Aligned Pattern Clusters for Sequence Variation Discovery
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Solution Overview
Problem
Existing bioinformatics techniques face challenges in efficiently discovering and analyzing sequence patterns with variations in macromolecular sequences, particularly due to high computational complexity and limitations in handling large datasets and dissimilar sequences, leading to large solution sets and inadequate representation of amino acid associations.
Innovation Solution
A method and system for discovering sequence patterns with variations by accessing a dataset of macromolecular sequences, applying a pattern discovery process to generate statistically significant patterns, grouping and aligning similar patterns into Aligned Pattern Clusters, and using statistical analysis to support the analysis of these clusters, enabling the identification of related and distal sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sequence alignment is used to identify conserved regions, then functional patterns can be detected, but computational complexity becomes NP-complete and efficiency deteriorates with large datasets
Solution Approach 1:
The patent segments the global alignment problem into local pattern discovery tasks. Instead of aligning entire sequences globally, the system identifies and analyzes local conserved regions independently, transforming the NP-complete global alignment problem into manageable local pattern matching tasks that can be processed efficiently.
Solution Approach 2:
The patent extracts conserved regions from sequences before performing alignment operations. By pre-identifying suspected consensus regions and extracting them for separate analysis, the system avoids the computational burden of aligning entire sequences while maintaining the ability to detect functional patterns in these extracted regions.
2Measurement precision
If multiple sequence alignment is applied, then conserved regions can be identified, but the method is only suitable for highly similar sequences and not for sequences with considerable dissimilarity
Solution Approach 1:
The patent applies local quality by focusing analysis on specific conserved regions rather than requiring global sequence similarity. Each local region is analyzed for its own pattern characteristics, allowing the system to detect functional motifs even when the rest of the sequences are highly divergent. This local-focused approach enables the detection of conserved functional elements across distantly related sequences.
3Device complexity
If prior art probabilistic methods are used to compress data into probability distributions, then sequence patterns can be represented, but complex amino acid associations cannot be expressed with statistical support
Solution Approach 1:
The patent introduces conserved region extraction as an intermediary step between raw sequence data and probability distribution analysis. By first identifying and extracting conserved regions with high confidence, the system creates an intermediate representation that preserves complex amino acid associations while enabling subsequent probabilistic analysis on a reduced, more manageable dataset that maintains the essential association information.
Data Source
AI summary
A system and method of discovering sequence patterns with variations is provided. The method includes: accessing or acquiring a data set including a family of sequences or related families of sequences; a) applying a pattern discovery process to the sequences; b) grouping and aligning the similar patterns that may have different lengths into one or more Aligned Pattern Clusters; c) discovering the co-occurrence relation between Aligned Patterns and/or Aligned Pattern Clusters to reveal the distal function between segments represented by the aligned Pattern Clusters and d) breaking down an Aligned Pattern Cluster into sub-clusters with stable cluster configuration that reveals sub-clusters with distinct and shared characteristic among sub-family of the sequences.