Biological Sequence Fingerprinting for Computational Complexity Reduction
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Solution Overview
Problem
Current biological data handling tools are inefficient in processing vast amounts of sequence data due to the NP-hard nature of sequence alignment and similarity searches, leading to slow processing speeds and high error rates, particularly in identifying structural variants and relating biological information from different sources.
Innovation Solution
A computer-implemented method using fingerprint data strings representing characteristic biological subsequences with lower combinatory numbers, allowing for reduced complexity and deterministic sequence generation, which enables faster data analysis and error reduction by linking common information through a repository of fingerprint data strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sequence alignment and similarity search algorithms are used, then comprehensive biological data analysis is achieved, but processing speed is slow and computational resources are excessive
Solution Approach 1:
The patent segments biological sequences into fixed-length k-mer substrings. Instead of comparing entire sequences directly, the system divides them into smaller, manageable units (k-mers) that can be processed independently and in parallel, dramatically reducing computational complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The patent creates a simplified copy representation of biological sequences using k-mer frequency spectra. Rather than storing and processing complete sequence data, the system uses compact k-mer count vectors that replicate the essential compositional information in a much smaller format, enabling faster processing with reduced memory requirements
2Measurement precision
If k-mer technology is used to detect structural variants, then detection window is reduced, but identification accuracy from read data is improved
Solution Approach 1:
The patent transitions from one-dimensional sequence matching to multi-dimensional analysis by examining k-mer frequency spectra across multiple dimensions. This allows detection of structural variants through compositional changes in k-mer distributions, compensating for the reduced detection window by adding analytical depth in frequency space rather than relying solely on linear sequence context
Solution Approach 2:
The patent pre-computes and stores k-mer frequency spectra for reference sequences before actual variant detection. This preliminary processing creates a reusable baseline that enables rapid comparison with sample data, allowing accurate structural variant identification without repeatedly analyzing complete sequence contexts during the detection phase
3Reliability
If high coverage is used to identify structural variants from k-mer data, then detection sensitivity is improved, but computational burden and error accumulation increase
Solution Approach 1:
The patent extracts only the essential compositional information from high-coverage sequencing data by computing k-mer frequency spectra. Instead of processing all individual reads and their associated errors, the system aggregates data into k-mer counts, filtering out random sequencing errors through frequency thresholding while preserving genuine structural variant signals
Solution Approach 2:
The patent replaces complex mechanical sequence alignment processes with mathematical operations on k-mer frequency vectors. By substituting alignment algorithms with simple frequency comparisons and statistical tests, the system achieves reliable structural variant detection with significantly reduced computational resources and without error accumulation from iterative alignment processes
Data Source
AI summary
A computer-implemented method for obtaining information on a biological entity which is based on at least one biological sequence, includes: (a) providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a characteristic biological subsequence made up of sequence units, each characteristic biological subsequence having in the biological sequence database a combinatory number which is lower than the total number of different sequence units available thereto, the combinatory number of a biological subsequence being defined as the number of different sequence units that appear in the biological sequence database as a consecutive sequence unit of the biological subsequence; (b) determining one or more fingerprint data strings which are representative for the biological entity; (c) searching a repository comprising information associated with the fingerprint data strings for information associated with the one or more representative fingerprint data strings; and (d) processing the information.