Documentation#
BRIDGE is an advanced multimodal deep learning framework for predicting dynamic RNA–protein binding landscapes and assessing the functional impact of genetic variants across multiple human cell types. It leverages a unified architecture that integrates:
Sequence embeddings from pretrained Transformer models to capture rich contextual nucleotide representations.
RNA secondary structure features to model the spatial and thermodynamic constraints on RBP binding.
Motif priors derived from de novo motif discovery (STREME) to incorporate known binding patterns.
Biochemical profiles capturing experimental signals such as reactivity, accessibility, and conservation.
Graph-based attention modeling to represent long-range dependencies between nucleotides via token-wise relational graphs.
By fusing these complementary modalities, BRIDGE can accurately characterize both conserved and dynamic binding preferences, enabling:
End-to-end model training and evaluation on large-scale eCLIP/HITS-CLIP datasets.
Dynamic cross-cell-type transfer prediction, where the model generalizes to unseen cellular contexts without fine-tuning.
Variant-aware inference, assessing the functional impact of genetic variants (e.g., SNVs) on RBP binding to facilitate disease and trait association studies.
Explicit motif extraction highlighting dynamic sequence–structure patterns learned from the fused modalities.
This multimodal and interpretable design positions BRIDGE as a powerful tool for dissecting post-transcriptional regulation, guiding functional genomics studies, and prioritizing disease-associated variants with potential regulatory impact.
New to BRIDGE? Check out the installation guide.
The API reference contains a detailed description of the BRIDGE API.
The tutorials walk you through real-world applications of BRIDGE models.