Gene Sequence Similarity Search Using Sliding Window Segmentation
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Solution Overview
Problem
Existing similarity search algorithms are not well-suited for gene sequences due to their large data amounts and complex base character combinations, making it inefficient to determine similarity and achieve effective data compression.
Innovation Solution
A computer-implemented method using a sliding window to extract parts of gene sequences and determine similarity based on these parts, allowing for the selection of a reference gene sequence that minimizes difference data, thereby compressing gene sequences effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing similarity search algorithms are used for gene sequences, then the search process can be performed, but the determination of similarity is inefficient and inaccurate due to large data amounts and complex base character combinations
Solution Approach 1:
The patent divides the gene sequence into multiple k-mers (subsequences of length k). Instead of comparing entire gene sequences directly, the method segments them into smaller units that can be processed independently and more efficiently, resolving the contradiction between accuracy and efficiency in similarity determination.
Solution Approach 2:
The patent transforms the gene sequence data by converting it into k-mer frequency vectors or presence-absence vectors. This parameter transformation converts the complex base character combination problem into a numerical vector comparison problem, enabling more efficient and accurate similarity calculation using standard computational methods.
2Quantity of substance
If a large amount of gene sequence data is stored, then comprehensive data availability is achieved, but storage space requirements and data transmission costs increase significantly
Solution Approach 1:
The patent creates compressed representations of gene sequences by storing only essential features (such as k-mer frequency vectors or presence-absence patterns) rather than the complete raw sequence data. This allows comprehensive data availability to be maintained while significantly reducing storage space requirements and transmission costs.
3Loss of substance
If reference gene sequences are used for data compression, then storage efficiency is improved, but finding the best matching reference sequence becomes computationally intensive
Solution Approach 1:
The patent segments the gene sequences into k-mers and represents them as frequency vectors or presence-absence vectors. This segmentation enables efficient comparison between query sequences and reference sequences by operating on these compressed representations rather than full sequences, reducing computation complexity while maintaining storage efficiency benefits.
Solution Approach 2:
The patent transforms gene sequences into numerical parameter spaces (k-mer frequency vectors). This parameter transformation enables the use of efficient distance metrics and indexing methods for finding best-matching reference sequences, significantly reducing computational complexity compared to direct sequence alignment methods.
Data Source
AI summary
A method and apparatus for determining similarity among gene sequences, for compressing a gene sequence, and for decompressing a gene sequence. The method for determining similarity between a first gene sequence and a second gene sequence includes: moving a sliding window of a predefined length on the first gene sequence and the second gene sequence respectively; extracting a first part String1i of the first gene sequence within the sliding window, and a second part String2i of the second gene sequence within the sliding window during the ith movement of the sliding window; and determining similarity between the first gene sequence and the second gene sequence based on the first part String1i and the second part String2i. Also provided is an apparatus for the above method.