Gene Regulatory Element Prediction With Cross-Cell-Type Expression Modeling
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Solution Overview
Problem
Existing methods for predicting gene expression and regulatory mechanisms are limited by their inability to perform cross-cell-type and cross-organism predictions, rely on bulk cell data that fail to capture cell-type-specific regulatory modules, and are biased towards regulatory modules near the transcription start site, lacking a generalizable understanding of transcriptional regulation.
Innovation Solution
A general expression transformer (GET) model utilizing unsupervised transfer learning and a comprehensive training dataset across 213 human cell types, enabling cross-cell-type expression modeling and predicting gene expression in both seen and unseen cell types, while incorporating multiple layers of biological information such as chromatin accessibility and transcription factor interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use bulk cell data for prediction, then computational simplicity is maintained, but cell-type-specific regulatory modules cannot be captured
Solution Approach 1:
The patent segments bulk cell data into cell-type-specific datasets by training separate predictive models for different cell types. This segmentation enables capture of cell-type-specific regulatory modules while maintaining computational feasibility through modular model architecture.
Solution Approach 2:
The patent adds a cell-type dimension to the prediction framework by incorporating cell-type-specific training data and models. This dimensional expansion allows simultaneous analysis across multiple cell types, capturing specificity while leveraging cross-cell-type patterns.
2Reliability
If methods focus on regulatory modules near transcription start site, then prediction reliability is improved, but generalizability to distant regulatory elements is lost
Solution Approach 1:
The patent creates a universal prediction framework that functions across multiple genomic regions and cell types. The model architecture is designed to handle both proximal and distal regulatory elements uniformly, enabling generalization while maintaining reliability through cell-type-specific training.
Solution Approach 2:
The patent performs preliminary training on cell-type-specific data before making predictions on new genomic regions. This preliminary action establishes cell-type-specific parameter sets that improve reliability when predicting effects of regulatory elements at any distance from transcription start sites.
3Adaptability or versatility
If cross-cell-type training data is used, then model generalizability is improved, but computational resources and training complexity increase
Solution Approach 1:
The patent segments the training process into cell-type-specific modules, allowing parallel training that can be distributed across computational resources. This segmentation reduces memory requirements per model while achieving cross-cell-type generalizability through ensemble prediction.
Solution Approach 2:
The patent adjusts model parameters and architecture based on available computational resources. The framework allows trade-offs between model depth, number of cell-type-specific models, and computational budget, enabling deployment on various hardware platforms.
Data Source
AI summary
The present disclosure provides methods and systems for identifying transcriptional regulatory modules (e.g., in non-coding portions of the genome), predicting gene regulation and expression, e.g., effects of non-coding mutations or chromosome rearrangements on the regulation and expression of the target genes, and designing and using modified regulatory sequences.