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Base-resolution models of transcription-factor binding reveal soft motif syntax

Nature Genetics volume 53, pages 354–366 (2021)Cite this article

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Abstract

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

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Fig. 1: BPNet predicts ChIP–nexus signal at base resolution.
Fig. 2: TF motifs and their genomic instances can be accurately derived from BPNet using interpretation tools.
Fig. 3: Discovery of composite motifs and indirect binding footprints.
Fig. 4: In silico motif interaction analysis reveals TF cooperativity and motif syntax.
Fig. 5: Pervasive helical periodicity between Nanog and partner motifs.
Fig. 6: CRISPR mutations in a Sox2 and a Nanog motif validate BPNet predictions.

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Data availability

The raw sequencing data are available from GEO under accession number GSE137193. Data used to train, evaluate and interpret the BPNet models are found on zenodo at https://doi.org/10.5281/zenodo.3371215. Trained BPNet models and all the model interpretation results are on zenodo at https://doi.org/10.5281/zenodo.3371163. The BPNet model trained on ChIP–nexus data is available on Kipoi under the name BPNet-OSKN (http://kipoi.org/models/BPNet-OSKN/). Genome browser tracks showing observed/predicted ChIP–nexus signal and contribution scores for all factors are available at https://genome.ucsc.edu/s/mlweilert/mesc_OSKN_tracks. ATAC-seq data in mouse ESCs used in Fig. 2 and Supplementary Fig. 7 were obtained from GSE134680. Blacklisted regions used to filter genomic coordinates throughout the analysis are available at https://www.encodeproject.org/files/ENCFF547MET. RepeatMasker mm10 annotations were obtained from http://www.repeatmasker.org/genomes/mm10/RepeatMasker-rm405-db20140131/mm10.fa.out.gz. The nuclear magnetic resonance structure 1O4X used to render Sox2 and Oct1 in Fig. 3 is available at https://www.rcsb.org/structure/1o4x. TRANSFAC (v.7.0) was used to identify the TFIIIC B-box discussed in Fig. 3. The PH0134.1 Pbx PWM used for motif validation in Supplementary Fig. 8 and Extended Data Fig. 5 was obtained from JASPAR at http://jaspar.genereg.net/api/v1/matrix/PH0134.1.jaspar. The MA0141.1 Esrrb PWM used in Extended Data Fig. 5 was obtained from JASPAR at http://jaspar.genereg.net/api/v1/matrix/MA0141.1.jaspar. The transfer RNA database GtRNAdb (v.2.0, release 17.1) annotations and associated tRNAscan-SE scores used in Extended Data Fig. 5 were obtained from http://gtrnadb.ucsc.edu/GtRNAdb_archives/release17/genomes/eukaryota/Mmusc10/mm10-tRNAs.tar.gz. Source data are provided with this paper.

Code availability

The BPNet software package is available at https://github.com/kundajelab/bpnet/. Code to reproduce the results is available at https://github.com/kundajelab/bpnet-manuscript (https://doi.org/10.5281/zenodo.4294813). The ChIP–nexus data processing pipeline is available at https://github.com/kundajelab/chip-nexus-pipeline. Software to trim and deduplicate ChIP–nexus reads is available at https://github.com/Avsecz/nimnexus/.

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Acknowledgements

We thank M. Levine and R. Krumlauf for comments and J. Israeli for initial technical help. This work was funded by the Stowers Institute for Medical Research (SIMR), NIH grant no. R01HG010211 to J.Z. and NIH grant nos. DP2GM123485, U01HG009431 and R01HG009674 to A.K. Ž.A. was supported by the German Bundesministerium für Bildung und Forschung through the project MechML (no. 01IS18053F). A.S. was supported by the Stanford BioX Fellowship and HHMI International Student Research Fellowship. Illumina sequencing was performed at SIMR (A. Perera and M. Peterson) and the University of Kansas Medical Center Genomics Core, supported by NIH grant nos. U54HD090216, S10OD021743 and COBRE P30GM122731. Generation of CRISPR/Cas9 mouse ESC lines was performed by the following cores at SIMR: Genome Engineering (K. Delventhal, B. Miller and K. Weaver), Tissue Culture (C. Zhao, A. Murray, Y. Wang, O. Kenzior, Q. Jiang, S. Hime and S. Gosh) and Cytometry (J. Haug and D. DeGraffenreid).

