Computational Framework for Predicting Gene Expression from DNA Sequence
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Solution Overview
Problem
Current methods are limited in accurately predicting gene expression levels from DNA sequences, especially for rare or unobserved variants, and struggle to determine the causal effects of genomic variations on gene transcription, which is crucial for understanding disease and trait associations.
Innovation Solution
A computational framework that integrates deep learning with spatial feature transformation and L2-regularized linear models to predict gene expression levels from a wide regulatory region, enabling the identification of causal variants and their effects on gene expression without relying on variant information, thus applicable to all possible variants, including rare ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional genome-wide association studies are used to identify variants affecting gene expression, then common variants can be detected, but rare or unobserved variants cannot be accurately predicted
Solution Approach 1:
The patent creates a computational model that copies the biological relationship between DNA sequence and gene expression by training on observed variants and their expression effects. This trained model then serves as a predictive copy that can estimate effects of unobserved rare variants without requiring actual observation of those specific variants in the training data
Solution Approach 2:
The patent transforms the approach from directly observing variants to predicting expression levels based on DNA sequence parameters. By changing the predictive parameters from variant-specific observations to sequence-based computational features, the system can generalize to rare variants that were not present in the training data
2Reliability
If computational models are trained on observed variant data, then common variants can be predicted, but the causal effects of rare variants remain undetermined
Solution Approach 1:
The patent replaces traditional statistical association methods with a deep learning computational framework. This substitution enables the system to model complex non-linear relationships between DNA sequence features and gene expression, thereby identifying causal variants with higher reliability while managing computational complexity through efficient model architecture design
3Loss of information
If genome-wide association studies are performed, then disease associations can be identified, but the underlying gene transcription mechanisms remain unclear
Solution Approach 1:
The patent introduces gene expression level as an intermediary variable that connects DNA sequence variants to disease associations. By measuring and incorporating expression levels into the analysis, the system retrieves lost mechanistic information about how variants affect disease risk through transcriptional regulation, while maintaining efficient disease association identification
Data Source
AI summary
Processes to determine the effect of genetic sequence on gene expression levels are described. Generally, models are used to determine spatial chromatin profile from genetic sequence, which can be used in several downstream applications. The effect of the spatial chromatin profile on gene expression is also determined in some instances. Various methods further develop research tools, perform diagnostics, and treat individuals based on sequence effects on gene expression levels.