Deep Learning Framework for Genetic Regulatory Activity Prediction
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Solution Overview
Problem
Current methods lack an integrated, global view of sequence regulatory activities, limiting the interpretability and effectiveness of sequence-based analysis of genomic variants and human genetics data.
Innovation Solution
A deep learning sequence model-based framework predicts comprehensive chromatin profiles for any sequence or variant, mapping sequences to regulatory activities quantitatively using a novel vocabulary of sequence classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for sequence-based analysis, then analysis can be performed, but interpretability and effectiveness are limited due to lack of integrated global view
Solution Approach 1:
The patent segments the complex regulatory landscape into discrete sequence classes (e.g., promoters, enhancers, silencers, insulators) with specific functional characteristics. Each sequence class is defined by unique combinations of transcription factor binding sites, chromatin accessibility patterns, and epigenetic marks, enabling precise interpretation of regulatory activities without requiring analysis of the entire genome simultaneously.
Solution Approach 2:
The patent introduces sequence classes as intermediary categories that bridge raw sequence data and regulatory function interpretation. These sequence classes serve as mediators that translate complex genomic variants into understandable regulatory outcomes by mapping variants to specific sequence class disruptions or modifications, thereby improving interpretability while managing complexity.
2Loss of information
If comprehensive chromatin profiles are predicted for any sequence, then global quantitative map of regulatory activities is provided, but computational model complexity increases
Solution Approach 1:
The patent develops a universal deep learning model ( Sei) that can predict multiple types of chromatin profiles simultaneously from a single sequence input. The model integrates predictions for histone modifications, chromatin accessibility, transcription factor binding, and other regulatory features into a unified framework, reducing the need for multiple separate models while capturing comprehensive regulatory information.
Solution Approach 2:
The patent employs a nested architecture where the deep learning model incorporates multiple levels of feature extraction and integration. The model nests sequence features, motif patterns, chromatin states, and regulatory element predictions within a hierarchical structure, allowing comprehensive regulatory profile prediction while managing computational complexity through organized feature integration.
3Measurement precision
If sequences are mapped to regulatory activities quantitatively using sequence classes, then variant effects on transcriptional regulation can be predicted, but analysis complexity increases
Solution Approach 1:
The patent applies local quality by assigning specific functional properties to different sequence classes based on their local genomic context. Each sequence class is characterized by local features such as specific transcription factor combinations, chromatin accessibility patterns, and epigenetic marks that define its regulatory function. This allows precise prediction of variant effects by evaluating changes within specific local sequence class contexts rather than requiring global genome-wide analysis.
Data Source
AI summary
Processes that determine transcriptional regulation from genetic sequence data are described. Generally, computational models are trained to predict transcriptional regulatory effects, which can be used in several downstream applications. Various methods further develop research tools, develop and perform diagnostics, and treat individuals based on identified variants.