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  1. Žiga Avsec

    Present address: DeepMind, London, UK

Authors and Affiliations

  1. Department of Informatics, Technical University of Munich, Garching, Germany

    Žiga Avsec & Julien Gagneur

  2. Graduate School of Quantitative Biosciences, Ludwig-Maximilians-Universität München, Munich, Germany

    Žiga Avsec

  3. Stowers Institute for Medical Research, Kansas City, MO, USA

    Melanie Weilert, Sabrina Krueger, Khyati Dalal, Robin Fropf, Charles McAnany & Julia Zeitlinger

  4. Department of Computer Science, Stanford University, Stanford, CA, USA

    Avanti Shrikumar, Amr Alexandari & Anshul Kundaje

  5. The University of Kansas Medical Center, Kansas City, KS, USA

    Khyati Dalal & Julia Zeitlinger

  6. Department of Genetics, Stanford University, Stanford, CA, USA

    Anshul Kundaje

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Contributions

Ž.A., A.K. and J.Z. conceived the project. Ž.A., A.S., A.K. and J.Z. conceived and implemented the computational methods. S.K., K.D. and R.F. performed the experiments. Ž.A., M.W., A.A. and C.M. performed further computational analysis. J.Z., A.K. and J.G. supervised the project. Ž.A., M.W., S.K., J.G., A.K. and J.Z. prepared the manuscript with input from all authors.

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Correspondence to Anshul Kundaje or Julia Zeitlinger.

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J.Z. owns a patent on ChIP–nexus (no. 10287628). All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Additional performance evaluation of BPNet’s predictions of ChIP-nexus data.

a, Observed and predicted ChIP-nexus read counts mapping to the forward strand (dark) and the reverse strand (light) for the Zfp281 and Sall1 enhancers located on the held-out (test) chromosome 1. b, Alternative profile shape evaluation metrics showing the difference to random predictions: multinomial negative log-likelihood and Jensen-Shannon (JS) divergence. Both metrics were computed at different resolutions (from 1 bp to 10 bp windows) in held-out test chromosomes 1, 8 and 9. c, auPRC of profile predictions is high across various learning rates on the tuning set chromosomes 2, 3 and 4, demonstrating the robustness of the model. d, The deconvolutional layer slightly improves the profile predictive performance compared to a point-wise convolutional layer (deconvolution size=1). e, auPRC of profile predictions (top) and the Spearman correlation of total count predictions (bottom) for a range of different relative total count weight α in the BPNet loss function parameterized as λ = α/2 n_obs. Relative weight of 1 (center) denotes equal weighting of the counts and profile loss functions. The best performance is obtained for α<1, showing that putting more weight to profile predictions aids both profile and count predictions. f, Observed and predicted total read counts for BPNet (top) and replicate experiments (bottom) across the four studied TFs along with the Spearman correlation coefficient.

Extended Data Fig. 2 Removal of long motifs in retrotransposons and clustering of motifs by similarity.

a, Among all motifs discovered by TF-MoDISco, 18 motifs display unusually high information content (IC) of >30 bits (green). The expected short motifs are shown in gray. b, Histogram of the overlap of short motifs (gray) and long motifs (green) with repeat elements shows that long motifs overlap >80% with annotated retrotransposons. c, Long motifs with their PFM, ID, fraction of motif instances overlapping with a repeat and the most frequent (top class) RepeatMasker annotation. Highlighted within the repeat elements are potential motif instances of Oct4-Sox2, Sox2, Nanog and Klf4 as indicated by the CWMs. d, To identify a set of representative motifs from the 33 short motifs discovered for different TFs (information content <30 bit, shown in Supplementary Fig. 3) and remove redundant short motifs, motifs were clustered by similarity using hierarchical clustering. The results were then manually inspected to select clusters that separate known motifs that are distinct (for example Oct4-Oct4 resembles the known MORE and PORE motifs that bind Oct4 homodimers, which is different from the monomerically bound Oct4 motif). Among very similar motifs within a cluster, we then selected the most abundant motif that was discovered for the most relevant TF (if known). The 11 representative motifs that we selected are shown on the left. Non-canonical motifs were given a name (Nanog-alt for Nanog alternative, Klf4-long for longer Klf4).

