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Predicting the human epigenome from DNA motifs

Nature Methods volume 12, pages 265–272 (2015)Cite this article

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Abstract

The epigenome is established and maintained by the site-specific recruitment of chromatin-modifying enzymes and their cofactors. Identifying the cis elements that regulate epigenomic modification is critical for understanding the regulatory mechanisms that control gene expression patterns. We present Epigram, an analysis pipeline that predicts histone modification and DNA methylation patterns from DNA motifs. The identified cis elements represent interactions with the site-specific DNA-binding factors that establish and maintain epigenomic modifications. We cataloged the cis elements in embryonic stem cells and four derived lineages and found numerous motifs that have location preference, such as at the center of H3K27ac or at the edges of H3K4me3 and H3K9me3, which provides mechanistic insight about the shaping of the epigenome. The Epigram pipeline and predictive motifs are at http://wanglab.ucsd.edu/star/epigram/.

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Figure 1: Identifying motifs that are predicative of epigenomic modifications.
Figure 2: Predicting epigenomic modification from DNA motifs.
Figure 3: The specificities of interplay between DNA motifs and the epigenome.
Figure 4: Predictive motifs have location preferences.
Figure 5: De novo motif disruption and H3K27ac levels are correlated.

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Acknowledgements

This work was partially supported by the US National Institutes of Health (U01 ES017166 to W.W., principal investigator, B. Ren). The authors wish to thank B. Ren, D.R. Westhead and M.H. Sherman for discussion of this work. We are grateful to M. Snyder for providing the SNP data of the 19 individuals.

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Author notes

  1. John W Whitaker

    Present address: Present address: Research & Development IT, Janssen Pharmaceutical of Johnson & Johnson, San Diego, California, USA.,

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, USA.,

    John W Whitaker, Zhao Chen & Wei Wang

  2. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California, USA.,

    Wei Wang

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Contributions

J.W.W. and W.W. conceived of and designed the project, J.W.W. performed all the analyses, Z.C. contributed to data analysis, W.W. analyzed the data, and J.W.W. and W.W. wrote the manuscript.

Corresponding author

Correspondence to Wei Wang.

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Integrated supplementary information

Supplementary Figure 1 Cross-validation procedures used to assess the prediction of epigenome from DNA motifs.

A schematic describes the different cross-validation procedures that were used to validate our predictions. In the top left the standard five-fold cross-validation procedure is shown. Beneath we show how shuffling of test sequences is carried out during cross-validation. This is done to ensure all the prediction performance is coming from the motifs and not simple sequence biases, such as GC-content. In the top right we show how sequences that are removed during SSB are still tested by training the model on the entire set of sequences that were selected during SSB.

Supplementary Figure 2 The effect of SSB on prediction performance.

The left ROC curve shows the prediction performance when the SSB step is carried out. The right ROC curve shows the performance on the same dataset but with the omission of the SSB step.

Supplementary Figure 3 The levels of histone modification ChIP-seq reads inside peaks and background regions in H1.

The bar plot shows the AUC from the ‘single mark analysis’ in H1. The violin plots show the normalized read counts (ChIPRPKM – inputRPKM) of ChIP-seq reads for each of the modifications. The levels are shown both inside and outside the modification peaks. The read counts were adjusted by region size and total reads. Then the corresponding input score was subtracted. The violin plots with dotted borderlines show the enrichment within modification peaks while the violin plots with solid borderlines show enrichment outside modifications peaks.

Supplementary Figure 4 Controls to confirm DNA motifs are predictive of histone modification.

(a) As Fig. 2a–b but showing the results of the ‘mark-specific analysis’. In the left hand schematic different colored stars represents different histone modifications. (b) As Fig. 2a–c but showing the results of the ‘typical background analysis’. (c) As Fig. 2a–b but showing the results of the ‘cell-type-specific analysis’. In the left hand schematic pink represents the epigenome of a different cell-type. The middle plot shows the H1 and MSC comparison. (d) Shows the average AUC for each histone modification in each of the four analyze.

Supplementary Figure 5 Comparison of cell type–specific analyses.

The average cell-type-specific prediction performance for each mark is shown. On the left are the results from comparing H1 to each of the four derived cell-types. On the right in the performance when H1 is compared to IMR90 and seven ENCODE cell-types (A549, CD14+, GM12878, HeLa, HepG2, HUVEC and K562).

Supplementary Figure 6 Combined clustering of cell type– and modification–specific interplay between DNA motifs and the epigenome.

The heat map shows the clustering of 589 motif groups. The dendrogram shows the motif groups clustered by their interplay with epigenomic modification and cell-type. Both motifs that are enriched and depleted from modification peaks are shown. On the side of the heat map furthest from the dendrogram, a histogram shows the number of motifs in each cluster. Furthest from the dendrogram are two plots that show the locations of: (i) motif groups that contain both positive and negative interplay for the same modification (ii) the locations of motif groups that correspond to certain groups proteins that share DNA-binding motifs.

Supplementary Figure 7 A modification-specific summary of interplay with DNA motifs.

Schematics represent the types of interplay that were identified for each of the histone modifications. Pie charts represent the proportion of a modifications predictive motifs that are unique to that modification or that overlap with other modifications.

Supplementary Figure 8 Overview of the motif location preference profiles.

A motifs profile is constructed using the complete (pre-SSB) set of sequences that were identified for that particular cell-type, modification and analysis type.

Supplementary Figure 9 Sequence-set balancing (SSB).

The figure illustrates the SSB process. (a) The sequences from each set are separately binned by region length and GC-content. In the figure only a subset of the bins are shown: region lengths from 500-700bps and GC-content from 45-47%. (b) Bins with uneven numbers are highlighted in red. (c) Sequences are randomly removed from bins that possess more sequences than their corresponding bin in the other set.

Supplementary Figure 10 Overview of Epigram.

(a) A general overview of the Epigram workflow is shown. (b) On the left 9-mers that differ by from the seed (show at the top 9-mer) by one or two positions are aligned. Differing positions are highlighted in red. Adjacently to the right of the 9-mers are their weight scores (W). On the right of the arrow a PWM is produced from the alignment. When making the PWM the 9-mers are weighted by their W scores. (c) The alignment shown in (b) is expanded by one position.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Note (PDF 4459 kb)

Supplementary Data 1

Comparison of YY1 ChIP-seq peaks to the ChIP-seq peaks of other factors (XLSX 14 kb)

Supplementary Data 2

A list of datasets that were used in this study (XLSX 16 kb)

Supplementary Data 3

The motif with the greatest information content from each of the 589 groups (meme format) (TXT 170 kb)

Supplementary Data 4

Annotation of the de novo motif groups with known motifs (XLSX 75 kb)

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Whitaker, J., Chen, Z. & Wang, W. Predicting the human epigenome from DNA motifs. Nat Methods 12, 265–272 (2015). https://doi.org/10.1038/nmeth.3065

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