New Results

Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks

Peter K. Koo, Matthew Ploenzke, Praveen Anand, Steffan B. Paul, Antonio Majdandzic
doi: https://doi.org/10.1101/2020.09.08.288068

ABSTRACT

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. For model interpretability, attribution methods have been employed to reveal learned patterns that resemble sequence motifs. First-order attribution methods only quantify the independent importance of single nucleotide variants in a given sequence – it does not provide the effect size of motifs (or their interactions with other patterns) on model predictions. Here we introduce global importance analysis (GIA), a new model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a new convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.

Introduction

To infer sequence preferences of RNA-binding proteins (RBPs), a variety of in vitro and in vivo experimental methods enrich for protein-bound RNA sequences1–8, and computational methods are used to deduce the consensus RNA sequence and/or structure features that these bound sequences share9–13. Many computational approaches employ position-weight-matrices (PWMs) or k-mers to model RNA sequence and, in some cases, its secondary structure context. These methods often make simplifying assumptions that do not fully consider biologically important features, such as the multiplicity, size, and position of the features along a given sequence.

Recently, deep neural networks (DNNs), predominantly based on convolutional neural networks (CNNs) or convolutional-recurrent network hybrids, have emerged as a promising alternative, in most cases, improving prediction performance on held-out test data13–19. DNNs are a powerful class of models that can learn a functional mapping between input genomic sequences and experimentally measured labels, requiring minimal feature engineering20–22. DeepBind was the first deep learning approach to analyze RBP-RNA interactions13. At the time, it demonstrated improved performance over PWM- and k-mer-based methods on the 2013-RNAcompete dataset, a standard benchmark dataset that consists of 244 in vitro affinity selection experiments that span across many RBP families5. Since then, other deep learning-based methods have emerged, further improving prediction performance on this dataset23–25 and other CLIP-seq-based datasets11,18,26,27.

To validate that DNNs are learning biologically meaningful features, learned representations are visualized and compared to known motifs, previously identified by PWM- and k-mer-based methods28. For RBPs, this has been accomplished by visualizing first convolutional layer filters and by attribution methods13,18,23,24. First layer filters have been shown to capture motif-like representations, but their efficacy depends highly on choice of model architecture29, activation function30, and training procedure31,32. First-order attribution methods, including in silico mutagenesis13,33 and other gradient-based methods34–37, are interpretability methods that identify the independent importance of single nucleotide variants in a given sequence toward model predictions – not the effect size of extended patterns such as sequence motifs. Representations from attribution methods are local to an individual sequence. Hence, it can be challenging to to generalize the importance of patterns disentangled from contributions by other factors, including competitive/cooperative binding sites and sequence/structure context, from attribution analysis of individual sequences.

Here we introduce global importance analysis (GIA), an approach that enables hypothesis-driven model interpretability by quantitatively measuring global effect sizes that patterns have on model predictions across a population of sequences. As a case study, we highlight the capabilities of GIA on a computational task of predicting RNA sequence specificities of RBPs. We introduce ResidualBind, a new convolutional network, and demonstrate that it outperforms previous methods on RNAcompete data. Using GIA, we demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.

Global importance analysis

Global importance analysis measures the population-level effect size that a putative feature, like a motif, has on model predictions. Given a sequence-function relationship i.e. Embedded Image, where x is a sequence of length Embedded Image, where A = {A, C, G, T}) and y represents a corresponding function measurement (y ∈ ℝ), the global importance of pattern ϕ (Embedded Image, where l < L) embedded starting at position i in sequences under the data distribution Embedded Image is given by: Embedded Image where Embedded Image is an expectation and Embedded Image represents sequences drawn from the data distribution that have pattern ϕ embedded at positions [i, i + l]. Equation 1 quantifies the global importance of pattern ϕ across a population of sequences while marginalizing out contributions from other positions. Important to this approach is the randomization of other positions, which is necessary to mitigate the influence of background noise and extraneous confounding signals that may exist in a given sequence. If the dataset is sufficiently large and randomized, then Eq. 1 can be calculated directly from the data. However, sequences with the same pattern embedded at the same position and a high diversity at other positions must exist for a good estimate of Eq. 1.

Alternatively, a trained DNN can be employed as a surrogate model for experimental measurements by generating synthetic sequences necessary to calculate Eq. 1 and using model predictions as a proxy for experimental measurements. Given a DNN that maps input sequence to output predictions, i.e. f:x → y*, where y* represents model predictions, the estimated global importance of pattern ϕ embedded starting at position i under the synthetic data distribution Embedded Image is given by: Embedded Image where Embedded Image represents an estimate of Embedded Image, the expectation is approximated with an average of N samples from a synthetic data distribution Embedded Image. Without loss of generality, if we sample the same nth sequence for both expectations with the only difference being that Embedded Image has an embedded pattern, then we can combine summations, according to: Embedded Image

The difference between the nth sequence with and without the embedded pattern inside the summation of Eq. 2 estimates the local effect size – the change in prediction caused by the presence of the pattern for the given sequence. The average across N samples estimates the global effect size – the change in prediction caused by the presence of the pattern across a population of sequences.

