Toneyan, Shushan, Koo, Peter K (September 2024) Interpreting cis-regulatory interactions from large-scale deep neural networks. Nature Genetics. ISSN 1546-1718 (Public Dataset)
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 orthogonal experimental data, providing insights into generalization but offering limited insights into their decision-making process. Existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we present cis-regulatory element model explanations (CREME), an in silico perturbation toolkit that interprets the rules of gene regulation learned by a genomic DNN. Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions. CREME can provide interpretations across multiple scales of genomic organization, from cis-regulatory elements to fine-mapped functional sequence elements within them, offering high-resolution insights into the regulatory architecture of the genome. CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > computational biology > algorithms bioinformatics > computational biology |
| CSHL Authors: | |
| Communities: | CSHL labs > Koo Lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 16 September 2024 |
| Date Deposited: | 18 Sep 2024 12:27 |
| Last Modified: | 18 Sep 2024 12:27 |
| Dataset ID: |
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| URI: | https://repository.cshl.edu/id/eprint/41673 |
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