Transcription factor binding site prediction in vivo using DNA sequence and shape features

Transcription

1 Transcription factor binding site prediction in vivo using DNA sequence and shape features Anthony Mathelier, Lin Yang, Tsu-Pei Chiu, Remo Rohs, and Wyeth REGSYSGEN 2015 Nov. 17th Centre for Molecular Medicine and Therapeutics 1

2 Transcriptional regulation of gene expression Histone octamer TFs Enhancer Nucleosome RNA transcripts Cohesin TSS A. Mathelier, W. Shi, and W.W. Wasserman, Trends in Genetics, DNA RNA PolII Regulatory proteins Promoters Transcription of genes is turned on/off thanks to transcription factors (TFs). TFs bind to DNA at transcription factor binding sites (TFBSs). 2

3 Modeling TFBS using position frequency matrices (PFMs) Known binding sites: GTAACAAT GTAAACAT GTAAACAA GTAAACAA GTAAACAT GTAAACAA GTAAACAC GTCAACAG GTAAACAT GTAAACAA GTAAACAT TTAAGTAA ATAAACAA CTAAACAG GTAAACAT GTAAACAA GTAAACAT GTAAACAC GTAAACAT GTAAACAG Position Frequency Matrix: A [ ] C [ ] G [ ] T [ ] PFMs - PWMs Classically, position weight (PWMs) are derived from PFMs to model TFBSs, assuming nucleotide independence within TFBSs. 3

4 Modeling TFBS using Transcription Factor Flexible Models >HNF4A 1...AGTTCAAAGTTCA... >HNF4A 2...AGTCCAAAGTTCA >HNF4A CTTGGAACCGGGG... >HNF4A GGCAAGGTTCATA... ChIP-seq sequences AA CA GA TA AA CA GA TA AC CC GC TC AC CC GC TC AG CG GG TG AG CG GG TG AT CT GT TT AT CT GT TT E0 E1 bg/fg position 1 BG 1 E2 position bg/bg TFFMs AA CA GA TA AA CA GA TA AC CC GC TC AC CC GC TC AG CG GG TG AG CG GG TG AT CT GT TT AT CT GT TT... 1 En position n bits Logos A. Mathelier and W.W. Wasserman, PLoS Computational Biology, TFFMs TFFMs model the sequence property of TFBSs from ChIP-seq data by capturing successive dinucleotide dependencies. 4

5 DNA shape features The DNAshape tool predicts DNA shape features of a DNA sequence. Genome wide DNA shape features available on GBshape are: Minor Groove Width (MGW) Roll Propeller Twist (ProT) Helix Twist (HelT) T. Zhou et al., Nucl. Acids Res., T.P. Chiu et al., Nucl. Acids Res.,

6 Using DNA shape to model TFBSs Studies showed DNA shapes importance to model TFBSs from: SELEX-seq experiments. Protein-binding microarray experiments. BunDLE-seq experiments. N. Abe et al., Cell, T. Zhou et al., PNAS, M. Levo et al., Genome Res.,

7 Using DNA shape to model TFBSs Studies showed DNA shapes importance to model TFBSs from: SELEX-seq experiments. Protein-binding microarray experiments. BunDLE-seq experiments. N. Abe et al., Cell, T. Zhou et al., PNAS, M. Levo et al., Genome Res., Aims of our study: Construct computational models from large scale in vivo data (ChIP-seq) by combining DNA sequence and shape features. Show TFBS prediction improvements on in vivo data. Analyze whether DNA shape induced improvements are TF family specific. Analyze position-specific DNA shape importance at TFBSs. 6

8 Combining TFFMs and DNA shapes at TFBSs Feature vector hit score MGW ProT Roll HelT We used an ensemble machine learning approach to combine DNA sequence and shape features. 7

9 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 the discriminative power. AUROC difference > 0.05 in 107 cases. 8

10 DNA shape features are important for specific TF families B C Data sets from E2F and MADS-domain TF families are enriched for strong improvements when considering DNA shape features. 9

11 Validation on independent plant MADS-domain TFs Incorporating DNA shape features significantly improve TFBS prediction for plant MADS-domain TFs. 10

12 ProT position-specific importance for MADS-domain TFs A 2 AGL15 bits 1 B ProT is of critical importance for predicting TFBSs associated to plant MADS-domain TFs in a position-specific manner. 11

13 Conclusions Our analyses of ChIP-seq data reprensent the in vivo conterpart of the published in vitro studies. We can construct computational models combining DNA sequence and shape features from ChIP-seq data to improve TFBS prediction in vivo. Incorporating DNA shape information is most beneficial when applied to the E2F and MADS-domain TF families. ProT is critical for MADS-domain TF binding specificity in a position-specific manner. 12

14 Acknowledgements Wyeth Wasserman Remo Rohs Lin Yang Tsu-Pei Chiu François Parcy Oriol Fornes Chih-Yu Chen Centre for Molecular Medicine and Therapeutics 13

15 2 1 hit score A B Feature vector MGW ProT Roll C HelT Thank you AGL15 bits 14