The synthetic data distribution must be chosen carefully to be representative of the data distribution and to minimize any distributional shift, which can lead to misleading results. One approach can be to generate randomized sequences from a position-specific probability model of the sequences from the data distribution – average nucleotide frequency at each position, also referred to as a profile. A profile model captures position-dependent biases while averaging down position-independent patterns, like motifs. Alternative sequence models include random shuffling and dinucleotide shuffling of the real sequences in the dataset. If there exists high-order dependencies in the observed sequences, such as RNA secondary structure or motif interactions, a distributional shift between the synthetic sequences and the data distribution may arise. A synthetic sequence model can also sample real sequences directly from the dataset, although this requires careful selection such that unaccounted patterns do not persist systematically, which can act as a potential confounder. Prior knowledge can help design a suitable synthetic sequence model.

Using model predictions as a proxy for experimental measurements means that GIA quantifies the effect size for a pattern under investigation through the lens of a DNN, and hence results should be taken from the perspective of model interpretability. While Eqs. 1 and 2 describe the global importance of a single pattern, GIA supports embedding more than one pattern. GIA can also be extended to multi-task problems when each class is independent.

GIA is related to previous in silico experiments that quantify population-level feature importance, including a previous version of this paper38, and more recently in some in silico experiments by Avsec et al39. Here, we formalize GIA to distinguish in silico experiments that measure global effect sizes of patterns from other in silico experiments that use model predictions as a proxy for experimental measurements40 and occlusion-based in silico experiments that identify the importance of features local to a sequence under investigation39,41.

Materials and methods

RNAcompete dataset

Overview

We obtained the 2013-RNAcompete dataset from5, where a full explanation of the data can be found. The 2013-RNAcompete experiments consist of ~241,000 RNA sequences each 38-41 nucleotides in length, split into two sets ‘set A’ (120,326 sequences) and ‘set B’ (121,031 sequences). Sequences were designed to ensure that all possible combinations of 9-mers are sampled at least 16 times, with each set getting 8 copies of all possible 9-mers. The provided binding score for each sequence is the log-ratio of the fluorescence intensities of pull-down versus input, which serves as a measure of sequence preference. The 2013-RNAcompete dataset consists of 244 experiments for 207 RBPs using only weakly structured probes5.

Preparation of RNAcompete datasets

Each sequence from ‘set A’ and ‘set B’ was converted to a one-hot representation. For a given experiment, we removed sequences with a binding score of NaN. We then performed either clip-transformation or log-transformation. Clip-transformation is performed by clipping the extreme binding scores to the 99.9th percentile. Log-transformation processes the binding scores according to the function: log (S − SMIN + 1), where S is the raw binding score and SMIN is the minimum value across all raw binding scores. This monotonically reduces extreme binding scores while maintaining their rank order, and also yields a distribution that is closer to a Normal distribution. The processed binding scores of either clip-transformation or log-transformation were converted to a z-score. We randomly split set A sequences to fractions 0.9 and 0.1 for the training and validation set, respectively. Set B data was held out and used for testing. RNA sequences were converted to a one-hot representation with zero-padding added as needed to ensure all sequences had the same length of 41 nucleotides. Henceforth, all predictions and experimental binding scores are in terms of the z-transformed clip- or log-transformed binding score.

ResidualBind

Architecture

ResidualBind takes one-hot encoded RNA sequence as input and outputs a single binding score prediction for an RBP. ResidualBind consists of: (1) convolutional layer (96 filters, filter size 11), (2) dilated residual module, (3) mean-pooling layer (pool size 10), (4) fully-connected hidden layer (256 units), and (5) fully-connected output layer to a single output. The dilated residual module consists of 3 convolutional layers with a dilation rate of 1, 2, and 4, each with a filter size of 3. Each convolutional layer employs batch normalization prior to a rectified linear unit (ReLU) activation and dropout probabilities according to layers (1) 0.1, (2) 0.2, (4) 0.5. The pre-activated output of the third convolutional layer is added to the inputs of the dilated residual module, a so-called skipped connection42, the output of which is then activated with a ReLU. The stride of all convolutions are 1 and set to the pool size for the mean-pooling layer. We found that varying the hyperparameter settings largely yielded similar results. Choice of the final model was based on slightly better performance on the validation set.

Training ResidualBind

For each RNAcompete experiment, we trained a separate, randomly-initialized ResidualBind model on ‘set A’ sequences by minimizing the mean squared-error loss function between the model predictions and the experimental binding scores (which were used as labels). All models were trained with mini-batch stochastic gradient descent (mini-batch of 100 sequences) with Adam updates43 with a decaying learning rate – the initial learning rate was set to 0.001 and decayed by a factor of 0.3 if the model performance on a validation set (as measured by the Pearson correlation) did not improve for 7 epochs. Training was stopped when the model performance on the validation set does not improve for 25 epochs. Optimal parameters were selected by the epoch which yields the highest Pearson correlation on the validation set. The parameters of each model were initialized according to Glorot initialization44. On average, it took about 100 epochs (13 seconds/epoch) to train an RNAcompete experiment on a single NVIDIA 2080ti RTX graphical processing unit.

Evaluation

We predicted binding scores on ‘set B’ sequences using corresponding experimental binding scores to test the efficacy of ResidualBind, using the Pearson correlation between model predictions and experimental binding scores on the held-out test data12,13.