Extended Data Fig. 3 BPNet and TF-MoDISco outperform traditional methods in motif discovery and the mapping of motif instances.

a, Motifs discovered by ChExMix, HOMER and MEME for Oct4, Sox2, Nanog and Klf4 ChIP-nexus peaks that are closest to the 11 primary representative BPNet motifs (top row). Green checkmark denotes whether the discovered motif is similar to the BPNet motif. b, Number of motif instances located up to 500 bp (top) or 100 bp (bottom) away from the ChIP-nexus peak summits showing a strong ChIP-nexus footprint. Only motif instances in peaks from held-out test chromosomes (1, 8 and 9) were used for the evaluation. (x-axis) top N motif instances from each of the methods were sorted in descending order of scores (PWM log odds score or CWM contrib score). For BPNet-augm, the center of the genomic region for which the contribution scores were computed was randomly jittered up to 200 bp away from the peak summit. This augmentation prevents BPNet from using the positional information of the peak summit. In the final column (Nanog replicate), the Nanog ChIP-nexus footprint was measured by a separate biological replicate using a different antibody (ɑ-Nanog from Abcam, ab214549), which was not used during training or evaluation.

Extended Data Fig. 4 BPNet training on ChIP-nexus profiles is faster and yields more accurate motif instances than a binary classification model.

a, Predictive performance as measured by the precision-recall curve of the binary classification models predicting the presence or absence of ChIP-nexus peaks from 1 kb DNA sequences evaluated across the held-out (tuning/validation) chromosomes 2, 3 and 4. The model trained to classify the sequences is outperformed when the model is trained to also predict the ChIP-nexus profiles from DNA sequence (without or without profile bias-correction) in addition to classifying them is shown in blue (without or without profile bias-correction) in light blue and with bias-correction in dark blue). b, Training time of the binary classification model trained genome-wide and the sequence-to-profile model (BPNet) trained in ChIP-nexus peaks. c, Detected motifs by TF-MoDISco using the contribution scores in ChIP-nexus peaks of the sequence-to-profile BPNet (profile reg.) or the binary classification model (binary class). A light color denotes a high number of seqlets for each motif. Motifs not discovered or motifs supported by less than 100 seqlets are shown in black. Questionable motifs are displayed separately on the right. d, The number of motif instances (500 bp within ChIP-nexus peak summit) showing a ChIP-nexus footprint (y-axis) within the top N motif instances with highest contribution scores (x-axis) from the held-out (test) chromosomes 1, 8 and 9. A site was considered to show a ChIP-nexus footprint if the number of reads at the position of the aggregate footprint summit (averaged across both strands) is higher than the 90th percentile value of all motif instances detected by the profile regression model for the corresponding TF (that is same as in Extended Data Fig. 3b).

Extended Data Fig. 5 Strict motif spacings are found on retrotransposons and indirectly bound motifs can be validated.

