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Evaluating deep learning for predicting epigenomic profiles

A preprint version of the article is available at bioRxiv.

Abstract

Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recently, quantitative models have emerged to directly predict the experimental coverage values as a regression. As new models with different architectures and training configurations continue to emerge, a major bottleneck is forming due to the lack of ability to fairly assess the novelty of proposed models and their utility for downstream biological discovery. Here we introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. We highlight various modelling choices that affect generalization performance, including a downstream application of predicting variant effects. In addition, we introduce a robustness metric that can be used to enhance model selection and improve variant effect predictions. Our empirical study largely supports that quantitative modelling of epigenomic profiles leads to better generalizability and interpretability.

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Fig. 1: GOPHER overview.
Fig. 2: Evaluation of Basenji-based quantitative models.
Fig. 3: Testing model robustness against translational shifts.
Fig. 4: Performance comparison between binary and quantitative models.
Fig. 5: Comparison of functional effect predictions.
Fig. 6: GIA for ResidualBind-32 on PC-3 cell line.

Data availability

The processed ATAC-seq data, JASPAR 2022 core motifs for vertebrates data set and CAGI5 challenge data set used that support the findings of this study are available in Zenodo at https://doi.org/10.5281/zenodo.646403158.

Code availability

The code to reproduce results and figures in this study is available in Zenodo at https://doi.org/10.5281/zenodo.646403158. The open-source project repository is available in GitHub at https://github.com/shtoneyan/gopher. A stable version of the code used for generating the results in the paper is available in Zenodo at https://doi.org/10.5281/zenodo.697721359

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Acknowledgements

This work was supported in part by funding from NIH grant R01HG012131 and the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. This work was performed with assistance from the US National Institutes of Health grants S10OD028632-01. We thank J. Kaczmarzyk and other members of the Koo laboratory for helpful discussions.

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Authors and Affiliations

Authors

Contributions

S.T., Z.T. and P.K.K. conceived the experiments. S.T. and Z.T. wrote the code base, conducted the experiments and analysed and interpreted the results. All authors contributed to the manuscript.

Corresponding author

Correspondence to Peter K. Koo.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Anat Kreimer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evaluation of BPNet-based quantitative models.

(a) Loss function analysis. Scatter plot of the whole-chromosome Pearson’s r versus the MSE for different loss functions (shown in a different color) and different target resolutions (shown in a different marker). The results for the scaled Pearson’s r loss function were removed due to poor training runs. (b) Bin resolution analysis. Plot of the whole-chromosome Pearson’s r for models trained on a given bin size (y-axis) with predictions that were systematically down-sampled to a lower resolution for evaluation (x-axis). (a, b) Pearson’s r represents the average across cell lines.

Extended Data Fig. 2 The effect of smoothing coverage on performance.

Basenji-based models were trained on target resolutions (y-axis) and evaluated using different levels of smoothing with a box-car filter. For each higher resolution model, a box-car filter was applied to both predictions and experimental coverage values with various kernel sizes prior to calculating the average Pearson’s r (x-axis). Pearson’s r represents the average across cell lines.

Extended Data Fig. 3 Performance comparison between quantitative and binary models.

Scatter plot of the classification-based AUPR versus the regression-based Pearson’s r for various binary models (blue) and quantitative models (orange) on peak-centred test data (left) and whole-chromosome test data (right). Metrics represent the average across cell lines.

Extended Data Fig. 4 GIA for optimal flanking nucleotides of motifs in PC-3 cell line for various models.

Ranked plot of the global importance for each tested flank for AP-1 motif (left column), ATAAA motif (middle column) and GATA (right column) for different models (shown in a different row). Dashed line represents the global importance of the core motif with random flanks. The hue in the first column represents the position-weight-matrix score for an AP-1 motif from the JASPAR database (ID: MA0491.1). The first 3 rows are quantitative models, the rest are binary models (with (exp) in the name indicating that the first layer ReLU activation has been replaced with an exponential function). For binary models, the results are based on the logits before the output sigmoid activation. The hue in the first column plots represents the PWM score for an AP-1 motif from the JASPAR database (ID: MA0491.1). The black dot in each plot (in the first column) indicates ‘TGTGATTCATG’, which has a high PWM score (12.800) but yields a global importance close to the core motif with randomized flanks.

Extended Data Fig. 5 GIA for distance dependence between AP-1 and other motifs for PC-3 cell line for various models.

Global importance plot for sequences with an AP-1 motif fixed at the centre of the sequence and another motif that is systematically placed in different locations. Positive and negative values represent the first positions the motifs w/ optimized flanks were embedded to be non-overlapping. First column shows results where the second motif is an identical AP-1 motif, the centre column shows results for ATAAA motif and right column for the GATA motif. All the motifs were embedded with optimized flanks. Red vertical dashed lines indicate the 1024bp position. Each row corresponds to a different trained model, the first 3 are quantitative models, the rest are binary models (with (exp) in the name indicating that the first layer ReLU activation has been replaced with an exponential function). For binary models, the results are based on the logits before the output sigmoid activation.

Extended Data Fig. 6 GIA for cooperative interactions between AP-1 and other motifs for PC-3 cell line for various models.

Each column corresponds to a motif pair between two copies of AP-1, ATAAA and AP-1 and AP-1 and GATA. Each row corresponds to a different trained model, the first 3 are quantitative models, the rest are binary models (with (exp) in the name indicating that the first layer ReLU activation has been replaced with an exponential function). For binary models, the results are based on the logits before the output sigmoid activation. Blue and orange box-plots show the global importance scores for the 1000 sampled sequences when motif 1 or motif 2 is individually embedded. Green box-plot shows the case when both motifs are embedded in the same sequence. Red box-plot shows the sum of the green and blue boxes as an estimate of the global importance if there is no interaction. The pairs were embedded at the optimal distance specified from the distance dependence GIA experiments. Box plots show the first and third quartiles, central line is the median, and the whiskers show the range of data with outliers removed. For each motif pair experiment n=1000 independent samples were drawn from the test set sequences.

Supplementary information

Supplementary Information

Supplementary Tables 1–6, Figs. 1–4 and Notes 1–3.

Reporting Summary

Supplementary Data 1

Full evaluation of model prediction performance.

Supplementary Data 2

Model architecture and hyperparameters.

Supplementary Data 3

CAGI5 data set prediction performance.

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Toneyan, S., Tang, Z. & Koo, P.K. Evaluating deep learning for predicting epigenomic profiles. Nat Mach Intell 4, 1088–1100 (2022). https://doi.org/10.1038/s42256-022-00570-9

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