Incorporation of secondary structure profiles

Paired-unpaired structural profiles were calculated using RNAplfold45. Structural profiles consisting of predicted paired probabilities of five types of RNA structure – paired, hairpin-loop, internal loop, multi-loop, and external loop (PHIME) – were calculated using a modified RNAplfold script10. For each sequence, the window length (-W parameter) and the maximum spanning base-pair distance (-L parameter) were set to the full length of the sequence. Secondary structure profiles were incorporated into ResidualBind by creating additional input channels. The first convolutional layer now analyzes either 6 channels (4 channels for one-hot primary sequence and 2 channels for PU probabilities) or 9 channels (4 channels for one-hot primary sequence and 5 channels for PHIME probabilities).

Model interpretability

In silico mutagenesis

In silico mutagenesis is calculated by systematically querying a trained model with new sequences with a different single nucleotide mutation along the sequence and ordering the predictions as a nucleotide-resolution map (4 × L, where 4 is for each nucleotide and L is the length of the sequence). Each prediction is subtracted by the wildtype sequence prediction, effectively giving zeros at positions where the variant matches the wildtype sequence. To visualize the in silico mutagenesis maps, we calculate the L2-norm across variants for each position. A sequence logo is generated for the wildtype sequence, where heights correspond the sensitivity of each position via the L2-norm, and visualized using Logomaker46.

Global Importance analysis

1,000 synthetic RNA sequences, each 41 nucleotides long, were sampled from a profile-based sequence model, which was estimated by the observed nucleotide frequency at each position of the training data. Patterns under investigation were embedded in positions specified in each GIA experiment. We queried a trained ResidualBind model with these sequences with and without the embedded pattern. We refer to the difference between the predictions with and without the pattern for each sequence as the“local” importance (the value inside the summation of Eq. 2) and the average across the population as the “global” importance (Eq. 2).

Motif Visualization

Motif representations learned by ResidualBind are visualized with 2 methods, top k-mer motif and k-mer alignment motif. Top k-mer motif plots the top k-mer as a logo with heights scaled according to the L2-norm of the global importance of nucleotide variants at each position, which is measured via GIA by systematically introducing a single nucleotide mutation to the top k-mer embedded at positions 18-24.

A k-mer alignment-based motif was generated by aligning the top 10 k-mers (identified via GIA) to the top k-mer according to the maximum cross-correlation value. The nucleotide frequency, weighted by the global importance score for each k-mer, gives a matrix that resemples a position probability matrix which can be visualized as a sequence logo using Logomaker46.

Results

To demonstrate the utility of GIA, we developed a deep CNN called ResidualBind to address the computational task of predicting RNA-protein interactions. Unlike previous methods designed for this task, ResidualBind employs a residual block consisting of dilated convolutions, which allows it to fit the residual variance not captured by previous layers while considering a larger sequence context47. Moreover, the skipped connection in residual blocks foster gradient flow to lower layers, improving training of deeper networks42. Dilated convolutions combined with skipped connections have been previously employed in various settings for regulatory genomics16,17,39.

ResidualBind yields state-of-the-art predictions on the RNAcompete dataset

To compare ResidualBind against previous methods, including MATRIXReduce9, RNAcontext10, GraphProt11, DeepBind13, RCK12, DLPRB23, cDeepbind24 and ThermoNet25, we benchmarked its performance on the 2013-RNAcompete dataset (see Methods for details). We found that ResidualBind (average Pearson correlation: 0.689±0.170) significantly outperforms previously reported methods based on PWMs (MATRIXReduce: 0.353±0.192, RNAcontext: 0.434±0.130), k-mers (RCK: 0.460±0.140), and DNNs (DeepBind: 0.409±0.167, cDeepbind: 0.582±0.169, DLPRB: 0.628±0.160, and ThermoNet: 0.671±0.171, p-value < 0.01, Wilcoxon sign rank test) (Fig. 1A).

Figure 1.
Figure 1.

Performance comparison on the 2013-RNAcompete dataset. (a) Box-violin plot of test performance by different computational methods. Each plot represents the Pearson correlation between model predictions and experimental binding scores on held out test data for all 244 RBPs of the 2013-RNAcompete dataset. Median value is shown as a red line. (b,c) Scatter plot of ResidualBind’s predicted binding scores and experimental binding scores from the test set of an RBP experiment in the 2013-RNAcompete dataset (RNCMPT00169) processed according to (b) clip-transformation and (c) log-transformation. Black dashed line serves as a guide-to-the-eye for a perfect correlation. (d) Box-violin plot of test performance for experimental binding scores processed according to a clip-transformation and a log-transformation. (e) Box-violin plot of the test performance for different input features: sequence, sequence and paired-unpaird secondary structure profiles (sequence+PU), and sequence and PHIME secondary structure profiles (sequence+PHIME). (f) Histogram of the one-to-one performance difference between ResidualBind trained on sequences and trained with additional PHIME secondary structural profiles.

