Compressed Genomic Sequence Search Without Full Decompression
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Solution Overview
Problem
Current computational methods for analyzing large genomic datasets are inefficient due to the exponential growth of data, as they require decompression for analysis, which does not mitigate the computational bottleneck, and existing techniques are unsatisfactory for inexact searches, especially when dealing with highly similar genomes.
Innovation Solution
The approach involves compressing genomic data to preserve redundancy, allowing direct computation on the compressed data using 'compressive' algorithms, which reduces the computational load by storing genomes in a format that respects similarities and differences, enabling analysis proportional to the compressed size, rather than the full dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression techniques are used to reduce storage space, then storage efficiency is improved, but computational analysis requires full decompression which negates the efficiency gain
Solution Approach 1:
The patent introduces an intermediary compressed data structure that preserves essential genomic information while enabling direct computational operations. Instead of fully decompressing data for analysis, the compressed representation serves as a mediator that allows search and comparison operations to be performed directly on compressed sequences, thereby maintaining both storage efficiency and computational productivity
Solution Approach 2:
The patent segments genomic sequences into reusable components and patterns that can be stored in compressed form. By dividing sequences into fundamental units and storing only unique patterns with references to repeated elements, the system enables computational operations on segmented compressed data rather than requiring full decompression of entire genomes
2Reliability
If computational analysis is performed on full genomic libraries, then analysis completeness is maintained, but processing time grows exponentially with data size
Solution Approach 1:
The patent creates compressed copies of genomic sequences that retain essential information for analysis. By storing sequences in a compressed format that preserves similarity relationships and enables direct comparison operations, the system maintains analysis completeness while reducing processing time from exponential to linear or sub-linear scaling with database size
3Quantity of substance
If redundancy in genomic data is exploited for compression, then storage requirements are reduced, but existing methods require full decompression before analysis
Solution Approach 1:
The patent implements dynamic compressed data structures that adapt to different analysis operations. The compressed representation allows for selective expansion or decomposition only of specific regions or patterns needed for particular queries, rather than requiring static full decompression of entire datasets. This dynamic approach reduces both storage requirements and workflow complexity by performing computations directly on compressed data when possible
Data Source
AI summary
The redundancy in genomic sequence data is exploited by compressing sequence data in such a way as to allow direct computation on the compressed data using methods that are referred to herein as “compressive” algorithms. This approach reduces the task of computing on many similar genomes to only slightly more than that of operating on just one. In this approach, the redundancy among genomes is translated into computational acceleration by storing genomes in a compressed format that respects the structure of similarities and differences important to analysis. Specifically, these differences are the nucleotide substitutions, insertions, deletions, and rearrangements introduced by evolution. Once such a compressed library has been created, analysis is performed on it in time proportional to its compressed size, rather than having to reconstruct the full data set every time one wishes to query it.