Non-Coding Regulatory Element Mapping for Precise Gene Pairing
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Solution Overview
Problem
Existing methods fail to accurately determine which regulatory elements control gene expression and assess the quantitative effects on gene regulation, particularly in non-coding regions, as they do not account for the complex interactions between regulatory elements and their target genes, which can be distant in the genome.
Innovation Solution
A method involving obtaining measures of intrinsic activity and proximity of genomic elements, scoring their predicted impact, and using perturbation data from guide RNAs to identify putative regulatory elements and genes, with techniques like CRISPRi and RNA-guided DNA binding proteins to validate these interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If chromatin state and chromosome conformation measurements are used to identify regulatory elements, then the quantity of identified elements increases, but the precision of determining which genes are regulated decreases
Solution Approach 1:
The method segments the regulatory element identification process into two distinct scoring components: intrinsic activity scoring (evaluating chromatin state, conservation, and sequence features) and proximity scoring (evaluating spatial and linear distance to target genes). This segmentation allows each component to be optimized independently while combining to provide comprehensive identification precision.
Solution Approach 2:
The invention changes multiple parameters simultaneously: it integrates multiple chromatin state parameters (H3K27ac, H3K4me1, DNase hypersensitivity), combines spatial proximity parameters (3D contact frequency from Hi-C) with linear distance parameters, and applies conservation parameters across multiple species. This multi-parameter approach resolves the contradiction by providing both comprehensive coverage and precise pairing.
2Ease of operation
If regulatory elements are identified based on proximity to genes, then the ease of identification increases, but the reliability of identifying true regulatory relationships decreases
Solution Approach 1:
The method merges proximity-based identification with intrinsic activity-based identification. The proximity score provides ease of identification by considering spatial and linear distance, while the intrinsic activity score (combining chromatin state, conservation, and sequence features) provides reliability. The combined approach ensures that identified regulatory elements are both accessible and functionally relevant.
Solution Approach 2:
The invention introduces an intermediary scoring system that mediates between simple proximity and complex functional validation. The composite score (intrinsic activity × proximity) acts as an intermediary metric that captures both the accessibility aspect (ease of identification) and the functional relevance aspect (reliability), avoiding the extremes of either approach alone.
3Measurement precision
If a comprehensive scoring system combining multiple factors is used to predict regulatory element impact, then the precision of prediction increases, but the complexity of the method increases
Solution Approach 1:
The comprehensive scoring system is segmented into distinct computational modules: intrinsic activity scoring (with sub-components for chromatin state, conservation, and sequence features) and proximity scoring (with sub-components for spatial and linear distance). This segmentation allows each module to be developed and validated independently while maintaining overall precision.
Solution Approach 2:
The scoring system is designed to be universal and multi-functional: the intrinsic activity score can be computed from various chromatin state measurements, the proximity score can incorporate different spatial and linear distance metrics, and the framework can be applied to any gene-regulatory element pair. This universality reduces complexity by providing a single framework that handles multiple data types and scenarios.
Data Source
AI summary
The application relates to methods for identifying putative regulatory elements that regulates a gene, comprising: obtaining a measure of intrinsic activity of a plurality of genomic elements; obtaining a measure of proximity between each of the genomic elements and the gene; scoring a predicted impact of each of the genomic elements on the gene as a function of the measure of intrinsic activity and the measure of proximity, wherein a plurality of predicted impacts scored are ranked to identify at least one genomic element as a putative regulatory element that regulates the gene; and optionally, training, optimizing, and/or validating the scoring of predicted impact using experimental or computational data describing functional interactions between the genomic elements and the gene. The application also relates to methods for identification of transcriptional enhancers and repressors regulating a gene associated with an agricultural trait of interest in plants or a disease phenotype in mammalians.