Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics.

ABSTRACT: The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with experimental perturbation assays, which provides insights into the generalization capabilities within the studied loci but offers a limited perspective of what drives their predictions. Moreover, existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we introduce CREME, an in silico perturbation toolkit that interrogates large-scale DNNs to uncover rules of gene regulation that it learns. Using CREME, we investigate Enformer, a prominent DNN in gene expression prediction, revealing cis-regulat

SUBMITTER: Toneyan S 

PROVIDER: S-EPMC10349992 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature