Transcription factor binding site prediction in vivousing DNA sequence and shape features

Anthony Mathelier, Lin Yang, Tsu-Pei Chiu, Remo Rohs, andWyeth Wasserman

anthony.mathelier@gmail.com @AMathelier

REGSYSGEN2015 Nov. 17th

Centre for Molecular Medicine and Therapeutics

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Transcriptional regulation of gene expression

Histone octamer TFs

Enhancer

Promoters

RNA PolII

RNAtranscripts

TSS

Cohesin

DNA

Nucleosome

Regulatoryproteins

A. Mathelier, W. Shi, and

W.W. Wasserman, Trendsin Genetics, 2015.

I Transcription of genes is turned on/off thanks to transcriptionfactors (TFs).

I TFs bind to DNA at transcription factor binding sites (TFBSs).

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Modeling TFBS using position frequency matrices (PFMs)Known binding sites:

GTAACAATGTAAACATGTAAACAAGTAAACAAGTAAACATGTAAACAAGTAAACACGTCAACAGGTAAACATGTAAACAAGTAAACATTTAAGTAAATAAACAACTAAACAGGTAAACATGTAAACAAGTAAACATGTAAACACGTAAACATGTAAACAG

Position Frequency Matrix:

A [ 10 0 190 210 180 15 210 70]C [ 10 0 20 0 15 180 0 25]G [175 0 0 0 15 0 0 35]T [ 15 210 0 0 0 15 0 80]

PFMs - PWMs

Classically, position weight (PWMs) are derived from PFMs tomodel TFBSs, assuming nucleotide independence within TFBSs.

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Modeling TFBS using Transcription Factor Flexible Models

>HNF4A 1...AGTTCAAAGTTCA...>HNF4A 2...AGTCCAAAGTTCA... ...>HNF4A 73554...CTTGGAACCGGGG...>HNF4A 73555...GGCAAGGTTCATA...

ChIP-seq sequences

TFFMs

positionn

...... 1

En

BG

bg/bg

bg/fg

E0

AAACAGAT

TATCTGTT

GAGCGGGT

CACCCGCT

position1

1

E1

AAACAGAT

TATCTGTT

GAGCGGGT

CACCCGCT

position2

1

E2

AAACAGAT

TATCTGTT

GAGCGGGT

CACCCGCT

AAACAGAT

TATCTGTT

GAGCGGGT

CACCCGCT

1

Logos

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1 2 3 4 5 6 7 8 9 10 11 12 13 140

1

2

bits

A. Mathelier and W.W. Wasserman, PLoS Computational Biology, 2013.

TFFMs

TFFMs model the sequence property of TFBSs from ChIP-seqdata by capturing successive dinucleotide dependencies.

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DNA shape features

The DNAshape tool predicts DNA shape features of a DNAsequence.Genome wide DNA shape features available on GBshape are:

I Minor Groove Width (MGW)

I Roll

I Propeller Twist (ProT)

I Helix Twist (HelT)

T. Zhou et al., Nucl. Acids Res., 2013.

T.P. Chiu et al., Nucl. Acids Res., 2015.

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Using DNA shape to model TFBSs

Studies showed DNA shapes importance to model TFBSs from:

I SELEX-seq experiments.

I Protein-binding microarray experiments.

I BunDLE-seq experiments.

N. Abe et al., Cell, 2015. T. Zhou et al., PNAS, 2015. M. Levo et al., Genome Res., 2015.

Aims of our study:I Construct computational models from large scale in vivo data

(ChIP-seq) by combining DNA sequence and shape features.

I Show TFBS prediction improvements on in vivo data.

I Analyze whether DNA shape induced improvements are TFfamily specific.

I Analyze position-specific DNA shape importance at TFBSs.

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Using DNA shape to model TFBSs

Studies showed DNA shapes importance to model TFBSs from:

I SELEX-seq experiments.

I Protein-binding microarray experiments.

I BunDLE-seq experiments.

N. Abe et al., Cell, 2015. T. Zhou et al., PNAS, 2015. M. Levo et al., Genome Res., 2015.

Aims of our study:I Construct computational models from large scale in vivo data

(ChIP-seq) by combining DNA sequence and shape features.

I Show TFBS prediction improvements on in vivo data.

I Analyze whether DNA shape induced improvements are TFfamily specific.

I Analyze position-specific DNA shape importance at TFBSs.

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Combining TFFMs and DNA shapes at TFBSs

hit score

MGW

ProT

Roll

HelT

Feature vector

We used an ensemble machine learning approach to combine DNAsequence and shape features.

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DNA shape features improve TFBS prediction in vivo

A B

Results on 400 human ENCODE ChIP-seq data sets

Combining TFFM scores and DNA shape features improve thediscriminative power. AUROC difference > 0.05 in 107 cases.

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DNA shape features are important for specific TF familiesB

C

Data sets from E2F and MADS-domain TF families are enrichedfor strong improvements when considering DNA shape features.

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Validation on independent plant MADS-domain TFs

Incorporating DNA shape features significantly improve TFBSprediction for plant MADS-domain TFs.

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ProT position-specific importance for MADS-domain TFs

AGL15

bits

1

2

A

B

ProT is of critical importance for predicting TFBSs associated toplant MADS-domain TFs in a position-specific manner.

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Conclusions

I Our analyses of ChIP-seq data reprensent the in vivoconterpart of the published in vitro studies.

I We can construct computational models combining DNAsequence and shape features from ChIP-seq data to improveTFBS prediction in vivo.

I Incorporating DNA shape information is most beneficial whenapplied to the E2F and MADS-domain TF families.

I ProT is critical for MADS-domain TF binding specificity in aposition-specific manner.

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Acknowledgements

I Wyeth Wasserman

I Remo Rohs

I Lin Yang

I Tsu-Pei Chiu

I Francois Parcy

I Oriol Fornes

I Chih-Yu Chen

Centre for Molecular Medicine and Therapeutics

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hit score

MGW

ProT

Roll

HelT

Feature vector

A B

C

Thank you

AGL15

bits

1

2

A

B

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