Transcriptionally Active Region Quantification via Genomic Binning
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Solution Overview
Problem
Current DNA sequencing technologies face challenges in identifying transcriptionally active regions and quantifying sequence abundance, particularly for non-coding RNAs, due to inexact mapping and high computational intensity in high-throughput sequencing settings.
Innovation Solution
A sequence data analysis system using a reference sequence-based design that aligns sequencing output sequences to reference sequences, calculates coverage counts, and determines contiguous high-coverage regions to identify transcriptionally active regions and quantify their abundance, reducing computational complexity through coordinate comparisons rather than traditional sequence alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequence alignment methods are used to identify transcriptionally active regions, then mapping accuracy may be maintained, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent segments the reference genome into discrete bins or intervals, and assigns sequencing reads to these bins based on positional information rather than performing full sequence alignment. This segmentation transforms the continuous sequence matching problem into discrete bin assignment, dramatically reducing computational complexity while maintaining the ability to identify transcriptionally active regions through coverage depth analysis.
Solution Approach 2:
The patent introduces an intermediary mapping approach where reads are mapped to genomic coordinates or bins without requiring precise sequence alignment. This intermediary representation (genomic position/ bin assignment) serves as a mediator between the raw sequencing data and the final expression quantification, enabling faster processing while preserving the essential information needed for identifying active transcription regions.
2Reliability
If comprehensive sequence alignment is performed for all sequencing output, then accurate identification of transcriptionally active regions is achieved, but computational resources and storage requirements increase
Solution Approach 1:
The patent performs preliminary binning or interval assignment of sequencing reads based on their genomic coordinates before any detailed analysis. This preliminary action organizes the data into discrete bins that represent potential transcriptionally active regions, allowing subsequent analysis to focus only on these pre-identified regions rather than processing the entire genome sequence data, thus reducing computational complexity while maintaining reliability.
Solution Approach 2:
The patent transitions from sequence-space analysis to genomic coordinate-space analysis by mapping reads to specific genomic positions or bins. This dimensional change from comparing sequence strings to analyzing coverage depth across genomic intervals simplifies the computational problem while preserving the ability to accurately identify transcriptionally active regions through coverage-based metrics.
3Ease of manufacture
If traditional RNA expression profiling methods are used, then established protocols are followed, but flexibility for small and non-coding RNA expression profiling is limited
Solution Approach 1:
The patent employs a universal binning-based approach that can handle diverse RNA types (coding and non-coding) uniformly. By representing all sequencing reads in terms of their genomic bin assignments rather than requiring type-specific alignment protocols, the method achieves multi-functionality across different RNA species while maintaining a standardized analytical framework, thus enhancing versatility without sacrificing protocol standardization.
Data Source
AI summary
This invention provides a quantitative method to determine transcriptionally active regions and quantify sequence abundance from large scale sequencing data. The invention also provides a system based on reference sequences to design and implement the method. The system processes large scale sequence data from high throughput sequencing, generates transcriptionally active region sequences as necessary, and quantifies the sequence abundance of the gene or transcriptionally active region. The method and system are useful for many analyses based on RNA expression profiling.