We noticed that the preprocessing step employed by previous methods, which clips large experimental binding scores to their 99.9th percentile value and normalizing to a z-score, a technique we refer to as clip-transformation, adversely affects the fidelity of ResidualBind’s predictions for higher binding scores, the most biologically relevant regime (Fig. 1b). Instead, we prefer preprocessing experimental binding scores with a log-transformation, similar to a Box-Cox transformation, so that its distribution approaches a normal distribution while also maintaining their rank-order (see Methods). With log-transformation, we found that ResidualBind yields higher quality predictions in the high-binding score regime (Fig. 1c), although the average performance was essentially the same (Fig. 1d, average Pearson correlation is 0.685 0.172 for log-transformation). Henceforth, our results will be based on preprocessing experimental binding scores with log-transformation.

Secondary structure context does not help ResidualBind

RNA structure is important for RBP recognition49. Previous work, including RCK, RNAcontext, DRPLB, cDeepbind, and ThermoNet, have found that including RNA secondary structure predictions as an additional input feature significantly improves the accuracy of their model’s predictions. Despite yielding better predictions when considering only sequences, we wanted to test whether incorporating secondary structure predictions would also improve ResidualBind’s performance. Similar to previous methods, we predicted two types of RNA secondary structure profiles for each sequence using RNAplfold45, which provides the probability for each nucleotide to be either paired or unpaired (PU), and a modified RNAplfold script10, which provides the probability for each nucleotide to be in a structural context: paired, hairpin-loop, internal loop, multi-loop, and external-loop (PHIME). Surprisingly, secondary structure profiles do not increase ResidualBind’s performance (Figs. 1 e and f, average Pearson correlation of 0.683±0.182, 0.684±0.183, and 0.682±0.183 for sequence, sequence + PU, and sequence + PHIME, respectively). One possible explanation is that ResidualBind has already learned secondary structure effects from sequence alone, an idea we will explore later.

Going beyond in silico mutagenesis with GIA

It remains unclear why ResidualBind, and many other DNN-based methods, including cDeepbind, DLPRB, and ThermoNet, yield a significant improvement over previous methods based on k-mers and PWMs. To gain insights into what DNN-based methods have learned, DLPRB visualizes filter representations while cDeepbind employs in silico mutagenesis. Filter repre-sentations are sensitive to network design choices29; ResidualBind is not designed with the intention of learning interpretable filters. Hence, we opted to employ in silico mutagenesis, which systematically probes the effect size that each possible single nucleotide mutation in a given sequence has on model predictions. For validation purposes, we perform a detailed exploration for a ResidualBind model trained on a RNAcompete dataset for RBFOX1 (dataset id: RCMPT000168), which has an experimentally verified motif ‘UGCAUG’6,48,50. Figure 2a highlights in silico mutagenesis sequence logos for two sequences with high predicted binding scores – one with a perfect match and the other with two mismatches to the canonical RBFOX1 motif (Methods). Evidently, a single intact RBFOX1 motif is sufficient for a high binding score, while the sequence that contains mismatches to the canonical motif can also have high binding scores by containing several ‘sub-optimal’ binding sites (Fig. 2a, ii). This suggests that the number of motifs and possibly their spacing is relevant.

Figure 2.
Figure 2.

Investigation of a ResidualBind model trained on RBFOX1. (a) Scatter plot of experimental binding scores versus predicted binding scores for test sequences in the 2013-RNAcompete dataset for RBFOX1 (Pearson correlation = 0.832). The color of each point is determined by the number of mutations between the canonical motif (UGCAUG) and its best match in the sequence. (i-ii) The inset shows sequence logos for in silico mutagenesis maps for a high binding score sequence with at best: (i) a perfect match and (ii) a double nucleotide mismatch to the canonical RBFOX1 motif. Box plot of the local importance for synthetic sequences with varying numbers of the (b) canonical RBFOX1 motif (UGCAUG) and (c) a sub-optimal motif AGAAUG embedded progressively at positions: 4-9, 11-16, and 18-23. Black dashed line represents a linear fit, red horizontal dashed line represents the median, and green triangles represents the global importance. (d) Box plot of the local importance for synthetic sequences with varying degrees of separation between two RBFOX1 motifs (‘N’ represents a position with random nucleotides). (e) Global importance for synthetic sequences embedded with single nucleotide mutations of the canonical RBFOX1 motif, with a sequence logo (above) that has heights scaled according to the L2-norm at each position. (f) Scatter plot of the experimental ln KD ratio of the mutant to wild type measured via surface plasmon resonance48 versus the global importance for the same RBFOX1 variants. Red dashed line represents a linear fit and the R2 and p-value from a t-test is shown in the inset. (g) Histogram of the R2 from a linear fit of global importance of embedding different numbers of the toper k-mer (identified by a separate, k-mer-based GIA experiment) at positions: 4-9, 11-16, and 18-23, across the 2013-RNAcompete dataset.

While in silico mutagenesis, which is the gold standard of model interpretability of DNNs in genomics, is a powerful approach to highlight learned representations that resemble known motifs, it cannot inform the effect size of larger patterns, such as motifs or combinations of motifs. To quantitatively test the hypothesis that ResidualBind learns additive effects from sub-optimal binding sites, we can employ GIA.

