Gapped Permutation Pattern Identification in Genome Sequences
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Solution Overview
Problem
Conventional methods are inadequate in identifying gapped permutation patterns in genome sequences, particularly in handling wild cards or gaps and the occurrences of sequences must be exact, while also efficiently analyzing genomes across multiple species.
Innovation Solution
A system and method that uses gapped gene clusters with a predetermined gap, genome clusters via a quorum parameter, and possible multiplicity to automatically discover all clusters occurring with a gap in at least K genomes, employing an output-sensitive algorithm to detect maximal clusters and apply to comparisons of gene orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional methods are used to identify permutation patterns in genome sequences, then exact matches can be detected, but the methods cannot handle gapped patterns or wild cards
Solution Approach 1:
The invention changes the parameter of pattern matching from exact matching to gapped matching by introducing a gap parameter g that allows up to g mismatches between consecutive elements. This transforms the rigid exact match requirement into a flexible gapped match that can handle wild cards and gaps in genome sequences while maintaining reliability through controlled parameter constraints.
2Measurement precision
If all permutation patterns are enumerated to ensure completeness, then the number of comparisons increases exponentially
Solution Approach 1:
The invention performs preliminary action by first identifying maximal gapped permutation patterns before searching for their occurrences. By pre-computing the maximal patterns and their properties, the algorithm avoids the need to enumerate all possible patterns, significantly reducing the computational complexity from exponential to polynomial time while maintaining complete detection capability.
Solution Approach 2:
The invention segments the pattern matching problem into two distinct phases: (1) identification of maximal gapped permutation patterns, and (2) detection of their occurrences in the genome sequence. This segmentation allows each phase to be optimized independently, improving overall algorithmic efficiency while ensuring comprehensive pattern detection.
3Adaptability or versatility
If gap parameters are used to handle noisy data, then flexibility increases, but the complexity of pattern identification increases
Solution Approach 1:
The invention introduces a gap parameter g as a controllable variable that quantifies the maximum number of allowed mismatches. This parameter transformation converts the complex problem of handling noisy and incomplete data into a manageable computational task with clear mathematical definitions, enabling flexible adaptation to different data quality levels while maintaining algorithmic tractability.
Data Source
AI summary
A system and method for identifying gapped permutation patterns, includes discovering all clusters in the input data sequence that occur with a predetermined gap.