Alu Element Bioinformatics for Cancer Detection
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Solution Overview
Problem
Current methods for predicting human genome instability, particularly in cancer-linked regions, are limited by small sample sizes and lack of precision in identifying the contributions of various repetitive element interactions, which hinders early detection and treatment of cancer.
Innovation Solution
The development of algorithms that incorporate the interactions of all human inverted repetitive element pairs, including Alu-Alu, L1-L1, Alu-L1, and L1-SVA pairs, to estimate genetic instability, using bioinformatic methods and considering the stability of flanking DNA regions to predict genomic damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for predicting human genome instability are used, then early detection of cancer can be achieved, but the precision and reliability are limited due to small sample sizes and inability to identify contributions of various repetitive element interactions
Solution Approach 1:
The patent segments the complex problem of genome instability prediction by analyzing individual repetitive element pairs (Alu-Alu, L1-L1, Alu-L1, L1-SVA) separately. Each pair type is evaluated independently to determine its specific contribution to instability, allowing precise quantification of each element's role while maintaining overall prediction accuracy and reliability.
2Adaptability or versatility
If algorithms incorporate interactions of all human inverted repetitive element pairs, then comprehensive estimation of genetic instability is achieved, but the complexity of the bioinformatic methodology increases
Solution Approach 1:
The methodology divides the comprehensive analysis into manageable segments by evaluating each repetitive element pair type (Alu-Alu, L1-L1, Alu-L1, L1-SVA) separately. This segmentation allows the system to incorporate multiple element types without overwhelming complexity, as each segment can be processed and interpreted independently while contributing to the overall comprehensive estimation.
Solution Approach 2:
The patent develops a universal bioinformatic framework that can handle multiple types of repetitive element interactions through a single integrated methodology. This universal approach uses consistent algorithms and metrics across different element pairs, allowing comprehensive analysis without requiring separate complex systems for each element type.
3Device complexity
If small sample sizes are used in predicting human genome instability, then the bioinformatic methodology is simpler, but the precision in identifying contributions of repetitive element interactions is reduced
Solution Approach 1:
The patent performs preliminary classification and organization of repetitive element data before analysis, pre-grouping elements by type (Alu, L1, SVA) and orientation. This preliminary action structures the data in advance, allowing the subsequent analysis to achieve high precision in identifying element contributions without requiring excessively complex methodologies or large sample sizes.
Data Source
AI summary
The present invention describes a bioinformatic method that can be used in the estimation of an individual's susceptibility to cancer through an evaluation of that individual's personal genome sequence. More specifically, this invention is a continuation-in-part of the methodology described in patent application Ser. No. 14/154,303 for the early detection of cancer. Said method is based upon an analysis of the structure of the repetitive DNA sequences surrounding and within the various cancer-linked regions of the individual's genome being evaluated. Said analysis of said individual's genome is then compared to the same analysis conducted for one or more reference genomes and/or genes for which cancer susceptibility has been previously determined. Said analysis can also be used to estimate the respective likelihoods that each cancer-linked genomic region will be damaged in the potential formation of a tumor. This patient-specific analysis can then be used in the economical design of locus-specific monitoring for early genetic damage as part of pre-cancer genetic screening.