GIA shows ResidualBind learns multiple binding sites are additive

By progressively embedding the canonical RBFOX1 motif (UGCAUG) and a suboptimal motif (AGAAUG, which contains two mismatches at positions 1 and 3) in synthetic sequences at various positions, 4-9, 11-16, and 18-23, we find ResidualBind has learned that the contribution of each motif is indeed additive (Fig. 2c). We also validate that the spacing between two binding sites can decrease this effect when two motifs are too close (Fig. 2d), which manifests biophysically through steric hindrance.

GIA identifies expected sequence motifs with k-mers

In many cases, the sequence motif of a RBP is not known a priori, which makes the interpretation of in silico mutagenesis maps more challenging in practice. We can use GIA for ab initio motif discovery by embedding all possible k-mers at positions 18-24. Indeed the top scoring 6-mer that yields the highest importance score for a ResidualBind model trained on RBFOX1 is ‘UGCAUG’ which is consistent with its canonical motif (Fig. 2e). Using the top scoring k-mer as a base binding site, we can determine the importance of each nucleotide variant by calculating the global importance for all possible single nucleotide mutations (Fig. 2e). Figure 2f shows that the global importance for different variants correlate significantly with experimentally-determined ln KD ratios of the variants and wild type measured by surface plasmon resonance experiments48 (p-value = 0.0015, t-test). Progressively embedding the top k-mer in multiple positions reveals that ResidualBind largely learns a function where non-overlapping motifs are predominantly additive (Fig. 2g).

A motif representation can be generated from the global in silico mutagenesis analysis in two ways, by plotting the top k-mer with heights scaled by the L2-norm of the GIA-based in silico mutagenesis scores at each position or by creating an alignment of the top k-mers and calculating a weighted average according to their global importance, which provides a position frequency matrix representation that cab be used to generate a sequence logo. ResidualBind’s motif representations and the motifs generated from the original RNAcompete experiment (which are deposited in the CISBP-RNA database) are quite similar (Supplementary Table 1).

GIA reveals ResidualBind learns RNA secondary structure context from sequence

The 2013-RNAcompete dataset was specifically designed to be weakly structured5, which means that the inclusion of secondary structure profiles as input features should, in principle, not add large gains in performance. To better assess whether ResidualBind benefits from the inclusion of secondary structure profiles, we trained ResidualBind on the 2009-RNAcompete dataset, which consists of more structured RNA probes that include stem-loops for nine RBPs51. On average, ResidualBind yielded only a slight gain in performance by including PU secondary structure profiles (average Pearson correlation of 0.711±0.115 and 0.721±0.116 for sequence only and sequence+PU ResidualBind models, respectively).

In this dataset, VTS1 is a well-studied RBP with a sterile-alpha motif (SAM) domain that has a high affinity towards RNA hairpins that contain ‘CNGG’52,53. ResidualBind’s performance for VTS1 was comparable (0.6981 and 0.7073 for sequence only and sequence+PU ResidualBind model, respectively), suggesting that the sequence-only model may be learning secondary structure context. An in silico mutagenesis analysis for the sequence-only ResidualBind model reveals that the VTS1 motif is found in sequences with a high and low binding score, albeit with flanking nucleotides given significant importance as well (Figure 3a). The presence of a VTS1 motif in a sequence is not sufficient to determine its binding score. Nevertheless, each sequence was accurately predicted by the sequence-only model. The PU secondary structure profile given by RNAplfold for each sequence reveals that the VTS1 motif is inside a loop region of a stem-loop structure in high binding score sequences and in the stem region for low binding score sequences. This further supports that the network may be learning positive and negative contributions of RNA secondary structure context directly from the sequence despite never explicitly being trained to do so. Moerover, the seemingly noisy importance scores that flank the VTS1 motif may represent signatures of secondary structure.

Figure 3.
Figure 3.

Investigation of a ResidualBind model trained on VTS1 from the 2009-RNAcompete dataset. (a) Scatter plot of experimental binding scores versus predicted binding scores on held-out test sequences. The color of each point is determined by the number of mutations between the CISBP-RNA-derived motif (GCUGG) and the best match across the sequence. The inset shows sequence logos for in silico mutagenesis maps generated by a ResidualBind model trained only on sequences for representative sequences with high predicted binding scores (i-ii) and low predicted binding scores which contain the VTS1 motif (iii-iv). Below each sequence logo is a PU structure logo, where ‘U’ represent unpaired (grey) and ‘P’ represents paired (black), calculated by RNAplfold. (b) Global importance for synthetic sequences embedded with single nucleotide mutations of the top scoring 6-mer, GCUGGC. Above is a sequence logo with heights scaled according to the L2-norm at each position. (c) Box plot of local importance for the top scoring 6-mer embedded in the stem and loop region of synthetic sequences designed with a stem-loop structure and in the same positions in random RNA sequences. Green triangle represents the global importance.

To quantitatively validate that ResidualBind has learned secondary structure context, we performed GIA by embedding the VTS1 motif in either the loop or stem region of synthetic sequences designed to have a stem-loop structure – enforcing Watson-Crick base pairs at positions 6-16 with 23-33. As a control, a similar GIA experiment was performed with the VTS1 motif embedded in the same positions but in random RNAs. Evidently, ResidualBind learns that the VTS1 motif in the context of a hairpin loop leads to higher binding scores compared to when it is placed in other secondary structure contexts.

