RNA Mutation Expression Quantification via Read Pair Classification
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Solution Overview
Problem
Current methods for quantifying RNA expression levels, particularly for neoantigens derived from somatic mutations in tumors, often miss insertions and deletions, leading to misquantification and failure to accurately identify relevant neoantigens for cancer therapies.
Innovation Solution
A system and method for quantifying RNA mutation expression by classifying read pairs as consistent with or inconsistent with specific alleles and isoforms, using contiguously aligned regions and splice junction configurations, to accurately identify neoantigens and their isoforms, thereby enabling the development of targeted cancer therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for quantifying RNA expression levels are used, then the quantification process is simple, but insertions and deletions are missed leading to misquantification
Solution Approach 1:
The patent segments the RNA quantification process into distinct stages: read pair classification based on contiguously aligned regions, splice junction configuration analysis, and mutation consistency evaluation. This segmentation allows each aspect to be handled separately with specialized algorithms, improving detection accuracy for insertions and deletions while maintaining manageable system complexity through modular processing steps
Solution Approach 2:
The patent introduces an intermediary classification system that evaluates read pairs against reference genomes and mutation databases before final quantification. This intermediary step acts as a mediator that filters and validates data, ensuring that insertions and deletions are properly identified and counted, thereby improving measurement precision without directly increasing the core quantification complexity
2Reliability
If read pairs are classified based on contiguously aligned regions and splice junction configurations, then neoantigens are accurately identified, but processing time increases
Solution Approach 1:
The patent performs preliminary classification of read pairs by evaluating contiguously aligned regions and splice junction configurations before full neoantigen analysis. This preliminary action pre-sorts and pre-validates data, so that when neoantigens are identified, the foundation is already established, reducing the time required for subsequent detailed processing while maintaining high identification accuracy
Solution Approach 2:
The patent applies partial action by focusing classification efforts on specific regions (contiguously aligned regions and splice junctions) that are most critical for neoantigen identification rather than analyzing entire transcripts uniformly. This selective approach concentrates computational resources on high-value areas, improving reliability where it matters most while limiting time consumption through targeted rather than exhaustive analysis
3Loss of information
If isoform-specific mutation expression is quantified, then relevant neoantigens are prioritized, but computational requirements increase
Solution Approach 1:
The patent applies local quality by quantifying mutation expression specifically at the isoform level rather than treating all transcripts uniformly. This allows the system to identify which specific isoforms express relevant neoantigens, preserving critical relevance information. The computational complexity is managed by applying this detailed analysis only where needed (at isoform-specific locations) rather than across the entire transcriptome
Data Source
AI summary
A method for quantifying ribonucleic acid (RNA) mutation expression. For each read pair of a read pair group, a set of contiguously aligned regions and a splice junction configuration are identified. Each read pair is within a selected range of a location of interest. Each read pair of the read pair group is classified based on the set of contiguously aligned regions and the splice junction configuration that correspond to each read pair, a reference genome, and a selected mutation. A mutation-centric output is generated for the read pair group.