Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks

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. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a 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 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.

Discovering differential genome sequence activity with interpretable and efficient deep learning

“…This in combination with the large number overlap of Nrf1 and Tcf12 binding sites suggests Nrf1 and Tcf12 binding may be regulated by logic beyond the presence or absence of their DNA binding motifs. We also show that under class imbalanced data, EPE and DEPE generate robust estimates of transcription factor effects in contrast to Global Importance Analysis [ 15 ], which takes a related approach but uses a difference instead of a ratio to estimate the effect of a pattern ( S2 Fig ).…”

“…In these cases, explicitly training models on each differential comparison between cell types would be computational expensive and time-intensive. We also note that Expected Pattern Effect resembles other methods for extracting pattern effects from deep neural networks [3,15], except that we compute Expected Pattern Effect to permit the comparison of pattern effects between conditions, which is important for identification of cell type-specific or condition-specific sequence features and show that our use of ratio to compare effects is more robust to analysis of cell type-specific transcription factor activity under class imbalance.…”

Domain-adaptive neural networks improve cross-species prediction of transcription factor binding

“…First, we set out to evaluate the ability of neural networks to predict TF binding in a previously unseen species. We chose neural networks owing to their ability to learn arbitrarily complex predictive sequence patterns ( Kelley et al 2018 ; Fudenberg et al 2020 ; Avsec et al 2021a , b ; Koo et al 2021 ). In particular, hybrid convolutional and recurrent network architectures have successfully been applied to accurately predict TF binding in diverse applications ( Quang and Xie 2016 ; Quang and Xie 2019 ; Srivastava et al 2021 ).…”

Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models

“…Closer to our work, [25]'s Global Importance Analysis (GIA) assesses the global effect size of different patterns on model predictions. While resembling DeepMR in terms of its focus on global effects, GIA allows users to test narrower hypotheses about specific features such as motifs and uses synthetic instead of observed sequences.…”