GIA highlights importance of GC-bias

By observing in silico mutagenesis plots across many 2013-RNAcompete experiments, we noticed that top scoring sequences exhibited importance scores for known motifs along with GC content towards the 3’ end (Figs. 4a and b). We did not observe any consistent secondary structure preference for the 3’ GC-bias using structure predictions given by RNAplfold. Using GIA, we tested the effect size of the GC-bias for sequences with a top 6-mer motif embedded at the center. Figures 4c and d show that GC-bias towards the 3’ end indeed is a systemic feature for nearly all RNAcompete experiments with an effect size that varies from RBP to RBP (Fig. 4e). We do not know the origin of this effect. Many experimental steps in the RNAcompete protocol could lead to this GC-bias7,54,55.

Figure 4.
Figure 4.

GC-bias in high binding score sequences. (a) Representative sequence logos from in silico mutagenesis analysis for a test sequence with a top-10 binding score prediction for RNAcompete experiments for CG17838 (RNCMPT00131) and HuR (RNCPT00112). (b) Motif comparison between CISBP-RNA and ResidualBind’s motif representations generated by k-mer alignments. (c) Box plot of local importance for synthetic sequences with the top scoring 6-mer embedded in position 18-24 and GCGCGC embedded at positions 1-7 (Motif+GC, left) or positions 35-41 (Motif + GC, right). As a control, the GC content embedded at positions 35-41 without any motif is also shown. Green triangle represents the global importance. (d) Histogram of the GC-bias effect size, which is defined as the global importance when GC-bias is placed on the 5’ end (orange) and the 3’ end (blue) of synthetic sequences with a top scoring 6-mer embedded at positions 18-24 divided by the global importance of the motif at the center without any GC content, for each 2013-RNAcompete experiment. (e) Histogram of the difference between the GC-bias effect size, GC-bias on the 5’ end minus the 3’ end for each 2013-RNAcompete experiment.

Discussion

Global importance analysis is a powerful method to quantify the effect size of putative features that are causally linked to model predictions. It provides a framework to quantitatively test hypotheses of the importance of putative features and explore specific functional relationships using in silico experiments, for both positive and negative controls.

As a case study, we introduced ResidualBind for the computational task of predicting RNA-protein interactions. By benchmarking ResidualBind’s performance on RNAcompete data, we showed that it outperforms previous methods, including other DNNs. While DNNs as a class of models have largely improved performance compared to previous methods based on PWMs and k-mers, model interpretability – based on attribution methods and visualization of first convolutional layer filters – often demonstrate that they learn similar motif representations as previous PWM-based methods, which makes it unclear what factors are driving performance gains. Since first-order attribution methods only inform the effect size of single nucleotide variants on an individual sequence basis, insights have to be gleaned by observing patterns that generalize across multiple sequences. Without ground truth, interpreting in silico mutagenesis plots can be challenging.

Using GIA, we were able to move beyond speculation from observations of attribution maps by quantitatively testing the relationships between putative features across a population of sequences. Interestingly, we found that despite ResidualBind’s ability to fit complex non-linear functions, it largely learns an additive model for binding sites, which any linear PWM or k-mer based model is fully capable of capturing. We believe the performance gains arise from positional information of the features, including spacing between binding sites and the position of sequence context, such as secondary structures and GC-bias. While these properties are well known features of RBP-RNA interactions, previous computational models were not fully considering these factors, which may have led to their lower performance on the RNAcompete dataset.

ResidualBind

ResidualBind is a flexible model that can be broadly applied to a wide range of different RBPs without modifying hyperparameters for each experiment, although tuning hyperparameters for each experiment would almost certainly boost performance further. While ResidualBind was developed here for RBP-RNA interactions as measured by the RNAcompete dataset, this approach should also generalize to other data modalities that measure sequence-function relationships, including high-throughput assays for protein binding, histone modifications, and chromatin accessibility, given the output activation and loss function are modified appropriately for the task-at-hand.

In vitro-to-in vivo generalization gap

Ideally, a computational model trained on an in vitro dataset would learn principles that generalize to other datasets, including in vivo datasets. However, models trained on one dataset typically perform worse when tested on other datasets derived from different sequencing technologies/protocols56, which have different technical biases7,54,55,57. Learn features like GC-bias may explain why DNNs exhibit large performance gains on held-out RNAcompete data but only a smaller gain compared to k-mer-based methods when tasked with generalization to in vivo data based on CLIP-seq23–25. While we focus our model interpretability efforts on sequences with high binding scores, exploration in other binding score regimes may reveal other sequence context. GIA highlights a path forward to tease out sequencing biases, which can inform downstream analysis to either remove/de-bias unwanted features from the dataset.

GIA as a surrogate for experiments

The global importance of features can be estimated experimentally with sequences designed to contain a pattern under investigation and randomizing the other positions. Calculating this through experimental measurements can be time consuming and costly due to the large number of sequences required to calculate Eq. 1. Here we demonstrate how a well-trained neural network can be employed as a proxy for these wetlab experiments, generating predictions (instead of experimental measurements) for sequences necessary to calculate Eq. 1. Of course predictions are based on the model’s fit of the data and hence GIA is at its core a model interpretability tool.

