SVM: Non-coding Neutral S equences V s Regulatory M odules

SVM: Non-coding Neutral Sequences Vs Regulatory

Modules

Ying Zhang, BMB, Penn StateRitendra Datta, CSE, Penn State

Bioinformatics – IFall 2005

Outline

Background: Machine Learning & Bioinformatics

Data Collection and Encoding

Distinguish sequences using SVM

Results

Discussion

Expression of genes are under regulation. Right protein, right time, right amount, right location…

Regulation: cis-element vs trans-element Cis-element: Non-coding functional sequence Trans-element: Proteins interact with cis-element

Predicting cis-regulatory elements remains a challenge: Significant effort put in the past Current trends: TFBS clusters, pattern analysis

Regulation: A Recurring Challenge

Alignments and Sequences: The Data

Information: Sequence Genetics information encoded in DNA sequence Typical information: Codon, Binding site, …

Codon: ATG (Met), CGT (Arg.), … Binding sites: A/TGATAA/G ( Gata1 ), …

Evolutionary Information: Aligned Sequence Similarity between species Conservation ~ Function

Human: TCCTTATCAGCCATTACC Mouse: TCCTTATCAGCCACCACC

Problem

Given the genome sequence information, is it possible to automatically distinguish Regulatory Regions from other genomic non-coding Neutral sequences using machine learning ?

Predicting Genes

Machine Learning: The Tool

Sub-field of A.I. Computers programs “learn” from

experience, i.e. by analyzing data and corresponding behavior

Confluence of Statistics, Mathematical Logic, Numerical Optimization

Applied in Information Retrieval, Financial Analysis, Computer Vision, Speech Recognition, Robotics, Bioinformatics, etc.

Statistics

Optimization Logic

M.L.

Analyzing Stocks

Personalized WWW search

Applications

Machine Learning: Types of Learning

Supervised Learning Learning statistical models from past

sample-label data pairs, e.g. Classification

Unsupervised Learning Building models to capture the inherent

organization in data, e.g. Clustering Reinforcement Learning

Building models from interactive feedback on how well the current model is doing, e.g. Robotic learning

Machine Learning and Bioinformatics: The Confluence

Learning problems in Bioinformatics [ICML ’03] Protein folding and protein structure prediction Inference of genetic and molecular networks Gene-protein interactions Data mining from micro arrays Functional and comparative genomics, etc.

Identification of DNaseI Hypersensitive Sites in the human genome (may disclose the location of cis-regulatory sequences) W.S. Noble et al., “Predicting the in vivo signature of human gene

regulatory sequences,” Bioinformatics, 2005. Functionally classifying genes based on gene expression data from

DNA microarray hybridization experiments using SVMs M. P. S. Brown, “Knowledge-based analysis of microarray gene

expression data by using support vector machines,” PNAS, 2004. Using Log-odds ratios from Markov models for identifying regulatory

regions in DNA sequences L. Elnitski et al., “Distinguishing Regulatory DNA From Neutral Sites,”

Genome Research, 2003. Selection of informative genes using an SVM-based feature

selection algorithm I. Guyon et al., “Gene selection for cancer classification using support

vector machines,” Machine Learning, 2002.

Machine Learning and Bioinformatics: Sample Publications

Machine Learning and Bioinformatics: Books

Support Vector Machines: A Powerful Statistical Learning Technique

Which of the linear separators is optimal?

Support Vector Machines: A Powerful Statistical Learning Technique

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Choose the one that maximizes the margin between the classes

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Support Vector Machines: A Powerful Statistical Learning TechniqueThe classes in these datasets linearly separate easily

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What about these datasets ?

0 x

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Support Vector Machines: A Powerful Statistical Learning TechniqueSolution: Kernel Trick !

x2

x

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Experiments: Overview

Classification in Question: Regulatory regions (REG) vs Ancestral Repeats (AR)

Two types of experiments: Nucleotide sequences – ATCG Alignments (reduced 5-symbol) - SWVIG

(S: match involving G & C, W: match involving A & T, G:gap V:transversion, I: transition)

Two datasets: Elnitski et al. dataset Dataset from PennState CCGB

Mapping Sequences/Alignments → Real Numbers Frequencies of short length K-mers (K=1, 2, 3) Normalizing factor - sequence length (Ambiguous for K > 1) Stability of variance – Equal length sequences (whenever possible)

Total number of features: Sequences: 4 + 42 + 43 = 84 Alignments: 5 + 52 + 53 = 155

Relatively high-dimensionality: Curse of dimensionality: Convergence of estimators very slow Over-fitting: Poor generalization performance

Solutions: Dimension Reduction – e.g., PCA Feature Selection - e.g., Forward Selection, Backward Elimination

Experiments: Feature Selection

Training Set: Elnitski et al. dataset Sequences: 300 samples of 100 bp each class (REG and AR) Alignments: 300 samples of length 100 from each class

SVM setup: RBF Kernel: k(x1, x2) = exp( δ || x1 – x2 || ) Implementation: LibSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)

