Gene Location and Sequence Variant Prioritization for Trait Breeding
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Solution Overview
Problem
Existing methods for identifying gene locations and sequence variants associated with specific traits in plants are time-consuming and resource-intensive, particularly when verifying modifications without negatively affecting other traits.
Innovation Solution
A method involving the use of ordered candidate lists, a reference set, and evaluation values to efficiently identify and select gene locations and sequence variants linked to desired phenotypic traits, utilizing genomic data and statistical analysis to prioritize candidates for further testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to identify gene locations and sequence variants associated with specific traits, then comprehensive analysis can be performed, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent applies preliminary action by performing in silico verification of candidate genes and sequence variants before conducting time-consuming wet lab experiments. The computational pipeline pre-filters candidates based on genomic data, candidate list generation, and in silico validation, ensuring only the most promising candidates proceed to experimental verification. This significantly reduces the time required for the overall identification process while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent segments the gene identification process into distinct computational stages: genomic data processing, candidate list generation, in silico verification, and prioritization. By dividing the comprehensive analysis into modular segments, the system can efficiently process large datasets and identify candidates without requiring exhaustive analysis of all possible genes, thus reducing time loss while preserving measurement precision.
2Measurement precision
If comprehensive genomic analysis is performed to identify all potential candidates, then accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on analyzing only the most relevant genomic regions and candidates that have the highest probability of being associated with the target trait. The system generates prioritized candidate lists based on preliminary genomic data analysis and in silico verification, allocating computational resources efficiently to verify only the top candidates rather than performing exhaustive analysis on all possible genes. This reduces resource consumption while maintaining high accuracy through targeted verification.
Solution Approach 2:
The patent changes parameters by adjusting the stringency and scope of genomic analysis at different stages of the pipeline. The system dynamically adjusts analysis depth, candidate filtering criteria, and verification thresholds based on the specific trait being investigated and the quality of available genomic data. This allows the system to achieve accurate candidate identification with optimized resource usage by adapting analysis parameters to the specific requirements of each case.
3Reliability
If multiple candidate lists are generated and verified, then the likelihood of identifying the correct trait gene increases, but the complexity of the process increases
Solution Approach 1:
The patent merges multiple candidate lists generated from different genomic analysis approaches into a single prioritized candidate list. The system integrates results from various genomic data sources, candidate generation methods, and in silico verification outcomes, combining them through a unified computational framework. This merging process increases reliability by cross-validating candidates across multiple approaches while reducing process complexity by consolidating multiple verification streams into a single integrated pipeline.
Solution Approach 2:
The patent implements feedback mechanisms where the results from in silico verification of candidate lists are fed back into the candidate prioritization process. The system uses verification outcomes to refine and update candidate lists, adjusting priorities based on what is learned from each verification round. This feedback loop increases reliability by iteratively improving candidate selection while managing complexity through automated feedback-driven refinement rather than manual analysis of multiple separate lists.
Data Source
AI summary
The present application is directed to a method for identifying at least one candidate (Loc), namely a gene location and/or a sequence variant, indicative for at least one selected (phenotypic) trait of an organism, in particular of a plant, comprising the steps of:a. receiving a plurality of candidate lists (Can1, Can2, Can3) of candidates (Loc), the candidate lists being ordered;b. receiving a reference set (RefDB) with gene locations and/or sequence variants;c. matching at least a subset of candidates (Loc) from the candidate lists (Can1, Can2, Can3) with the reference list (RefDB) to determine an evaluation value (EV) for at least the subset;d. assigning each evaluation value (EV) to the respective candidate (Loc) in the respective candidate lists (Can1, Can2, Can3);e. calculating for each candidate list a performance value based on the evaluation value (EV), in particular by using the evaluation values (EV);f. selecting at least one candidate (Loc) as (preferred) candidate (Loc) from one of the candidate lists (Can1, Can2, Can3) using the performance values.