Transfer String Kernel for Cross-Context Sequence Specific - - PowerPoint PPT Presentation

Transfer String Kernel for Cross-Context Sequence Specific DNA-Protein Binding Prediction

by Ritambhara Singh IIIT-Delhi June 10, 2016

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Biology in a Slide

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DNA RNA PROTEIN CELL ORGANISM

DNA and Diseases

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DNA RNA PROTEIN CELL ORGANISM

• Down Syndrome
• Parkinson’s Disease
• Autism
• Muscular Atrophy
• Sickle Cell Disease

………. ………..

Transcription Factors

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DNA RNA PROTEIN CELL ORGANISM

Gene Transcription Factor Transcription Factor Binding Site

ATCGCGTAGCTAGGGATGACAGACACACATAATTCTAGATA ¡

ChIP-seq Maps TF binding

5 Transcription Factor Gene Genome

ATATCGTATCTTTTAAACCGGGTTGGCCACTAGA ¡ ATATCGTATCTAAACCGCCTCGG ¡

ChIP-seq Map for TF Peak Transcription Factor Binding Site

CHIP-SEQ

DNA

TF Binding Differs Across Contexts

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ATATCGTATCTTTTAAACCGGGTATGTAATGCAT ¡ ATATCGTATCTAAACCGCCCGTGT ¡ ATATCGTATCTTTTAAACCGGGTTGGCCAGTATA ¡ ATATCGTATCTAAACCGCCCTGCA ¡

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? ?

(Blood Cell) (Stem Cell) (Leukemia) (Lung Cancer) (Cervical Cancer) (Nerve Cell) (Immunity related)

Current Challenge: ENCODE Data Gap

Source : http://genome.ucsc.edu/ENCODE/dataMatrix/encodeChipMatrixHuman.html

Case for Computational Tools

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Existing Computational Tools

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Generative Approaches Discriminative Approaches

MEME CISFINDER STRING KERNEL+SVM

Generative : PWM Based approach

10 Genome

ATATCGTATAACAATAACCGGGAACTAATAGC ¡ ATATCGTATCTAACAAATCCTACT ¡

ChIP-seq Map for TF Peak Sequence Logo

1 2 3 4 5 6 7 8 9 10 11 12 A 14 14 28 40 9 45 42 13 15 9 T 12 3 4 12 11 10 9 6 5 38 12 3 C 3 1 8 2 2 36 2 2 1 G 1 16 10 1 2 3 2 7 11

Position Weight Matrix

Genome

ATATCGTATCTTTTAAACCGGGTTGGCCAATAGC ¡ ATATCGTATCTAAACCGCCCTACT ¡

? ?

Generative Approach : Output

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Source : http://www.cbil.upenn.edu/EpoDB/release/version_2.2/meme/meme-output.html#sample

Generative Approach: Limitations

– Output: Long list of potential TFs – Work well for only well preserved motifs or large training datasets – PWMs for all ~2000 TFs not available – Lower prediction performance than discriminative approaches

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Genome

ATATCGTATCTTTTAAACCGGGTTGGCCAATAGC ¡ ATATCGTATCTAAACCGCCCTACT ¡

? ?

ATATCGTATCTTTTAAACCGGGTTGGCCAATAGC ¡

Peak

ATATCGTATCTAAACCGCCCTACT ¡

Genome

+1

• 1

Discriminative Approach : Output

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Discriminative : String Kernel Approach

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Support Vector Machine

Discriminative Approach : Limitation

Assumption: Training/test data follow same distribution regardless of context.

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Aim

• Improve prediction of Transcription Factor

Binding sites across contexts using knowledge transfer.

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Proposed Solution : Cross-Context Knowledge Transfer

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Transfer String Kernel : Overview

Feature Conversion Feature Conversion Knowledge Transfer Classification Source Context Target Context Training (KMM)

ATCGAT GTATAC ATACAT GCTTAC

Xs Xt

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Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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String Kernel : Spectrum Kernel

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Feature map indexed by all k-length subsequences (“k-mers”) from alphabet Σ of amino acids, |Σ|=20

String Kernel : Mismatch Kernel

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For k-mer s, the mismatch neighborhood N(k,m)(s) is the set of all k-mers t within m mismatches from s.

Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Support Vector Machine

24 Negative Instances (y = -1) Positive Instances (y = +1)

w . x + b ≤ -1 w . x + b ≥ +1 w . x + b = 0

Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Transfer Learning (KMM)

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True densities Ratios

ptr(x) pte(x) r(x) r(x)

Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Importance Re-weighting

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Original Weights KMM Weights

Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Transfer String Kernel (TSK)

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Feature Conversion! Feature Conversion! Knowledge Transfer! Classification! Source! Context! Target! Context! Training!

ATCGATCG ATCGATCG% CCCGATCG CTCGCTCC%

Mismatch String Kernel Mismatch String Kernel Kernel Mean Matching (KMM) Importance Re- weighting SVM

Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Experimental Setup

• 14 Transcription Factors (ENCODE ChIP-seq)
• Top 1000 positive sequences (500 training and

500 testing)

• 1000 random negative sequences
• Hyper-parameter tuning for k=(8,10,12) and

m=(1,2,3)

• Dictionary size = 4 {A,T,C,G}

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Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Results

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Results – Cross Context

0.8 0.82 0.84 0.86 0.88 0.9 Sin3a Max Mxi1 Chd2 Ctcf

AUC Score Transcription Factors

TSK SK

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Outline

• Method

– String Kernel – Support Vector Machine – Transfer Learning (KMM) – Importance re-weighting – Transfer String Kernel

• Evaluation

– Experimental Setup – Cross-context TFBS prediction – Cross-context Protein Binding prediction

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Results – Cross context

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Summary

• TSK overall improves the cross-context TFBS predictions;
• String kernel based approaches perform better than the state-of-

the-art Position Weight/Frequency Matrix based TFBS tools;

• TSK approach is generalizable for performance improvement of

any cross-context sequence prediction task. Presented in BIOKDD ’15

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Acknowledgements

• Dr. Mazhar Adli

Adli Lab : Department of Biochemistry and Molecular Genetics @Uva Nipun Batra IIIT-Delhi

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Machine Learning Lab @ UVa

• Dr. Yanjun Qi

(Advisor) Jack Lanchantin Beilun Wang Weilin Xu Ji Gao

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Future Directions

• Deep Learning :

– Gene expression prediction using histone modification data (ECCB 2016) – Improving TFBS prediction using DNA sequences (ICLR Workshop 2016, ICML Workshop 2016)

• String Kernels: Improving efficiency!! (on-going

work)

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Thank You

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