Availability

Dataset and code: http://github.com/p-koo/residualbind

Acknowledgements

This work was supported in part by funding from the NCI Cancer Center Support Grant (CA045508) and the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. MP was supported by NIH NCI RFA-CA-19-002. The authors would like to thank Sean Eddy, Justin Kinney, David McCandlish, Nicholas Lee, Amaar Tareen, Fred Davis, members of the Koo Lab and the Eddy Lab for helpful discussions and feedback on the manuscript.

References

  1. 1.↵
    Licatalosi, D. D. et al. Hits-clip yields genome-wide insights into brain alternative rna processing. Nature 456, 464–469 (2008).
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.
    Hafner, M. et al. Transcriptome-wide identification of rna-binding protein and microrna target sites by par-clip. Cell 141, 129–141 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.
    König, J. et al. iclip reveals the function of hnrnp particles in splicing at individual nucleotide resolution. Nat. Struct. & Mol. Biol. 17, 909 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.
    Guenther, U.-P. et al. Hidden specificity in an apparently nonspecific rna-binding protein. Nature 502, 385–388 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  5. 5.↵
    Ray, D. et al. A compendium of rna-binding motifs for decoding gene regulation. Nature 499, 172–177 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    Lambert, N. et al. Rna bind-n-seq: quantitative assessment of the sequence and structural binding specificity of rna binding proteins. Mol. Cell 54, 887–900 (2014).
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    Sundararaman, B. et al. Resources for the comprehensive discovery of functional rna elements. Mol. Cell 61, 903–913 (2016).
    OpenUrlCrossRef
  8. 8.↵
    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of rna-binding protein binding sites with enhanced clip (eclip). Nat. Methods 13, 508–514 (2016).
    OpenUrlCrossRefPubMed
  9. 9.↵
    Foat, B. C., Morozov, A. V. & Bussemaker, H. J. Statistical mechanical modeling of genome-wide transcription factor occupancy data by matrixreduce. Bioinformatics 22, e141–e149 (2006).
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    Kazan, H., Ray, D., Chan, E. T., Hughes, T. R. & Morris, Q. Rnacontext: a new method for learning the sequence and structure binding preferences of rna-binding proteins. PLoS Comput. Biol. 6, e1000832 (2010).
    OpenUrlCrossRefPubMed
  11. 11.↵
    Maticzka, D., Lange, S. J., Costa, F. & Backofen, R. Graphprot: modeling binding preferences of rna-binding proteins. Genome Biol. 15, 1–18 (2014).
    OpenUrlCrossRefPubMed
  12. 12.↵
    Orenstein, Y., Wang, Y. & Berger, B. Rck: accurate and efficient inference of sequence-and structure-based protein–rna binding models from rnacompete data. Bioinformatics 32, i351–i359 (2016).
    OpenUrlCrossRefPubMed
  13. 13.↵
    Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015).
    OpenUrlCrossRefPubMed
  14. 14.
    Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12, 931–934 (2015).
    OpenUrlCrossRefPubMed
  15. 15.
    Quang, D. & Xie, X. Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic Acids Res. 44, e107–e107 (2016).
    OpenUrlCrossRefPubMed
  16. 16.↵
    Kelley, D. R. et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res. 28, 739–750 (2018).
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548 (2019).
    OpenUrl
  18. 18.↵
    Ghanbari, M. & Ohler, U. Deep neural networks for interpreting rna-binding protein target preferences. Genome Res. 30, 214–226 (2020).
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    Jha, A., Aicher, J. K., Gazzara, M. R., Singh, D. & Barash, Y. Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study. Genome Biol. 21, 1–22 (2020).
    OpenUrlCrossRefPubMed
  20. 20.↵
    Cybenko, G. Approximation by superpositions of a sigmoidal function. Math. Control. Signals Syst. 2, 303–314 (1989).
    OpenUrlCrossRef
  21. 21.
    Raghu, M., Poole, B., Kleinberg, J., Ganguli, S. & Sohl-Dickstein, J. On the expressive power of deep neural networks. In international Conference on Machine Learning, 2847–2854 (2017).
  22. 22.↵
    Shaham, U., Cloninger, A. & Coifman, R. R. Provable approximation properties for deep neural networks. Appl. Comput. Harmon. Analysis 44, 537–557 (2018).
    OpenUrl
  23. 23.↵
    Ben-Bassat, I., Chor, B. & Orenstein, Y. A deep neural network approach for learning intrinsic protein-rna binding preferences. Bioinformatics 34, i638–i646 (2018).
    OpenUrlCrossRef
  24. 24.↵
    Gandhi, S., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. cdeepbind: A context sensitive deep learning model of rna-protein binding. bioRxiv 345140 (2018).
  25. 25.↵
    Su, Y., Luo, Y., Zhao, X., Liu, Y. & Peng, J. Integrating thermodynamic and sequence contexts improves protein-rna binding prediction. PLoS Comput. Biol. 15, e1007283 (2019).
    OpenUrl
  26. 26.↵
    Pan, X. & Shen, H.-B. Rna-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinforma. 18, 136 (2017).
