailey & Elkan 1994)B Background Model, M: Motif ModelΛ Percentage of Bound Sequences (Mixture Model parameter)Sequences are drawn from the distribution
P(S) = P(S| M) Λ + P(S|B)(1- Λ)
Hidden Variable for EM: Zij : 1 or 0, position j in sequence i is bound by the TF (1) or not (0)
E-step:Prob(Zij) = [Λ *P(Si bound at j |M)] ----------------------------------------- [(1- Λ)P(Si |B) + Λ *∑ j P(Si bound at j |M)]
M-step:(SAME AS BEFORE)Updating M (Motif Model): For position p on the motif model and each base b (A C T or G)Baseip : Base at position p of ith sequencePWM(p,b) = ∑ i (∑ j (prob(Zi(j-p+1))* (Baseij = = b))) + pseudocounts AND NORMALIZE
Updating Background Model [[WE DON’T UPDATE BACKGROUND)
Updating ΛΛ = (∑ i ∑ j prob(Zij))/( number of sequences )
P(Si)
P(M bound at j | Si)
Hypothesis testing
Get motifs from EM Use 2 sets of bound and
unbound seq. ( Train and test)
Train a linear SVM on train set.
Find classification error on test set Error = Misclassifications/Total Samples
Score = 1 – error
B
UB
B
UB
Train Set
Input = P(S|M)/P(S|B)Output = B OR UB
Train ClassifierTest SetTest Classifier
B + EM Motif (M)
Expectation-Maximization
When to stop? Will it overtrain?
Rules of thumb (When likelihood increases very slowly) Second derivative is negative for given number of times Euclidean distance is less than given value
Over-train to given sequences
Maximizes likelihood of motif in given sequences. Disregards their likelihood in unbound sequences
Find test classification error at each EM step using SVMs.
Expectation-Maximization
A different Methodology: 4 sets of data:
Bound (for EM), B & U.B. (Train SVM), B. & U.B. (Test SVM), B. & U.B. (Validation)
At each EM iteration, train SVM and find test Error.
Use two kind of motifs Best Test Error motif EM last iteration motif Choose 10 best hypothesis Use larger validation set
Initial Points
Final Motif
SVM & Error
Initial Points
Final Motif
SVM & Error
SVM & ErrorSVM & Error
SVM & Error
Expectation-MaximizationDetails of RUN Transfactor: Nanog Beta = [0 0.2 0.35 0.5 0.6 0.7 1]
(Strength of prior) 5 motifs per beta by masking motifs Motif Length : 8 25 bound seqs for EM 500 base pairs in each seq. 150 total train seq (SVM) [Low: Noisy] 150 total test seq (SVM) [Low: Noisy] 500 total Validation seq. c = [1e-3,0.05,100.0] (SVM: Budget for misclassifications) EM for minimum 60 iterations, Second derivative is negative for five
iterations
Expectation-Maximization
Representative Score graphs during EM iterations
Beta 0.0 Beta 0.35
Beta 0.6 Beta 0.7
X-Axis: EM Iteration Y-Axis: Score of Motif
Expectation-Maximization
Test and Validate Error of refined Motifs
Test Classification Score
*: End of iteration EM resulto: Best of Iteration
Validate Classification Score
*: End of iteration EM resulto: Best of Iteration
X-Axis: beta Value Y-Axis: Score of Motif
Expectation-Maximization
When is it the best-of-iteration?
itera
tion
RUNS
Total iterations Iterations for Best-Of-Iterations
Expectation Maximization
Results:: 6 out of 7 top ranking motifs were best-of-
iteration and 1 was end-of-iteration (6 out of 10 as well)
Best Motif: Validate Error over set of 500 Score: 61.2%, Error: 38.8%A 0.003392 0.764554 0.995187 0.072268 0.063644 0.459349 0.000033 0.088069 C 0.268216 0.050266 0.000149 0.000022 0.303880 0.003363 0.472214 0.201074 G 0.039865 0.000023 0.002015 0.205620 0.105970 0.537248 0.446827 0.228689 T 0.688527 0.185157 0.002648 0.722090 0.526506 0.000040 0.080927 0.482167 T A A T T A or G C or G T
Assumptions and Caveats
Random baseline: End-of-run motif in EM
Low number of sequences for test error
Bound sets may actually not be bound. Better to use highly probable sequences as bound.
All runs (inc. beta=0) used starting point as the structural prior.
Objective: Motif discovery and use for deriving biological information
Find a motif (nanog) using a motif finding algorithm
Get bound and unbound Sequences by TF nanog
in Human ES cells
Genome wide functional analysis using motif to find biological pattern
GSEA (Subramanian et al 2005)
Gene Set Enrichment Analysis (GSEA) determines whether an a priori defined set of genes shows statistically significant differences between two biological states.
GSEA Output
Enrichment Plot Gene List Gene Set Information
GSEA Ranked List
Set of promoter sequences for every human gene.
2000 bp upstream and 200 bp downstream of Transcription initiation site.
Score each promoter for likelihood of the motif. Input this ranked list into GSEA. Search for gene sets enriched in the ranked list.
Results
Human embryonic stem cell genes OCT4, NANOG, STELLAR, and GDF3 are expressed in both seminoma and breast carcinoma. ( Ezeh et al 2006 )
Breast cancer geneset found at p-value: 0.008
Implementation Details
Young Lab Error model for chIP-chip data Analysis
Motif finding Algorithm in MATLAB Implemented Markov Model Implemented ZOOPS Model Integrated SVM Toolbox ( by S. R. Gunn.) with code
Used structural prior from MacIsaac, et.al. 2006
Used software for GSEA for Functional Analysis.
Future Directions
Algorithm Better use of classification error. Maximize Likelihood in Bound + Minimizes Likelihood in
Unbound (Multi-objective Optimization using GAs) Biological Information: Distance from transcription site,
Conservation Integrating expression data Cross-species Motif search and functional analysis,
maybe using GO Terms Scoring Sequence length
Acknowledgments
Fraenkel Lab Young Lab Kenzie D. MacIsaac Dr. David Gifford (CSAIL) Dr. Richard Young (WIBR) Dr. Tommi Jaakkola (CSAIL)
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