a, To show that TF binding occurs with strict spacings in retrotransposons and that this is likely ancestral, the RLTR9E N6 motif is shown as an example. Sequences of the individual instances in the genome were sorted by the Kimura distance from the consensus motif, with the most similar sequences on top (which are likely more ancestral). Nanog, Sox2 and Klf4 ChIP-nexus binding footprints are shown in the same order on the right (+ strand reads in red, - strand reads in blue), revealing that the binding site spacing is largely constant across all sequences. b, Analysis of the most frequent distances between motif pairs (with >500 co-occurrences, distance measured at the trimmed motifs’ centers). The top 1% most frequent distances mapped in 83% to ERVs and were often longer than 20 bp. c, To validate the identified Zic3 motif instances, Zic3 ChIP-nexus experiments were performed. The average signal across the Zic3 instances reveals a strong Zic3 binding footprint. d, A similar validation was performed for the Esrrb motif instances, revealing that the Esrrb ChIP-nexus signal is present but more diffuse at the discovered Esrrb motif instances. e, To better understand the binding of Oct4 to the B-box, which is frequently found in tRNA, tRNA-overlapping B-box motif instances were reoriented to match the transcriptional direction and sorted by tRNA gene start proximity. This reveals Oct4 binding at tRNA gene start/stop sites. f, Amino acid anti-codons and their copy count of the tRNAs that overlapped with the B-box motif instances.

Extended Data Fig. 6 Additional genomic in-silico interaction analyses confirm the directional effects.

a, Example genomic in-silico mutagenesis analysis at the distal Oct4 enhancer. Predicted ChIP-nexus profiles and the contribution scores greatly decrease at both motifs (Oct4-Sox2 and Nanog) when erasing the Oct4-Sox2 motif (through random sequence insertion). By contrast, when the Nanog motif is erased (right), the predicted profile and the contribution scores of Oct4-Sox2 motif remain intact. b, Such directional effect of motifs can be quantified by the corrected binding fold change (Supplementary Fig. 10a) for all motif pairs in the genome and visualized as a scatterplot. c, Example scatterplot for the interaction between Sox2 and Nanog. Sox2 shows a positive directional effect on Nanog most profound for short motif distances (<35 bp). d, Predicted binding fold changes for all motif pairs in genomic sequences.

Extended Data Fig. 7 Helical periodicity of Nanog motifs is not discovered with traditional methods and requires BPNet’s large receptive field.

a, The pairwise spacing of Nanog motif instances located up to 100 bp away from the ChIP-nexus peak summits in all possible strand orientations (rows) for different methods and/or thresholds (columns). Results for all chromosomes are shown. b, The pairwise spacing of Nanog motif instances when BPNet is trained with different numbers of convolutional layers (Fig. 1g). BPNet with only a single convolutional layer (first column) is unable to capture the 10 bp periodicity due to the limited receptive field similar to PWMs.

Extended Data Fig. 8 The ChIP-nexus data on CRISPR-mutated ESCs are highly reproducible.

a, Nanog and Sox2 ChIP-nexus profiles normalized to reads per million (RPM) show highly similar profiles and read counts across known enhancer regions for wild-type (Wt) and CRISPR ESCs with either a mutated Sox2 motif (Sox2 CRISPR) or mutated Nanog motif (Nanog CRISPR) at a selected genomic region (chr10: 85,539,626-85,539,777). b, Pairwise comparisons of ChIP-nexus RPM counts between Wt and CRISPR ESCs at bound genomic regions (151 bp centered on the respective motif) with Sox2 ChIP-nexus counts on Sox2 motifs and Nanog ChIP-nexus counts on Nanog motifs (motifs based on the original model). The bulk data (gray) are highly correlated and known enhancer regions as shown in Supplementary Fig. 5 (green) are highly reproducible between ESC lines. Note the specific loss of counts in the selected mutated genomic region (red) over wild-type. Pearson correlations (Rp) between groups are shown in the top left of each scatter plot.

Extended Data Fig. 9 The base-resolution BPNet model can be trained on ChIP-seq profiles.

a, Observed read counts (Obs) and Predicted read counts (Pred) for BPNet trained on ChIP-seq data for the Zfp281 and Lefty1 enhancers located on the held-out (test) chromosome 1, with forward strand reads (dark) and reverse strand reads (light). For Obs, a sliding window of 50 bp was used to smooth the raw 5’ end read counts (line); raw counts are shown as points on the bottom at y = 0. b, BPNet predicts the ChIP-seq profile shape better than replicates. Multinomial log-likelihood difference compared to the constant model was used to evaluate the profile shape quality at different resolutions (from 1 bp to 10 bp windows) in held-out chromosomes 1, 8 and 9. A log-likelihood of 0 corresponds to the constant model. Multinomial log-likelihood was conditioned on the observed number of total counts as in the training loss. c, Total counts in 1 kb regions can be predicted by BPNet (red) at decent accuracy (measured by Pearson correlation with log(1+observed values)). They do not surpass replicate performance (blue), but are well above the Input control (grey). d, Obs and Pred as in panel a, as well as contribution scores for the known Oct4 enhancer. Motif instances derived by CWM scanning are highlighted with a green box.

