B-field Data Structure for Fast Biological Sequence Search
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Solution Overview
Problem
Current biological sequence search and analysis methods are computationally expensive and slow, requiring large databases and significant computing resources, making them inefficient for large datasets such as those generated from genome sequencing.
Innovation Solution
The implementation of a probabilistic data structure called the B-field data structure, which enables efficient storage and lookup of key-value pairs, allowing for fast DNA sequence searches by encoding values into binary strings and using hash functions for insertion and lookup operations, resulting in significantly faster query times and reduced computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional BLAST or LAST methods are used for biological sequence search, then search accuracy is maintained, but search speed is slow and computational resources are heavily consumed
Solution Approach 1:
The patent extracts only the essential features of biological sequences by representing them as sets of k-mers (short subsequences) rather than storing or processing complete sequence data. This extraction allows for rapid comparison and search operations while maintaining the ability to identify meaningful biological relationships, resolving the contradiction between speed and computational efficiency.
Solution Approach 2:
The invention changes the parameter representation from complete biological sequences to simplified k-mer sets with associated abundance values. By transforming the data representation parameters, the system enables much faster search operations (three orders of magnitude improvement) while preserving the ability to perform accurate biological sequence analysis.
2Reliability
If complete biological sequence databases are stored and searched, then comprehensive analysis is possible, but storage requirements and computational overhead increase significantly
Solution Approach 1:
The patent extracts only the essential compositional features (k-mer sets and abundance values) from complete biological sequences, storing only these extracted features rather than the full sequence data. This enables comprehensive comparative analysis while dramatically reducing storage requirements, as the extracted features capture the essential biological information needed for reliable analysis.
Solution Approach 2:
The invention segments biological sequences into discrete k-mer units, transforming continuous sequence data into discrete, countable elements. This segmentation enables efficient storage and processing by allowing the system to work with sets of k-mers and their abundances rather than storing and manipulating complete sequence strings, thus reducing storage while maintaining analytical completeness.
3Productivity
If probabilistic data structures are used to reduce storage and improve speed, then search efficiency increases, but measurement precision may be affected
Solution Approach 1:
The patent replaces traditional mechanical sequence alignment methods with a probabilistic data structure approach using hash sets and abundance counting. This substitution uses statistical principles rather than exact mechanical matching, achieving both improved search efficiency and maintained measurement precision through probabilistic analysis of k-mer composition and abundance patterns.
Data Source
AI summary
A system and method for resolving data through a probabilistic data structure can include initializing a B-field data structure, inserting a key-value element into the B-field data structure, selecting at least one key query, and looking up the value of a key lookup request through the B-field data structure.