Validation: N-fold Cross-validation Used in feature selection, parameter tuning, and testing

Experiments: Training and Validation

Results: The Elnitski et al. dataset

Parameter selection SVM Parameters: δ and C

Feature Selection Assessing Feature Importance G-C Normalization Sequences: 10 out of 84 Symbols: 10 out of 155

Accuracy scores Overall Ancestral Repeats (AR) Regulatory Regions (Reg)

Results: SVM Parameter Selection

Iterative selection procedure Coarse selection – Initial neighborhood Fine-grained selection - Brute force

Validation Set from data Within-loop CV Chosen Parameters:

δ = 1.6 C = 1.5

Results: Feature Selection - Sequence

Distribution of Nucleotide frequencies of the top 9 most significant k-mers

Cho

sen

by O

ne-d

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sion

al S

VMs

Results: Feature Selection - Symbol

Distribution of 5-symbol frequencies of the top 9 most significant k-mers

Cho

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by O

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sion

al S

VMs

Results: Feature Selection

Procedure: Greedy Forward Selection + Backward Elimination

Chosen Features: Sequence: [5 68 3 20 63 4 16 10 1 22]

( 0 = A, 1 = T, 2 = G, 3 =C, 4 = AA, 5 = AT, etc. ) [AT,CAA,C,AAA,GGC,AA,CA,TG,T,AAG]

Symbol: [3 5 4 18 24 124 17 143 19 95 103] ( 0 = G, 1 = V, 2 = W, 3 =S, 4 = I, 5 = GG, 6 = GV, etc. ) [S, GG, I, WS,SI,SIG,WW,IWI,WI,WSV,WII]

Results: Accuracy Scores

Experiment Type Overall Accuracy

Reg Precision

AR Precision

Elnitski et al. 5-symbolHexamers

≈ 74.7%≈ 75%

78.49%81.4%

73%72.5%

Sequences only 1-mers2-mers3-mersSelection

78.33%77.67%80.17%80.33%

76.54%72.84%83.67%80.87%

80.5482.97%77.21%79.63%

Symbols only 1-mers2-mers3-mersSelection

84.33%84.33%85.17%86.00%

79.39%77.53%78.83 %80.58%

90.03%90.96%92.42%91.54%

Results: Laboratory Data Training:

SVM models built using Elnitski et al. data Same parameters; Same features selected

Data: 9 candidate cis-regulatory regions predicted by RP score 1: negative control based on the definition. 5 of the 9 candidates passed current biological testing,positive

Accuracy Classification result for sequence (1-, 2-, 3-mer):

1 negative control 4 out of 5 positive element + 3 out of 4 “negative” element

Classification result for alignment (1-, 2-, 3-mer): 1 negative control 9 original candidates

Discussion

High validation rate for Ancestral repeat The structure of selected training set is not that diverse Ancestral repeat tends to be AT-rich AR: LINE, SINE etc.

SVM performs a little better than RP scores in training set Statistically more powerful

RP: Markov model for pattern recognition SVM: Hyper-plane in high-dimensional feature space

Feature selection using wrapper method possible

Discussion (cont’d) Performance degradation in Lab Data classification

No improvement in SVM classification compared to RP score Features identified from the Elnitski et al. data may have some bias – other

features may be more informative on the Lab data

Sequence classification vs Alignment (Accuracy Table) SVM yields higher overall cross-validation accuracy for aligned symbol

sequences compared to nucleotide sequences Gained accuracy rate: Ancestral Repeat driven

No improvement for aligned symbol sequence In Lab data classification, sequence classification is better than aligned

symbol sequence No information gained from evolutionary history !!!

Alphabet reduction not optimal Assumption worng!!!

Summary

Generally, SVM is a powerful tool for classification Performance better than RP in distinguishing AR training set from

Reg training set

SVM: answer “yes or no” question RP: Probabilistic method, can generate quantitative measurement

genome-wide SVM: Results can be extended using probabilistic forms of SVM

SVM can reveal potentially interesting biological features e.g. the transcription regulation scheme

Explore more complex features Refine models for neutral non-coding genomic segments Utilize multi-species alignment for the classification Combining sequence and alignment information to build

more robust multi-classifiers – “Committee of Experts” Pattern recognition for more accurate prediction

Future Directions: Possible extensions

Questions and recommendations?

Using original alignment features, 20 columns.

Other lab data (avoiding the possible bias of RP preselection) for SVM performance testing.

References L. Elnitski et al., “Distinguishing Regulatory DNA From Neutral

Sites,” Genome Research, 2003. Machine Learning Group, University of Texas at Austin, “Support

Vector Machines,” http://www.cs.utexas.edu/~ml/ . N. Cristianini, “Support Vector and Kernel Methods for Pattern

Recognition,” http://www.support-vector.net/tutorial.html.

Acknowledgement

Dr. Webb Miller Dr. Francesca Chiaromonte David King