Extended Data Fig. 10 BPNet trained on ChIP-seq discovers similar motifs and recovers the Nanog motif periodicity.

a, BPNet applied to ChIP-seq discovers the majority of the motifs identified by BPNet applied to ChIP-nexus data. The models ‘ChIP-nexus profile cr’ and ‘ChIP-seq profile cr’ were trained on the union of the ChIP-nexus/seq peaks predicting Oct4, Sox2, and Nanog binding and were interpreted on the intersection of the ChIP-nexus/seq peaks. b, The pairwise spacing of Nanog motif instances derived from the ChIP-seq profile model in all possible strand orientations shows helical periodicity (similar to Extended Data Fig. 7a). c, Motif instance calling with CWM scanning has higher accuracy for BPNet trained on ChIP-nexus data than for BPNet trained on ChIP-seq data (evaluated on the union of the ChIP-nexus/seq peaks, 500 bp around the peak summit using ChIP-nexus footprints as ground truth). d, Training a sequence-to-profile model on ChIP-seq data yields more accurate motif instances (500 bp around the ChIP-seq peak summits using ChIP-nexus footprints as ground truth) than training a binary classification model or using a PWM scanning approach using FIMO for motifs derived directly from ChIP-nexus data. See Extended Data Figs. 3b, 4d and Supplementary Note for more details.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Note

Reporting Summary

Peer Review Information

Supplementary Tables 1–3

(1) List of all ChIP–nexus and ChIP–seq replicate experiments and associated quality control metrics. (2) Further binary classification metrics corresponding to Extended Data Fig. 4a. (3) Sequences of gRNA and single-stranded oligo donors for CRISPR mutations.

Supplementary Data 1

Clustered motifs and their labels. Motifs were obtained by TF–Modisco run on BPNet models trained on six different datasets: (1) seq/profile.peaks-union (ChIP–seq profile model trained on a combination of ChIP–nexus and ChIP–seq peaks); (2) seq/binary (binary classification model trained on genome-wide ChIP–seq peaks); (3) seq/profile (ChIP–seq profile model trained on ChIP–nexus peaks); (4) nexus/profile.peaks-union (ChIP–nexus profile model trained on a combination of ChIP–nexus/ChIP–seq peaks); (5) nexus/binary (binary classification model trained on genome-wide ChIP–nexus peaks); and (6) nexus/profile (ChIP–nexus profile model trained on ChIP–nexus peaks). Each motif logo shows the sequence information content of a PFM. The logo title consists of the manually assigned motif label (for example, TE1, Oct4–Sox2) and the motif ID composed from the model name, the task name and TF–Modisco motif ID (for example, seq/profile/Nanog/m0_p13).

Supplementary Video 1

BPNet profile predictions averaged across 128 random sequences with two motifs inserted at different positions. Centers of the motifs are marked by the vertical gray line; motif distance is shown on the right. For each motif, the predicted profile of the corresponding TF is shown on the y axis.

Supplementary Video 2

See Supplementary Video 1.

Supplementary Video 3

See Supplementary Video 1.

Supplementary Video 4

See Supplementary Video 1.

Supplementary Video 5

See Supplementary Video 1.

Supplementary Video 6

See Supplementary Video 1.

Source data

Source Data Fig. 5

Motif 10-bp periodicity for all motifs visualized in Fig. 5d.

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Avsec, Ž., Weilert, M., Shrikumar, A. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat Genet 53, 354–366 (2021). https://doi.org/10.1038/s41588-021-00782-6

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