Tissue-Sample Gene Identification With Sequence Comparison
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Solution Overview
Problem
Current methods for gene identification face challenges such as complex genome assembly, errors in annotation, false-positive/negative results, and inaccuracies, particularly in non-model organisms, leading to incomplete or fragmented gene annotations.
Innovation Solution
A method involving pre-processing of genomic data to remove noise, followed by data analysis using bioinformatics tools and visualization techniques, and validation through experimental methods to ensure accurate gene identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional high-throughput sequencing and computational assembly methods are used for gene identification, then comprehensive genome coverage is achieved, but the process becomes complex and prone to errors in annotation and assembly
Solution Approach 1:
The patent segments the complex genome assembly problem into smaller, manageable contigs by using overlapping sequences from multiple sources (tissue sample, database, synthetic constructs). Each contig is assembled independently and then integrated, reducing the overall complexity and error rate of the assembly process
Solution Approach 2:
The patent introduces an intermediary validation step where obtained sequences are compared against database sequences and synthetic construct sequences before final assembly. This intermediary comparison process acts as a mediator to verify sequence accuracy and resolve ambiguities, improving reliability without significantly increasing complexity
2Productivity
If automated gene prediction algorithms are used, then gene identification speed is improved, but false-positive and false-negative results increase
Solution Approach 1:
The patent implements a feedback mechanism where initial gene predictions are validated by comparing obtained sequences with database sequences and synthetic construct sequences. Discrepancies trigger re-analysis and refinement of predictions, creating a feedback loop that improves accuracy while maintaining efficiency through automated iterative validation
Solution Approach 2:
The patent performs preliminary validation of sequences before final gene annotation by comparing them against known database sequences and synthetic constructs. This preliminary action filters out obvious false positives and negatives early in the process, allowing faster subsequent analysis with higher confidence in results
3Measurement precision
If comprehensive validation methods are used to reduce errors, then gene identification accuracy improves, but time and resource consumption increase
Solution Approach 1:
The patent applies partial validation by comparing sequences against database entries and synthetic constructs for specific critical regions rather than performing exhaustive validation of entire genomes. This selective validation approach achieves sufficient accuracy for gene identification while significantly reducing time and computational resources required
Data Source
AI summary
The present disclosure relates to a method for identifying a particular gene from a tissue sample is disclosed. The method includes obtaining a first sequence from the tissue sample, wherein the sequence is associated with a particular gene. The method further includes comparing the first sequence with a second sequence, wherein the second sequence is associated with another gene. The method further includes determining whether the first sequence is different from the second sequence, and thereby identifying the particular gene.