    OpenUrl
  27. 27.↵
    Grønning, A. G. B. et al. Deepclip: predicting the effect of mutations on protein–rna binding with deep learning. Nucleic Acids Res. 48, 7099–7118 (2020).
    OpenUrl
  28. 28.↵
    Koo, P. K. & Ploenzke, M. Deep learning for inferring transcription factor binding sites. Curr. Opin. Syst. Biol. 19, 16–23 (2020).
    OpenUrl
  29. 29.↵
    Koo, P. K. & Eddy, S. R. Representation learning of genomic sequence motifs with convolutional neural networks. PLoS Comput. Biol. 15, e1007560 (2019).
    OpenUrl
  30. 30.↵
    Koo, P. & Ploenzke, M. Improving representations of genomic sequence motifs in convolutional networks with exponential activations. bioRxiv (2020).
  31. 31.↵
    Ilyas, A. et al. Adversarial examples are not bugs, they are features. In Advances in Neural Information Processing Systems, 125–136 (2019).
  32. 32.↵
    Koo, P. K., Qian, S., Kaplun, G., Volf, V. & Kalimeris, D. Robust neural networks are more interpretable for genomics. bioRxiv 657437 (2019).
  33. 33.↵
    Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016).
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034 (2013).
  35. 35.
    Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. arXiv:1703.01365 (2017).
  36. 36.
    Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. arXiv:1704.02685 (2017).
  37. 37.↵
    Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 4765–4774 (2017).
  38. 38.↵
    Koo, P. K., Anand, P., Paul, S. B. & Eddy, S. R. Inferring sequence-structure preferences of rna-binding proteins with convolutional residual networks. bioRxiv 418459 (2018).
  39. 39.↵
    Avsec, Ž. et al. Deep learning at base-resolution reveals motif syntax of the cis-regulatory code. bioRxiv 737981 (2019).
  40. 40.↵
    Le, D. D. et al. Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. Proc. Natl. Acad. Sci. 115, E3702–E3711 (2018).
    OpenUrlAbstract/FREE Full Text
  41. 41.↵
    Zeiler, M. D. & Fergus, R. Visualizing and understanding convolutional networks. In European Conference on Computer Vision, 818–833 (Springer, 2014).
  42. 42.↵
    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).
  43. 43.↵
    Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).
  44. 44.↵
    Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–256 (2010).
  45. 45.↵
    Lorenz, R. et al. Viennarna package 2.0. Algorithms for Mol. Biol. 6, 26 (2011).
    OpenUrlCrossRef
  46. 46.↵
    Tareen, A. & Kinney, J. B. Logomaker: beautiful sequence logos in python. Bioinformatics 36, 2272–2274 (2020).
    OpenUrl
  47. 47.↵
    Yu, F., Koltun, V. & Funkhouser, T. Dilated residual networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 472–480 (2017).
  48. 48.↵
    Auweter, S. D. et al. Molecular basis of rna recognition by the human alternative splicing factor fox-1. The EMBO journal 25, 163–173 (2006).
    OpenUrlAbstract/FREE Full Text
  49. 49.↵
    Lunde, B. M., Moore, C. & Varani, G. Rna-binding proteins: modular design for efficient function. Nat. Rev. Mol. Cell Biol. 8, 479–490 (2007).
    OpenUrlCrossRefPubMedWeb of Science
  50. 50.↵
    Lovci, M. T. et al. Rbfox proteins regulate alternative mrna splicing through evolutionarily conserved rna bridges. Nat. Struct. & Mol. Biol. 20, 1434 (2013).
    OpenUrlCrossRefPubMed
  51. 51.↵
    Ray, D. et al. Rapid and systematic analysis of the rna recognition specificities of rna-binding proteins. Nat. Biotechnol. 27, 667–670 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  52. 52.↵
    Aviv, T., Lin, Z., Ben-Ari, G., Smibert, C. A. & Sicheri, F. Sequence-specific recognition of rna hairpins by the sam domain of vts1p. Nat. Struct. & Mol. Biol. 13, 168–176 (2006).
    OpenUrlCrossRefPubMed
  53. 53.↵
    Aviv, T. et al. The nmr and x-ray structures of the saccharomyces cerevisiae vts1 sam domain define a surface for the recognition of rna hairpins. J. Mol. Biol. 356, 274–279 (2006).
    OpenUrlCrossRefPubMed
  54. 54.↵
    Wang, T. et al. Design and bioinformatics analysis of genome-wide clip experiments. Nucleic Acids Res. 43, 5263–5274 (2015).
    OpenUrlCrossRefPubMed
  55. 55.↵
    Friedersdorf, M. B. & Keene, J. D. Advancing the functional utility of par-clip by quantifying background binding to mrnas and lncrnas. Genome Biol. 15, R2 (2014).
    OpenUrlCrossRefPubMed
  56. 56.↵
    Weirauch, M. T. et al. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126–134 (2013).
    OpenUrlCrossRefPubMed
  57. 57.↵
    Wheeler, E. C., Van Nostrand, E. L. & Yeo, G. W. Advances and challenges in the detection of transcriptome-wide protein–rna interactions. Wiley Interdiscip. Rev. RNA 9, e1436 (2018).
    OpenUrl
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Posted September 09, 2020.
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Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks
Peter K. Koo, Matthew Ploenzke, Praveen Anand, Steffan B. Paul, Antonio Majdandzic
bioRxiv 2020.09.08.288068; doi: https://doi.org/10.1101/2020.09.08.288068
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